Projects and Theses
Climbing research: from instrumented holds to markerless kinematic analysis
We currently want to (i) elaborate the added value of a campus board that records the forces per limb, (ii) determine grasping phases for kinematic analyses of the phalanges more pragmatically than with 6DoF sensors, and (iii) drive forward a competition analysis based purely on video material.
Keywords
climbing, performance analysis, kinetics, kinematics, marker-based and marker less tracking, pose estimation
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Semester Project , Internship
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Published since: 2024-05-11 , Earliest start: 2024-08-11 , Latest end: 2025-07-03
Applications limited to ETH Zurich
Organization Sensory-Motor Systems Lab
Hosts Wolf Peter
Topics Medical and Health Sciences , Engineering and Technology
Human Pose Estimation with Global Trajectories
Estimating human poses within global trajectories is critical for applications such as augmented reality and sports analytics, yet it often demands precisely calibrated cameras and significant computational efforts. With advancements in deep learning and pose estimation technologies, various models can be trained using 2D or 3D motion data. However, effectively integrating these models to predict and analyze human movement trajectories in a continuous and dynamic environment remains challenging. This project aims to create a robust system that estimates and predicts human poses accurately, facilitating advancements in dynamic pose analysis and real-world applications.
Keywords
Human Pose Estimation, Human Motion Model, Global Trajectory Estimation
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Semester Project , Lab Practice , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-05-10 , Earliest start: 2024-05-01 , Latest end: 2024-12-31
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Organization Advanced Interactive Technologies
Hosts Jiang Tianjian , Song Jie
Topics Information, Computing and Communication Sciences
Collision-free object reaching & grasping with a legged mobile manipulator
Reaching and grasping an object of interest is a relatively simple task that can be achieved robustly in case the object is equipped with a simple handle and a visual marker. However, often the difficulty in the task originates from the rest of the environment. The object may be placed in cluttered spaces with diverse obstacles as well as dynamic entities, e.g. humans, other robots. As a result, executing the task of reaching and grasping the object necessitates collision-free motion control capabilities.
Keywords
Mobile Manipulation, Loco-Manipulation, Reinforcement Learning, Robot Control, Legged Robotics
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Semester Project , Master Thesis
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Published since: 2024-05-10
Organization Robotic Systems Lab
Hosts Klemm Victor
Topics Information, Computing and Communication Sciences , Engineering and Technology
The AI Sleep Doctor: AI-based evaluation of medical sleep examinations
The process of evaluating sleep examinations and diagnosing sleep disorders through polysomnographies (PSGs) is labor-intensive as it requires manual analysis from sleep technicians and doctors. In collaboration with Clinic Barmelweid, a leading sleep and rehabilitation clinic in northwestern Switzerland, we plan to automate this process using machine learning models. Clinic Barmelweid conducts approximately 400-450 PSGs annually and has access to a dataset of more than 5,000 recordings.
Keywords
Machine Learning, Polysomnographies, Sleep disorders, AI-based evaluation, Clinical database, Interdisciplinary collaboration
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Semester Project , Collaboration , Internship , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-05-09 , Earliest start: 2024-06-03
Organization Sensory-Motor Systems Lab
Hosts Breuss Alexander , Padilla Neira Sara
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Multisensory assessment of physiological markers during neural stimulation for stroke rehabilitation
Project goal is to assess outcomes of a non-invasive brain stimulator for future application in stroke rehabilitation. This will involve using an exciting novel method of brain stimulation together with simultaneous multisensory recordings of various physiological parameters, including heart rate, galvanic skin response, pupillometry and electroencephalogram (EEG). The results of the project will help develop brain stimulation protocols that elicit meaningful neural responses in healthy subjects, and in stroke patients.
Keywords
neural stimulation, neural biomarkers, neurophysiology, physiology, neuroscience, EEG, pupillometry
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Semester Project , Master Thesis
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Published since: 2024-05-08 , Earliest start: 2023-06-01
Organization Rehabilitation Engineering Lab
Hosts Viskaitis Paulius , Donegan Dane
Topics Engineering and Technology
Real-time control of neural stimulation for stroke patients.
Real-time analysis of movement kinematics can benefit multiple different strategies in rehabilitation after stroke, including allowing closed-loop brain stimulation. Use of inertial measurement units (IMUs) allows detection of movement and extraction of kinematic features, but application in real-time remains challenging. This project will develop algorithms for real-time movement data analysis and feature extraction in typical rehabilitation tasks and general real-life movements. In turn, these algorithms will be applied to control novel brain stimulation approaches in stroke neurorehabilitation.
Keywords
Inertial measurement unit, IMU, movement tracking, machine learning, real-time, signal processing
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Semester Project , Master Thesis
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Published since: 2024-05-08 , Earliest start: 2023-05-09
Organization Rehabilitation Engineering Lab
Hosts Viskaitis Paulius , Donegan Dane
Topics Engineering and Technology
Efficient data processing and reporting in stroke neuro-rehabilitation
Project goal is to optimise existing and develop new algorithms into an efficient system for signal pre-processing, data storage, analysis and visualization in motor-neurorehabilitation. This data is generated by stroke patients wearing motion sensors during their therapy sessions. Key endpoint of the project is to display real-time and longitudinal therapy results, which can aid therapists and patients. The results of the project will help develop a more efficient therapy and is a key part of a larger project that seeks to develop an intelligent and closed-loop neural stimulation system for stroke rehabilitation.
Keywords
health biomarkers, data science, computer science, data visualization, data processing, real-time, internet of medical things, IoMT, healthcare internet of things, healthcare IoT
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Published since: 2024-05-08 , Earliest start: 2023-05-09
Organization Rehabilitation Engineering Lab
Hosts Donegan Dane , Viskaitis Paulius
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Master Student Project for SmartVNS App Development
As part of your master student project, you will play a crucial role in the development of an app designed to provide stroke patients with valuable insights into their therapy data and progress over time. The app's primary goal is to encourage treatment adherence, addressing a significant clinical challenge in stroke rehabilitation. By visualizing therapy data in an easy-to-understand format, the app aims to empower patients, enhance their motivation, and guide them towards targeted areas of improvement.
Keywords
health biomarkers, data science, computer science, data visualization, data processing, real-time, internet of medical things, IoMT, healthcare internet of things, healthcare IoT
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Master Thesis
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Published since: 2024-05-08 , Earliest start: 2023-06-01
Organization Rehabilitation Engineering Lab
Hosts Donegan Dane
Topics Information, Computing and Communication Sciences
Semester Project_Design customized molds for manufacturing a novel joint implant
Background: The Laboratory of Orthopedic Technology has recently developed a novel joint implant and is undergoing optimization of the manufacturing process. We are looking for a master's student who is passionate about medical devices and mechanical design to join us for a semester project. Objectives: • Design different molds for material casting using SolidWorks or Fusion 360. • Optimize implant using matlab or Python. • Utilize 3D printing or laser cutting to create the molds. • Conduct mechanical tests on the implants. Your Profile: • Strong knowledge in mechanical design and drawing skills. • Hands-on and detail-oriented. • Experience with SolidWorks or Fusion 360, as well as Python or Matlab. Timeframe: Starting ASAP until the end of September.
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Semester Project , ETH Zurich (ETHZ)
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Published since: 2024-05-08 , Earliest start: 2024-05-13 , Latest end: 2024-09-30
Organization Bone Pathologies and Treatment
Hosts Du Xiaoyu
Topics Engineering and Technology
Investigating the Transient Effects of Alcohol Intake on Movement Planning Abilities using Deep Learning
This thesis aims to utilize deep learning techniques to analyze eye-tracking data during a goal-directed upper limb task, particularly focusing on participants under the influence of alcohol. The objective is to develop digital health metrics that can elucidate differences in movement planning.
Keywords
Deep Learning, Eye Tracking, Alcohol, Algorithm, Computer Vision, Technology-assisted Assessment, Upper Limb, Movement Planning, Study, Data Analysis
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2024-05-06 , Earliest start: 2024-06-01
Organization Rehabilitation Engineering Lab
Hosts Domnik Nadine
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Feasibility evaluation of an upper-limb assistive exosuit to increase the reachable arm workspace and function of persons with upper limb impairments in activities of daily living.
In this project you will support the clinical evaluation of an upper-limb exosuit (Myoshirt, see: https://sms.hest.ethz.ch/research/current-research-projects/wearable-robots-for-assistance-and-rehabilitation/The%20Myoshirt.html) in various patient populations.
Keywords
rehabilitation robotics, assistive robotics, therapy, assistance, exosuits, soft wearable robotics, testing, evaluation
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-05-06 , Earliest start: 2024-05-06 , Latest end: 2025-02-28
Organization Sensory-Motor Systems Lab
Hosts Esser Adrian
Topics Engineering and Technology
Master Thesis internship with WayBetter Inc. in collaboration with ETH Zurich (6-months duration)
This six-month internship at WayBetter Inc., in collaboration with ETH Zurich, involves a cutting-edge machine learning project to develop an AI model that detects weight changes through facial images using a unique dataset of 6 million labeled full-body images. This model aims to facilitate significant applications in telehealth and clinical monitoring. Candidates will have the option to integrate this project into their Master's thesis at ETH Zurich, benefitting from expert guidance while contributing to transformative health monitoring solutions. Ideal candidates should have a solid foundation in machine learning, image processing, and data management.
Keywords
Machine Learning, Computer Vision, Digital Health
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-05-06 , Earliest start: 2024-05-12 , Latest end: 2024-10-31
Organization Health-IS Lab
Hosts Jakob Robert
Topics Medical and Health Sciences , Information, Computing and Communication Sciences
Foot placement control in people with Parkinson's disease
Parkinson’s disease is one of the most common neurodegenerative movement disorders affecting over 10 million people worldwide. Symptoms like impaired gait and postural instability can cause falls and highly impair patients’ mobility. The consequences of falls include fractures, hospital admissions, loss of independence, fear of falls, social isolation and early mortality. Falls are cited as one of the worst aspects of PD and unfortunately few efficacious interventions are available.
Keywords
Fall risk, Biomechanics, Parkinson's disease, Gait analysis
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Semester Project , Master Thesis
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Published since: 2024-05-05 , Earliest start: 2023-09-01
Organization Neuromuscular Biomechanics
Hosts Lang Charlotte
Topics Medical and Health Sciences , Engineering and Technology
Online Safe Locomotion Learning in the Wild
Reinforcement learning (RL) can potentially solve complex problems in a purely data-driven manner. Still, the state-of-the-art in applying RL in robotics, relies heavily on high-fidelity simulators. While learning in simulation allows to circumvent sample complexity challenges that are common in model-free RL, even slight distribution shift ("sim-to-real gap") between simulation and the real system can cause these algorithms to easily fail. Recent advances in model-based reinforcement learning have led to superior sample efficiency, enabling online learning without a simulator. Nonetheless, learning online cannot cause any damage and should adhere to safety requirements (for obvious reasons). The proposed project aims to demonstrate how existing safe model-based RL methods can be used to solve the foregoing challenges.
Keywords
safe mode-base RL, online learning, legged robotics
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Master Thesis
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Published since: 2024-05-03
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Li Chenhao , Li Chenhao , Li Chenhao
Topics Engineering and Technology
Evaluation of Finger Individuation Ability in Flexion and Extension
After a neurological injury (such as stroke), many patients suffer from impairment of the hand and finger function. Clinical assessments aim to measure and quantify those impairments for a better understanding and to specifically target those deficits in rehabilitation. One aspect of hand function, that is not truly understood yet is finger individuation: the ability to move one finger independently of the others. In a previously developed assessment device, we use force sensors attached to a hand module to measure this dexterous skill. This individuation device measures finger flexion (pushing) over different force levels, but the individuation ability in extension (pulling) remains unknown. The aim of this project is to implement an extension assessment (by adapting the existing protocol) and compare as well as test it before its implementation into the clinical routine.
Keywords
Rehabilitation engineering, neurology, finger individuation, hand, upper limb, assessment, patients, clinic
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2024-05-01 , Earliest start: 2024-05-20 , Latest end: 2024-12-20
Organization Rehabilitation Engineering Lab
Hosts Knill Anna
Topics Medical and Health Sciences , Engineering and Technology
Optimization of Individuation Assessment Software and Protocol
After a neurological injury (such as stroke), many patients suffer from impairment of the hand and finger function. Clinical assessments aim to measure and quantify those impairments for a better understanding and to specifically target those deficits in rehabilitation. One aspect of hand function, that is not truly understood yet is finger individuation: the ability to move one finger independently of the others. In a previously developed assessment device, we use force sensors attached to a hand module to measure this dexterous skill. This individuation device measures finger flexion (pushing) over different force levels, using a simple user interface. But to facilitate the measurement process and increase comprehension for cognitively impaired patients, we need to improve the assessment visualization and execution.
Keywords
Rehabiliation engineering, software development, finger individuation, user interface
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Published since: 2024-05-01 , Earliest start: 2024-05-20 , Latest end: 2024-12-20
Organization Rehabilitation Engineering Lab
Hosts Knill Anna
Topics Information, Computing and Communication Sciences , Engineering and Technology
Reliability and Validity testing of an Individuation device for a clinical use
After a neurological injury (such as stroke), many patients suffer from impairment of the hand and finger function. Clinical assessments aim to measure and quantify those impairments for a better understanding and to specifically target those deficits in rehabilitation. One aspect of hand function, that is not truly understood yet is finger individuation: the ability to move one finger independently of the others. In a previously developed assessment device, we use force sensors attached to a hand module to measure this dexterous skill. This individuation device will be used in a clinical setting to measure neurological patients. But before it can routinely be put into practice, its reliability (in a test-retest setting) and validity must be proven.
Keywords
Rehabilitation engineering, reliability, validity, neurology, finger individuation, clinic, patients, assessment testing
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2024-05-01 , Earliest start: 2024-06-24 , Latest end: 2025-01-31
Organization Rehabilitation Engineering Lab
Hosts Knill Anna
Topics Medical and Health Sciences , Engineering and Technology
Arm Activity Tracker for Stroke Patients - A Smart Phone Application
The project aims to design a phone application tailored for stroke patients, utilizing smartwatches to monitor and encourage arm activity. Preferences of stroke survivors are integral to the design process to ensure the application's usability and effectiveness in promoting recovery and functional improvement.
Keywords
Stroke, app design, smartphone, activity monitor
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Semester Project , Master Thesis
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Published since: 2024-04-30 , Earliest start: 2024-06-01 , Latest end: 2025-03-31
Organization Rehabilitation Engineering Lab
Hosts Mayrhuber Laura
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Learning Robust Perceptive Locomotion for Humanoid Robots Over Challenging Terrains
Quadrupedal robots have demonstrated a great potential to play a significant role in various applications [1, 2], including operating autonomously in remote and hazardous environments, industrial surveillance, etc. Their kinematic structure, similar to that of quadrupedal animals, makes them highly adaptable to both natural landscapes and human-made environments. Over the past decade, significant progress has been made in developing robust quadrupedal locomotion. However, achieving successful bipedal locomotion remains a greater challenge. Humanoids exhibit more intricate dynamic and kinematic properties, including a smaller base of support, more DoFs, potential self collisions from arm swing, etc. In this project, based on the gaited network structure proposed in [2] for quadrupedal robots, we aim at adapting this approach to bipedal locomotion in simulation and on the real machine.
Keywords
Reinforcement Learning, Humanoid, Locomotion
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-04-30
Organization Robotic Systems Lab
Hosts He Junzhe
Topics Information, Computing and Communication Sciences , Engineering and Technology
Task Planning with Pre-trained Foundation Models for Legged Mobile Manipulators
Imagine this scenario: you return home after a tiring day at work. Instead of reaching for a drink yourself, you simply instruct your home robot, perhaps with a casual "Fetch me a cold drink" or even a specific request like "Could you mix me a gin and tonic?". The robot then undertakes a series of actions, such as navigating to the fridge, opening it, identifying a cold beverage, and bringing it back to you. Such a seemingly intricate task might have once been confined to the realm of science fiction. However, thanks to the rise of powerful foundation models, this futuristic vision is gradually turning into reality. Currently, experts in robotics painstakingly design numerous behaviors to accomplish a limited range of tasks. Furthermore, operators require extensive training to navigate these intricate systems. With the rise of foundation models capable of perception and reasoning, robots can now understand human instructions across languages, comprehend complex environments, and plan extended sequences of actions, such as preparing a cup of coffee. We find ourselves at a crucial juncture where general-purpose robots are finally within reach. In this project, we seek to advance the field of mobile manipulation using these powerful pretrained foundation models. In particular, we’ll exploit knowledge embedded in VLMs and LLMs to allow the robot to better interpret a task given by a human and develop an understanding of how to achieve and verify the completion of this task.
Keywords
Zero-shot task planning, mobile manipulation
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Semester Project , Master Thesis
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Published since: 2024-04-29 , Earliest start: 2024-05-01
Organization Robotic Systems Lab
Hosts Qu Kaixian , Zurbrügg René , Cramariuc Andrei
Topics Engineering and Technology
Redesign and Test of an E-Touring Ski
Ski touring provides a unique and immersive outdoor experience, but the ascent can impose a considerable amount of strain on the body, especially for novices, elderly, or people with disabilities. The objective of this master thesis is to redesign an existing concept and functional model of an electric ski touring device that supports hill ascents, aiming to enhance the ski touring experience for individuals with lower fitness levels by making it less physically demanding and more enjoyable. The current model must be optimized with respect to weight, function, energy consumption, and usability (donning/doffing). After successful fabrication and testing, first steps shall be performed to identify intellectual property and market needs, and finally plan the commercialization of the e-touring ski.
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Master Thesis
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Published since: 2024-04-29 , Earliest start: 2024-05-06 , Latest end: 2025-04-30
Applications limited to Department of Mechanical and Process Engineering
Organization Sensory-Motor Systems Lab
Hosts Wolf Peter
Topics Engineering and Technology
Mid-Range Path Planning Integrating Vision with LLM and Depth Sensing
This project aims to advance the field of robotic navigation by focusing on mid-range path planning, a crucial layer that connects the overarching routes designed by global planning and the immediate, reactive maneuvers of local planning. The project will develop a neural network model capable of generating a sequence of waypoints toward a specified 3D goal position by leveraging current RGB images, GPT cost reasoning from large language model (LLM) , and estimated depth images. This integration will facilitate more efficient navigation through complex environments by smoothing transitions between planning layers and optimizing route adjustments in real-time.
Keywords
Robot Planning; Vision Learning; LLM (Vision GPT) Reason,
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2024-04-26 , Earliest start: 2024-05-01 , Latest end: 2025-01-01
Organization Robotic Systems Lab
Hosts Yang Fan
Topics Information, Computing and Communication Sciences
Master Thesis / Internship / Semester Project: Digitization of large 12-lead ECG Image database
12-lead electrocardiograms (ECGs) are still solely documented on paper in many hospitals, especially in the Global South. These physical paper records provide a multitude of conditions recorded in many different countries. Our lab has access to a dataset with more than 8000 patient’s ECG photos / scans of 12-lead signals printed onto physical paper sheets. The dataset comprises 12-lead ECG image records from more than 35 hospital sites across Europe. The primary objective of this project is to develop an automated digitization pipeline from raw image scan in .png format towards 12 vectorized ECG time series in WFDB format.
Keywords
Spinal Cord Injury, Computer Vision, CV, Machine Learning, Deep Learning, AI, Signal Processing, ECG, Medical Data, Healthcare
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2024-04-22 , Earliest start: 2024-05-01 , Latest end: 2024-11-01
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Paez Diego, Dr.
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Enhancing EEG Analysis with AI: Developing a Tailored Foundational Model for EEG Signal Classification
This project aims to revolutionize the analysis of electroencephalography (EEG) data by developing a specialized foundational model utilizing the principles of artificial intelligence. Despite the critical role of EEG in diagnosing and treating neurological disorders, challenges such as low signal-to-noise ratios and complex signal patterns hinder practical analysis. By adapting strategies from successful domains like natural language processing and computer vision, this project will build a machine learning model tailored for EEG signals. The model will undergo extensive pre-training on diverse EEG datasets to establish a robust understanding of neural activities, followed by fine-tuning for specific clinical tasks such as seizure detection and sleep stage classification. Our approach promises to enhance the accuracy, efficiency, and accessibility of EEG diagnostics, paving the way for improved patient outcomes. Validation and testing using standard performance metrics will measure the model's efficacy, setting a new standard in EEG analysis.
Keywords
EEG Analysis, Foundational Models, Large Language Models, Machine Learning, Deep Learning, Transfer Learning, Signal Processing
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Semester Project , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-04-22 , Earliest start: 2024-04-22 , Latest end: 2024-12-22
Organization Digital Circuits and Systems (Benini)
Hosts Ingolfsson Thorir
Topics Information, Computing and Communication Sciences , Engineering and Technology
Master Thesis / Internship: Automated Time Series Analysis in Urinary Tract Assessment in Spinal Cord Injury
The primary objective of this project is to develop an automated pipeline for the identification and recognition of patterns within urodynamic recordings, utilizing urodynamic recording data in conjunction with annotated patterns provided by experts. This endeavor seeks to reduce the susceptibility of interpreting urodynamic recordings to potential errors arising from human judgment and inaccuracies, thereby improving the management of urinary tract complications in patients with spinal cord injury. By implementing a systematic approach to pattern recognition in Bladder Valomue/Pressure Time Series Measurements of urodynamic data, the potential for error in decision-making can be significantly reduced.
Keywords
Spinal Cord Injury, Machine Learning, Deep Learning, Pattern Recognition, Feature Engineering, Time Series Analysis, Signal Processing
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Semester Project , Internship , Master Thesis
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Published since: 2024-04-21 , Earliest start: 2024-05-19 , Latest end: 2024-12-31
Applications limited to Agroscope , Berner Fachhochschule , CERN , Corporates Switzerland , CSEM - Centre Suisse d'Electronique et Microtechnique , Department of Quantitative Biomedicine , Eawag , Empa , EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich , Fernfachhochschule , Forschungsinstitut für biologischen Landbau (FiBL) , Friedrich Miescher Institute , Hochschulmedizin Zürich , IBM Research Zurich Lab , Institute for Research in Biomedicine , Lucerne University of Applied Sciences and Arts , NCCR Democracy , NGOs Switzerland , Pädagogische Hochschule St.Gallen , Paul Scherrer Institute , Physikalisch-Meteorologisches Observatorium Davos , Sirm Institute for Regenerative Medicine , Swiss Federal Institute for Forest, Snow and Landscape Research , Swiss Institute of Bioinformatics , Swiss National Science Foundation , SystemsX.ch , Università della Svizzera italiana , Université de Neuchâtel , University of Basel , University of Berne , University of Fribourg , University of Geneva , University of Lausanne , University of Lucerne , University of St. Gallen , University of Zurich , Wyss Translational Center Zurich , Zurich University of Applied Sciences , Zurich University of the Arts , University of Konstanz , Technische Universität München , TU Berlin , Eberhard Karls Universität Tübingen , European Molecular Biology Laboratory (EMBL) , FH Aachen , Humboldt-Universität zu Berlin , Justus Liebig University, Gießen , Ludwig Maximilians Universiy Munich , Martin Luther Universitat, Halle , Max Delbruck Center for Molecular Medicine (MDC) , Max Planck Society , Otto Von Guericke Universitat, Magdeburg , RWTH Aachen University , Social Science Research Center Berlin , Technische Universität Hamburg , TU Darmstadt , TU Dresden , Universität der Bundeswehr München , Universität Ulm , Universität zu Lübeck , University of Cologne , University of Erlangen-Nuremberg , University of Hamburg , Universtity of Bayreuth , Delft University of Technology , Maastricht Science Programme , Radboud University Nijmegen , Utrecht University , Max Planck ETH Center for Learning Systems , European Molecular Biology Laboratory , IEE S.A. Luxembourg , Istituto Italiano di Tecnologia , Technical University of Denmark , Technion - Israel Institute of Technology , University of Southern Denmark , Imperial College London , UCL - University College London , University of Oxford , University of Cambridge , National Institute for Medical Research
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Paez Diego, Dr. , Paez Diego, Dr.
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Interpret Health with Wearable Measures
The widespread adoption of wearable technology enables continuous monitoring of physiological parameters like activity levels, heart rate, and sleep patterns. This study investigates the relationship between wearable measures and well-being, focusing on physical and mental health as well as overall quality of life.
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Semester Project , Internship , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-04-18
Organization Health-IS Lab
Hosts Wu Fan
Topics Information, Computing and Communication Sciences
Voice Pathology Detection Using Deep Learning
This study aims to detect voice pathologies distinguishing homophonic from dysphonic labels.
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Semester Project , Internship , Master Thesis
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Published since: 2024-04-18
Organization Health-IS Lab
Hosts Wu Fan
Topics Information, Computing and Communication Sciences
Cough and Health Status in Heart Failure Patients
This study aims to investigate the relationship between cough and health status among heart failure patients, recognizing cough as a potential indicator of underlying health status and symptom severity.
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Semester Project , Internship , Master Thesis
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Published since: 2024-04-18
Organization Health-IS Lab
Hosts Wu Fan
Topics Information, Computing and Communication Sciences
Co-Axial extrusion for biocementation
The project investigates the development of a co-axial extrusion methods for large-scale 3D printing bio-cementation structures. The extruded paste will host microorganisms such as S.Pasteurii, capable of precipitating calcite (MICP) to create bio-concrete structures. A robotic paste 3D printing platform will be used for the fabrication process; the bio-paste will be precipitated and calcified by the bacterial activity reinforcing the material.
Keywords
co-axial, 3d printing, biocementation, MICP, robotics, mechanical engineering
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2024-04-17 , Earliest start: 2024-05-01 , Latest end: 2024-12-31
Organization Digital Building Technologies
Hosts Antorveza Karen
Topics Engineering and Technology , Chemistry , Architecture, Urban Environment and Building
Development of a smart sock for plantar pressure monitoring
The goal of the project is to develop and test a smart sock prototype for plantar pressure measurements. The smart sock contains textile based pressure sensors and a readout module. This technology can be used for plantar pressure monitoring in diverse wearable applications ranging from healthcare to sports.
Keywords
wearables, smart textiles, plantar pressure, pressure sensors
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Master Thesis
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Published since: 2024-04-16 , Earliest start: 2024-03-01 , Latest end: 2025-02-28
Organization Biomedical and Mobile Health Technology Lab
Hosts Galli Valeria
Topics Medical and Health Sciences , Engineering and Technology
Image Based Robust Pose Estimation for General Excavator Buckets
The efficient operation of excavators in construction environments necessitates precise pose estimation of their buckets. Current methods rely on IMUs placed on the excavator arm which require tedious calibration and can be damaged during construction operations. This project aims to leverage computer vision and machine learning to enhance pose estimation, thereby enabling VR overlays for teleoperation and facilitating automation tasks.
Keywords
Computer Vision, Machine Learning, Synthetic Images, Excavators, Construction, 3D Pose Estimation
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Semester Project , Master Thesis
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Published since: 2024-04-16 , Earliest start: 2024-04-01
Organization Robotic Systems Lab
Hosts Schorp Vincent , Terenzi Lorenzo
Topics Information, Computing and Communication Sciences
Object Slippage Detection for a Miniature Force-Sensitive Gripper
We are developing a teleoperated micro-assembly system. A core component is a force-sensitive micro-gripper. A first gripper prototype has been realized and evaluated. Your task will be to review and improve the current design and to implement automated object slippage detection.
Keywords
Micro-manipulation, robotic gripper, force sensing, slippage detection, teleoperation
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Master Thesis
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Published since: 2024-04-15 , Earliest start: 2023-02-01 , Latest end: 2023-12-31
Organization Bio-Inspired RObots for MEDicine-Laboratory (BIROMED-Lab)
Hosts Duverney Cédric , Rauter Georg, Prof. Dr.-Ing. , Rauter Georg, Prof. Dr.-Ing.
Topics Engineering and Technology
Student Research Assistance for App development in Biosensing and Healthcare Data (~12 months)
Join a team of scientists improving the long-term prognosis and treatment of Spinal Cord Injury (SCI) through mobile and wearable systems and personalized health monitoring. Joining the SCAI Lab part of the Sensory-Motor Systems Lab at ETH, you will have the unique opportunity of working at one of the largest and most prestigious health providers in Switzerland: Swiss Paraplegic Center (SPZ) in Nottwil (LU).
Keywords
App development, Machine Learning, Data bases, Data engineering, Systems Engineering, Data Modelling
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Internship , Lab Practice , Student Assistant / HiWi , ETH Zurich (ETHZ)
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Published since: 2024-04-11 , Earliest start: 2024-06-01 , Latest end: 2025-06-30
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , IBM Research Zurich Lab , Institute for Research in Biomedicine , Hochschulmedizin Zürich , Swiss Institute of Bioinformatics , University of Lucerne , University of Zurich , Zurich University of Applied Sciences , Zurich University of the Arts , Lucerne University of Applied Sciences and Arts , Berner Fachhochschule
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Paez Diego, Dr. , Paez Diego, Dr. , Paez Diego, Dr.
Topics Information, Computing and Communication Sciences
Computational design optimization of a motion preserving spinal implant
Following trauma or due to degeneration it can be necessary to replace one or more intervertebral discs with an implant, a so-called Total Disc Replacement (TDR). Such devices enable motion though surfaces articulating against each other. While this treatment is clinically successful, it is connected to considerable complication and reoperation rates. Therefore, we are optimizing the design of such an implant to address these issues. While many different designs and design types have been proposed and are used in clinical practice, there is no consensus on what design or design type is the most beneficial. However, it is hypothesized, that replicating the situation that is present in healthy (asymptomatic) subjects as closely as possible, is optimal. Since the motions of the cervical spine are coupled (coupling of rotation and translation as well as multiple rotations) the optimal design of the articulating surfaces is not obvious. Therefore, this master’s thesis project aims at designing the implants articulating surfaces using parametric design optimization in LS-OPT based on finite element simulations.
Keywords
Computational, FEM, finite element method, simulation, mechanics, biomechanics, design, optimization
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Master Thesis
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Published since: 2024-04-10 , Earliest start: 2024-04-15 , Latest end: 2024-10-15
Organization Bone Pathologies and Treatment
Hosts Kölle Lucia
Topics Engineering and Technology
Push Notification Integration for Enhanced Adherence to At-Home Rehabilitation Therapy in Stroke and Traumatic Brain Injury Patients
Adherence to rehabilitation therapy is crucial for the recovery of hand functionality in stroke and traumatic brain injury (TBI) patients. However, sustaining patient motivation to train at home remains a challenge. This project aims to explore the impact of push notifications on adherence to physical therapy among stroke and TBI patients. By investigating the optimal frequency and content of notifications, the goal is to develop a notification/reminder system that fosters continuous engagement with the rehabilitation plan, ultimately promoting increased therapy and better functional outcomes for patients.
Keywords
App Development, Stroke, Traumatic Brain Injury, Rehabilitation, Adherence to Therapy, Push Notifications, mHealth Apps, Interdisciplinary Research, React Native
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Internship , Master Thesis , Student Assistant / HiWi , Summer School
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Published since: 2024-04-08 , Earliest start: 2024-04-21 , Latest end: 2025-03-01
Organization Rehabilitation Engineering Lab
Hosts Retevoi Alexandra
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Internship/ Master Thesis: Machine Learning for Assessment of Walking Patterns in the SCI population - Time Series Classification
Gait patterns in multiple impairments present unique and complex patterns, which hinders the proper quantitative assessment of the walking ability for chronic ambulatory conditions when translated to daily living. In this project, we will focus on finding clusters of gait patterns through unsupervised learning from a large dataset of incomplete spinal cord injury individuals. The goal is to investigate hidden patterns in relation to the type of injuries and find their application for future diagnosis and rehabilitation treatment. Your work will guide future rehabilitation methods in general clinical practice, through applied classification and dimensionality reduction in Biomechanics of walking. Goal: Develop an unsupervised clustering pipeline for a large dataset of gait patterns from spinal cord injured individuals for class similarity evaluation
Keywords
Medical and health science, computing and computational science, engineering and technology, information, machine learning, data science, data engineering
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Internship , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-04-03 , Earliest start: 2024-06-01 , Latest end: 2025-03-31
Applications limited to EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich , CERN , Corporates Switzerland , IBM Research Zurich Lab , NGOs Switzerland , Zurich University of Applied Sciences , Wyss Translational Center Zurich , University of Zurich , University of St. Gallen , University of Lucerne , University of Lausanne , University of Geneva , University of Fribourg , University of Berne , University of Basel , Université de Neuchâtel , Università della Svizzera italiana , Swiss National Science Foundation , Swiss Institute of Bioinformatics , Empa , Eawag , TU Berlin , Technische Universität München , Technische Universität Hamburg , RWTH Aachen University , Max Delbruck Center for Molecular Medicine (MDC) , Delft University of Technology , UCL - University College London , University of Cambridge , University of Oxford , University of Leeds , University of Manchester , University of Nottingham , National Institute for Medical Research , Imperial College London , Radboud University Nijmegen , Maastricht Science Programme
Organization Sensory-Motor Systems Lab
Hosts Paez Diego, Dr. , Paez Diego, Dr.
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Learning Pose Estimation for Partially Occluded Objects from Simulation
This project addresses the task of 6D pose estimation for general-purpose objects, particularly when dealing with occlusion. We aim to leverage recent deep learning methods and synthetic data generation schemes to enable robust object manipulation.
Keywords
Object Pose Estimation, Perceptive Manipulation, Photorealistic Simulation
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Semester Project , Master Thesis
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Published since: 2024-04-03
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Robotic Systems Lab
Hosts Zurbrügg René , Bhardwaj Arjun , Patil Vaishakh
Topics Information, Computing and Communication Sciences
Master Thesis/ Internship: Quantifying Biomechanics of Gait from IMU Data Simulation
Gait analysis is crucial for evaluating walking ability in individuals with ambulatory conditions e.g., Stroke or Parkinson’s disease. Traditional marker-based motion capture systems face limitations in real-life scenarios. This project proposes using wearable IMUs for gait analysis due to their portability. The goal is use datasets already recorded in our labs to model and synchronize IMU data and with 3D motion capture recordings, extract meaningful gait features for different pathologies. The extracted gait features will be validated against, and validate them against a motion capture-based ground truth features calculated for the same patients. This research aims to enhance gait analysis outside of labs and provide valuable insights for decision-making in gait disorders.
Keywords
Gait Analysis, Inertial Measurement Unit, Wearable Sensors, Signal Processing, Pattern Recognition, Machine Learning, Time Series Analysis
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Internship , Master Thesis
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Published since: 2024-04-03 , Earliest start: 2024-06-01 , Latest end: 2025-03-31
Applications limited to Agroscope , Berner Fachhochschule , CERN , Corporates Switzerland , CSEM - Centre Suisse d'Electronique et Microtechnique , Department of Quantitative Biomedicine , Eawag , Empa , EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich , Fernfachhochschule , Forschungsinstitut für biologischen Landbau (FiBL) , Friedrich Miescher Institute , Hochschulmedizin Zürich , IBM Research Zurich Lab , Institute for Research in Biomedicine , Lucerne University of Applied Sciences and Arts , NCCR Democracy , NGOs Switzerland , Pädagogische Hochschule St.Gallen , Paul Scherrer Institute , Physikalisch-Meteorologisches Observatorium Davos , Sirm Institute for Regenerative Medicine , Swiss Federal Institute for Forest, Snow and Landscape Research , Swiss Institute of Bioinformatics , Swiss National Science Foundation , SystemsX.ch , Università della Svizzera italiana , Université de Neuchâtel , University of Basel , University of Berne , University of Fribourg , University of Geneva , University of Lausanne , University of Lucerne , University of St. Gallen , University of Zurich , Wyss Translational Center Zurich , Zurich University of Applied Sciences , Zurich University of the Arts , Eberhard Karls Universität Tübingen , European Molecular Biology Laboratory (EMBL) , FH Aachen , Humboldt-Universität zu Berlin , Justus Liebig University, Gießen , Ludwig Maximilians Universiy Munich , Martin Luther Universitat, Halle , Max Delbruck Center for Molecular Medicine (MDC) , Max Planck Society , Otto Von Guericke Universitat, Magdeburg , RWTH Aachen University , Social Science Research Center Berlin , Technische Universität Hamburg , Technische Universität München , TU Berlin , TU Darmstadt , TU Dresden , Universität der Bundeswehr München , Universität Ulm , Universität zu Lübeck , University of Cologne , University of Erlangen-Nuremberg , University of Hamburg , University of Konstanz , Universtity of Bayreuth , Delft University of Technology , Maastricht Science Programme , Radboud University Nijmegen , Utrecht University , Chalmers University of Technology , Champalimaud Foundation , CNRS - Centre national de la recherche scientifique , European Molecular Biology Laboratory , Grenoble Institute of Technology (G-INP) - Phelma , IDEA League , IEE S.A. Luxembourg , Max Planck ETH Center for Learning Systems , Politecnico di Milano , Research Internships at HU Berlin , Technical University of Denmark , Technion - Israel Institute of Technology , The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI) , Université de Strasbourg , Universiteit Stellenbosch , University College Dublin , University of Southern Denmark , Vienna Biocenter - Scientific Training , Uppsala Universitet
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Paez Diego, Dr.
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Physics-constrained 3D reconstruction of fluid flows
The goal of this project is the grid-less 3D reconstruction of the motion of fluids from experimental images using physical constraints. We want to use novel 3D reconstruction techniques and physical constraints in the form of the governing PDEs, to resolve ambiguities and obtain an accurate flow field from a sparse set of views
Keywords
computer vision, fluid dynamics, physics-informed networks, 3D reconstruction, 3D vision
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Semester Project , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-04-03 , Earliest start: 2024-04-01 , Latest end: 2024-12-31
Applications limited to ETH Zurich
Organization Advanced Interactive Technologies
Hosts Tsalicoglou Christina
Topics Information, Computing and Communication Sciences , Engineering and Technology
Master thesis: Hand-object 3D reconstruction from Internet videos (Computer Vision)
This project is designed for a master student looking for a CVPR submission via a thesis project.
Keywords
Computer vision, VR/AR, 3D reconstruction, 3D pose estimation, machine learning, neural networks, human-object interactions
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Semester Project , Master Thesis
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Published since: 2024-04-02 , Earliest start: 2024-04-22 , Latest end: 2024-12-01
Applications limited to ETH Zurich , Department of Computer Science , Eberhard Karls Universität Tübingen
Organization Advanced Interactive Technologies
Hosts Fan Zicong
Topics Information, Computing and Communication Sciences
Interpretation of instrumented movement analysis in neurorehabilitation
With advancing technology, healthcare professionals now have greater access to quantifying human movement, which will increasingly influence health assessments. However, interpreting movement data, particularly for individuals with neurological impairments, remains challenging. Our project aims to explore experts' insights on interpreting such data. Through multi-center focus groups, we gather healthcare professionals' perspectives to enhance informed decision-making in clinical settings.
Keywords
Neurorehabilitation, Focus Groups, Instrumented Movement Analysis
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Semester Project , Internship
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Published since: 2024-04-02 , Earliest start: 2024-04-14 , Latest end: 2024-10-31
Organization Rehabilitation Engineering Lab
Hosts Mayrhuber Laura
Topics Medical and Health Sciences , Engineering and Technology , Behavioural and Cognitive Sciences
Investigating the kinematics of the scapular motion using non-invasive optical technologies
Accurate non-invasive assessment modalities that incorporate both scapular motion and its morphology are currently unavailable, presenting a clear need for sustainable clinical application. To address this need, the Laboratory for Movement Biomechanics (LMB) utilizes a unique optical 4D scanning system (SLOT) to estimate the underlying anatomical structures using non-invasive structured light to produce high-quality images of the human skin surface, both statically and dynamically. By utilizing the clear cutaneous surface contours surrounding the scapula, the application of this technology to the shoulder joint could allow a novel non-invasive and dynamic approach for estimating scapular kinematics that overcomes the challenges associated with soft-tissue artifacts. The key challenge in the development of this approach is the precise identification and tracking of relevant scapula landmarks, as well as soft tissue artifacts, all of which are expected to affect the accuracy of the SLOT-measured kinematics.
Keywords
Scapulae, kinematics, dynamic scapulae motion, 3D scanning, back shape, machine learning, VICON, OpenSIM.
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Master Thesis
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Published since: 2024-03-28 , Earliest start: 2024-04-01 , Latest end: 2024-12-01
Applications limited to Department of Computer Science , Department of Mathematics , Department of Information Technology and Electrical Engineering , Department of Mechanical and Process Engineering
Organization Functional Spinal Biomechanics
Hosts Cukovic Sasa
Topics Information, Computing and Communication Sciences , Engineering and Technology
Nutrition Tracker for Health Care
This master’s thesis is dedicated to developing an advanced nutrition tracking system for hospitals, integrating QR-code recognition and structured light camera technology. The focus is to significantly enhance the precision of food volume measurements and patient meal tracking with machine learning, thereby improving nutritional monitoring accuracy.
Keywords
Nutrition Tracking, Computer Vision, 3D vision, Machine Learning, Software Development, Startup
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Published since: 2024-03-27 , Earliest start: 2024-03-28 , Latest end: 2025-03-31
Organization Laboratory of Exercise and Health (De Bock group)
Hosts Iten Raban
Topics Medical and Health Sciences , Information, Computing and Communication Sciences
Assessing the feasibility of plantar pressure measurement devices for monitoring the diabetic population
The goal of the project is to assess the feasibility of using commercially available plantar pressure monitoring devices (so called smart insoles) on the diabetic population. Pressure ulcers are a common complication of the diabetic foot, and monitoring plantar pressure continuously is a potential measure of prevention. Diabetic patients are often prescribed personalized footwear (e.g., curved insoles that accommodate any deformity in the feet). This project aims at assessing the potential of the smart insoles available on the market to monitor plantar pressure in diabetic patients with such custom footwear.
Keywords
wearables, mobile health, prevention, plantar pressure monitoring, diabetic foot
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Semester Project , Bachelor Thesis
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Published since: 2024-03-26 , Earliest start: 2024-04-08 , Latest end: 2024-09-02
Organization Biomedical and Mobile Health Technology Lab
Hosts Galli Valeria
Topics Medical and Health Sciences , Engineering and Technology
Lifelike Agility on ANYmal by Learning from Animals
The remarkable agility of animals, characterized by their rapid, fluid movements and precise interaction with their environment, serves as an inspiration for advancements in legged robotics. Recent progress in the field has underscored the potential of learning-based methods for robot control. These methods streamline the development process by optimizing control mechanisms directly from sensory inputs to actuator outputs, often employing deep reinforcement learning (RL) algorithms. By training in simulated environments, these algorithms can develop locomotion skills that are subsequently transferred to physical robots. Although this approach has led to significant achievements in achieving robust locomotion, mimicking the wide range of agile capabilities observed in animals remains a significant challenge. Traditionally, manually crafted controllers have succeeded in replicating complex behaviors, but their development is labor-intensive and demands a high level of expertise in each specific skill. Reinforcement learning offers a promising alternative by potentially reducing the manual labor involved in controller development. However, crafting learning objectives that lead to the desired behaviors in robots also requires considerable expertise, specific to each skill.
Keywords
learning from demonstrations, imitation learning, reinforcement learning
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Master Thesis
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Published since: 2024-03-25
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Li Chenhao , Klemm Victor
Topics Information, Computing and Communication Sciences
Learning Real-time Human Motion Tracking on a Humanoid Robot
Humanoid robots, designed to mimic the structure and behavior of humans, have seen significant advancements in kinematics, dynamics, and control systems. Teleoperation of humanoid robots involves complex control strategies to manage bipedal locomotion, balance, and interaction with environments. Research in this area has focused on developing robots that can perform tasks in environments designed for humans, from simple object manipulation to navigating complex terrains. Reinforcement learning has emerged as a powerful method for enabling robots to learn from interactions with their environment, improving their performance over time without explicit programming for every possible scenario. In the context of humanoid robotics and teleoperation, RL can be used to optimize control policies, adapt to new tasks, and improve the efficiency and safety of human-robot interactions. Key challenges include the high dimensionality of the action space, the need for safe exploration, and the transfer of learned skills across different tasks and environments. Integrating human motion tracking with reinforcement learning on humanoid robots represents a cutting-edge area of research. This approach involves using human motion data as input to train RL models, enabling the robot to learn more natural and human-like movements. The goal is to develop systems that can not only replicate human actions in real-time but also adapt and improve their responses over time through learning. Challenges in this area include ensuring real-time performance, dealing with the variability of human motion, and maintaining stability and safety of the humanoid robot.
Keywords
real-time, humanoid, reinforcement learning, representation learning
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Master Thesis
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Published since: 2024-03-25
Organization ETH Competence Center - ETH AI Center
Hosts He Junzhe , Li Chenhao , Li Chenhao
Topics Information, Computing and Communication Sciences
Continuous Skill Learning with Fourier Latent Dynamics
In recent years, advancements in reinforcement learning have achieved remarkable success in teaching robots discrete motor skills. However, this process often involves intricate reward structuring and extensive hyperparameter adjustments for each new skill, making it a time-consuming and complex endeavor. This project proposes the development of a skill generator operating within a continuous latent space. This innovative approach contrasts with the discrete skill learning methods currently prevalent in the field. By leveraging a continuous latent space, the skill generator aims to produce a diverse range of skills without the need for individualized reward designs and hyperparameter configurations for each skill. This method not only simplifies the skill generation process but also promises to enhance the adaptability and efficiency of skill learning in robotics.
Keywords
representation learning, periodic autoencoders, learning from demonstrations, policy modulating trajectory generators
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Master Thesis
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Published since: 2024-03-25
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Rudin Nikita
Topics Information, Computing and Communication Sciences , Engineering and Technology
Universal Humanoid Motion Representations for Expressive Learning-based Control
Recent advances in physically simulated humanoids have broadened their application spectrum, including animation, gaming, augmented and virtual reality (AR/VR), and robotics, showcasing significant enhancements in both performance and practicality. With the advent of motion capture (MoCap) technology and reinforcement learning (RL) techniques, these simulated humanoids are capable of replicating extensive human motion datasets, executing complex animations, and following intricate motion patterns using minimal sensor input. Nevertheless, generating such detailed and naturalistic motions requires meticulous motion data curation and the development of new physics-based policies from the ground up—a process that is not only labor-intensive but also fraught with challenges related to reward system design, dataset curation, and the learning algorithm, which can result in unnatural motions. To circumvent these challenges, researchers have explored the use of latent spaces or skill embeddings derived from pre-trained motion controllers, facilitating their application in hierarchical RL frameworks. This method involves training a low-level policy to generate a representation space from tasks like motion imitation or adversarial learning, which a high-level policy can then navigate to produce latent codes that represent specific motor actions. This approach promotes the reuse of learned motor skills and efficient action space sampling. However, the effectiveness of this strategy is often limited by the scope of the latent space, which is traditionally based on specialized and relatively narrow motion datasets, thus limiting the range of achievable behaviors. An alternative strategy involves employing a low-level controller as a motion imitator, using full-body kinematic motions as high-level control signals. This method is particularly prevalent in motion tracking applications, where supervised learning techniques are applied to paired input data, such as video and kinematic data. For generative tasks without paired data, RL becomes necessary, although kinematic motion presents challenges as a sampling space due to its high dimensionality and the absence of physical constraints. This necessitates the use of kinematic motion latent spaces for generative tasks and highlights the limitations of using purely kinematic signals for tasks requiring interaction with the environment or other agents, where understanding of interaction dynamics is crucial. We would like to extend the idea of creating a low-level controller as a motion imitator to full-body motions from real-time expressive kinematic targets.
Keywords
representation learning, periodic autoencoders
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Master Thesis
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Published since: 2024-03-25
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Li Chenhao , Li Chenhao
Topics Information, Computing and Communication Sciences , Engineering and Technology
Humanoid Locomotion Learning and Finetuning from Human Feedback
In the burgeoning field of deep reinforcement learning (RL), agents autonomously develop complex behaviors through a process of trial and error. Yet, the application of RL across various domains faces notable hurdles, particularly in devising appropriate reward functions. Traditional approaches often resort to sparse rewards for simplicity, though these prove inadequate for training efficient agents. Consequently, real-world applications may necessitate elaborate setups, such as employing accelerometers for door interaction detection, thermal imaging for action recognition, or motion capture systems for precise object tracking. Despite these advanced solutions, crafting an ideal reward function remains challenging due to the propensity of RL algorithms to exploit the reward system in unforeseen ways. Agents might fulfill objectives in unexpected manners, highlighting the complexity of encoding desired behaviors, like adherence to social norms, into a reward function. An alternative strategy, imitation learning, circumvents the intricacies of reward engineering by having the agent learn through the emulation of expert behavior. However, acquiring a sufficient number of high-quality demonstrations for this purpose is often impractically costly. Humans, in contrast, learn with remarkable autonomy, benefiting from intermittent guidance from educators who provide tailored feedback based on the learner's progress. This interactive learning model holds promise for artificial agents, offering a customized learning trajectory that mitigates reward exploitation without extensive reward function engineering. The challenge lies in ensuring the feedback process is both manageable for humans and rich enough to be effective. Despite its potential, the implementation of human-in-the-loop (HiL) RL remains limited in practice. Our research endeavors to significantly lessen the human labor involved in HiL learning, leveraging both unsupervised pre-training and preference-based learning to enhance agent development with minimal human intervention.
Keywords
reinforcement learning from human feedback, preference learning
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Master Thesis
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Published since: 2024-03-25
Organization ETH Competence Center - ETH AI Center
Hosts Li Chenhao , Li Chenhao , Chen Xin , Li Chenhao
Topics Information, Computing and Communication Sciences , Engineering and Technology
Machine Learning with little data: PCE on agent-based model of osteoporosis and its treatments
Combine two exploding fields in computer science: machine learning and agent-based modelling. Based on preclinical and in vitro studies of cell behaviour and cytokine reaction-diffusion and mechanical tests we have generated an in-house biofidelic agent-based model of the human skeleton and its response to diseases and their treatments. This model reproduces the effects of several widely used osteoporosis treatments on key parameters used to quantify fracture risk. This rule-based approach involves studying bone mechanobiology at the cell scale and extrapolating this to millions of cells at the tissue scale to understand the pharmacokinetics of treatments and identify possible new therapies and approaches to patient-specific treatment. An alternative approach to in silico prediction of response to treatment is a supervised learning approach where we simply input baseline and follow-up bone scans to a CNN with twelve layers constructed using keras. We then attempt to dive into the black box and quantify what characteristics of the input govern the response of our model. The issue is the clinical data is not big enough to do this well so we use the agent-based model as input to the ML approach to construct a proxy model! This also helps us understand, validate and quantify the uncertainty in the agent-based model. To decide which runs of the agent-based model to use as input to the ML approach to construct the proxy model we use polynomial chaos expansion.
Keywords
machine learning, artificial intelligence, uncertainty quantification, polynomial chaos expansion, agent-based modelling, bone mechanobiology, osteoporosis, patient-specific treatment, personalized medicine, innovation, therapy, medical research, fragility, fractures
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2024-03-19 , Earliest start: 2024-04-01 , Latest end: 2025-01-01
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , Empa , Eawag , Paul Scherrer Institute , University of Zurich , Wyss Translational Center Zurich , Zurich University of Applied Sciences , Swiss Institute of Bioinformatics , Swiss National Science Foundation , Balgrist Campus , Berner Fachhochschule , CERN , Corporates Switzerland , CSEM - Centre Suisse d'Electronique et Microtechnique , Department of Quantitative Biomedicine , Hochschulmedizin Zürich , IBM Research Zurich Lab , Institute for Research in Biomedicine , Sirm Institute for Regenerative Medicine , Università della Svizzera italiana , Université de Neuchâtel , University of Basel , University of Berne , University of Fribourg , University of Geneva , University of Lausanne , University of Lucerne , University of St. Gallen , RWTH Aachen University , Ludwig Maximilians Universiy Munich , University of Cambridge , University of Oxford , UCL - University College London , Imperial College London , Delft University of Technology , Maastricht Science Programme , IDEA League
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Ledoux Charles
Topics Information, Computing and Communication Sciences , Biology
Refreshing Articular Cartilage Defects by Laser Ablation - Parameter Optimization and Validation
Cartilage damage in the knee joint can be caused by aging or repetitive actions. It can be treated by surgically removing the damaged cartilage tissue and filling the generated defect with a precisely shaped, healthy cartilage graft. Removing the defected cartilage is commonly done with surgical curettes. We are investigating the use of laser ablation for a more precise defect preparation process. With two different lasers, we managed to obain promising results regarding cell viability in live samples. However, laser parameters such as pulse frequency and energy need to be optimized towards higher cutting efficiency. Your task will be to prepare a setup to test, optimize, and validate various parameter sets using different lasers for articular cartilage ablation.
Keywords
Laser ablation, laser parameter optimization, cartilage regeneration, biomedical engineering
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Master Thesis
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Published since: 2024-03-15 , Earliest start: 2023-02-01 , Latest end: 2023-12-31
Organization Bio-Inspired RObots for MEDicine-Laboratory (BIROMED-Lab)
Hosts Duverney Cédric , Rauter Georg, Prof. Dr.-Ing. , Rauter Georg, Prof. Dr.-Ing.
Topics Engineering and Technology , Physics
A Personalized Bone Organoid Diagnostic Framework for Predicting Drug Response in Children with Rare Bone Diseases
Rare genetic disorders are defined by a prevalence of fewer than 1/2000 people, are chronic and affect patients throughout their lifespan. Osteogenesis imperfecta (OI) is a heterogeneous group of rare genetic bone disorders. OI is a debilitating condition that involves impaired mobility, high fracture incidence and subsequent limb deformities. No treatment exists today that targets the underlying abnormal collagen structure and organization. The mainstay in pediatric care of OI remains antiresorptive therapy with bisphosphonates, despite concerns of long-term effects on depressed bone turnover. While antiresorptive monoclonal antibody treatments are currently undergoing clinical trials in children and young adults, anabolic treatments that directly increase bone formation are currently approved for adults only and decrease in efficacy over a relatively short time span. The experience with these drugs in OI therapy is limited, as clinical studies are still ongoing.
Keywords
bone organoid, diagnostics, bone diseases, 3D bioprinting, personalized medicine
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Semester Project , Internship , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-03-15 , Earliest start: 2023-11-01 , Latest end: 2024-07-31
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Schädli Gian Nutal
Topics Engineering and Technology
Using LLMs to adjust to human preferences during human-robot collaboration
We want to exploit LLMs to adjust to human preferences while interacting. We think that we can generate desired behaviors by leveraging LLMs to translate the natural language to robot motion, e.g. "move faster, lift higher, come closer". We aim to carry out tests in a robot-human handover scenario.
Keywords
LLMs, deep learning, human-robot interaction, legged robots
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Master Thesis
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Published since: 2024-03-14
Organization Robotic Systems Lab
Hosts Zurbrügg René , Tulbure Andreea
Topics Information, Computing and Communication Sciences , Behavioural and Cognitive Sciences
Master Thesis/ Internship: Causal Machine Learning with Experts in the Loop for Spinal Cord Injury (SCI) Comorbitities
Despite the growing amount of work on applying causal discovery method with expert knowledge to areas of interest, few of them inspect the uncertainty of expert knowledge (what if the expert goes wrong?). This is highly important since that in scientific fields, causal discovery with expert knowledge should be cautious and an approach taking expert uncertainty into account will be more robust to potential bias induced by individuals. Therefore, we aim to develop an iterative causal discovery method with experts in the loop to enable continual interaction and calibration between experts and data. Based on the qualifications of the candidates, we can arrange a subsidy/allowance for covering traveling or living costs.
Keywords
Causal Discovery, Expert Knowledge, Iterative Algorithm, Spinal Cord Injury
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Semester Project , Internship , Master Thesis
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Published since: 2024-03-13 , Earliest start: 2024-04-15 , Latest end: 2024-10-15
Organization Sensory-Motor Systems Lab
Hosts Paez Diego, Dr. , Paez Diego, Dr.
Topics Medical and Health Sciences , Mathematical Sciences , Information, Computing and Communication Sciences
Unraveling Calcium Dynamics and Immune Interactions in Bone Graft Substitute Environments through Advanced Ratiometric Imaging
This project endeavors to explore the dynamic interplay among calcium ions, bone graft substitutes, and resident immune cells in both orthotopic and ectopic environments, employing advanced ratiometric imaging techniques.
Keywords
Bone Graft Substitute, Calcium, Ratiometric Imaging, Immune Cells, in vitro, in vivo, Intravital Microscopy
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2024-03-12 , Earliest start: 2024-04-01 , Latest end: 2024-12-31
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Wissmann Stefanie
Topics Engineering and Technology , Biology
Advancing Spinal Fusion Surgery Predictions
Join us in this exciting project that seeks to contribute to the improvement of spinal fusion surgery. This project offers the chance to develop and validate an advanced pipeline based on finite element modeling, other mechanical modeling approaches and computer vision. By leveraging a comprehensive dataset of pre- and postoperative CT scans, you'll have the opportunity to closely collaborate with clinicians and research engineers, ensuring the real-world applicability of your work.
Keywords
Biomechanical modeling, finite element modeling, orthopedics, medical imaging, biomedical engineering, personalized medicine
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Semester Project , Internship , Master Thesis
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Published since: 2024-03-11 , Earliest start: 2024-03-18 , Latest end: 2024-11-30
Organization Snedeker Group / Laboratory for Orthopaedic Biomechanics
Hosts Götschi Tobias
Topics Medical and Health Sciences , Engineering and Technology
Deploying Locomotion Policies Trained in Differentiable Simulation on Real Hardware
In recent years, using deep Reinforcement Learning (RL) for robotic motion policies has demonstrated impressive performance, yielding unprecedented robustness on real hardware. Current sim2real approaches rely on large-scale pre-training with domain randomization to make policies robust but struggle with high-dimensional spaces. Current RL methods are primarily limited by their low sample efficiency. Leveraging differentiable simulators for first-order gradient information shows great results for enhancing sample efficiency. Although promising simulation results exist, deployment on hardware is not usually done. The goal of this thesis is to train quadrupedal locomotion policies in a differentiable simulation framework, and then enable real-world deployment by modifying the simulation, the policy training, or the learning algorithm. Ideally, we can leverage properties of differentiable simulators in this process to improve sim2real transfer by fitting real data.
Keywords
Deep Reinforcement Learning, Differentiable Simulation, Quadrupedal Locomotion Control, Sim2Real
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Semester Project , Master Thesis
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Published since: 2024-03-11
Organization Robotic Systems Lab
Hosts Klemm Victor
Topics Information, Computing and Communication Sciences , Engineering and Technology
Development of a Heterocellular Human Bone Organoid for Precision Medicine and Treatment
Our goal is to establish a heterocellular 3D printed bone organoid model comprising all major bone cell types (osteoblasts, osteocytes, osteoclasts) to recapitulate bone remodeling units in an in vitro system. The organoids will be produced with the human cells, as they could represent human pathophysiology better than animal models, and eventually could replace them. These in vitro models could be used in the advancement of next-generation personalised treatment strategies. Our tools are different kinds of 3D bioprinting platforms, bio-ink formulations, hydrogels, mol-bioassays, and time-lapsed image processing of micro-CT scans.
Keywords
3D printing, bone organoids, co-culture, bioreactor, hydrogels, drug testing
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Semester Project , Internship , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-03-08 , Earliest start: 2022-08-01 , Latest end: 2024-08-31
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Steffi Chris
Topics Engineering and Technology , Biology
Investigation of gait performance and brain activity during walking in Parkinson’s patients
Parkinson's disease is a prevalent neurodegenerative condition in individuals over 60 years old. It results from impaired dopaminergic cells in the basal ganglia, leading to gait disturbances and reduced independence. While treatment options like dopamine replacement therapies and Deep-Brain Stimulation (DBS) exist, not all patients benefit from DBS. The lack of reliable biomarkers hampers understanding of surgical outcomes. A new DBS device enables wireless recording of subcortical brain activity, offering novel insights into Parkinson's subcortical activity. To explore personalized therapies, this study will measure the gait performance, neuro-activities like deep brain activity as well as electroencephalography (EEG) during walking in Parkinson's patients. Combining cortical (EEG) and subcortical (DBS) recordings aim to investigate comprehensive brain activity during pathological gait.
Keywords
Parkinson's disease, Gait, EEG, EMG
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Semester Project , Collaboration , Internship , Lab Practice , Master Thesis
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Published since: 2024-03-07 , Earliest start: 2023-09-01
Organization Neuromuscular Biomechanics
Hosts Mei Zhongke
Topics Medical and Health Sciences , Information, Computing and Communication Sciences
Unravelling the spatial and biomechanical dynamic of fracture healing in mice
Fracture healing is a complex process that involves inflammation, angiogenesis, and bone remodeling. The remodelling process helps maintain bone density, repair micro-damage that occurs due to everyday activities, and adapt bones to the specific needs of an individual's body. Mechanical loading is a crucial factor in the regulation of fracture healing. The forces and strains experienced by the bone during everyday activities influence the cellular responses, callus formation, bone deposition, remodelling, and, ultimately, the successful recovery of the fractured bone. The mechanisms underlying spatial cell reorganization during loading, which contributes to fracture healing, remain unclear. The project aims to investigate and explore the fracture healing process of mice using spatial transcriptome changes in response to mechanical loading. By shedding light on this aspect, the project aims to contribute to the broader understanding of fracture healing and potentially pave the way for more effective treatment strategies in the future.
Keywords
Spatial transcriptomics, Dimensionality reduction, Spatial expression pattern, Spatial interaction, Cell Segmentation and Visualization, Fracture healing, Bone
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IDEA League Student Grant (IDL) , Semester Project , Course Project , Internship , Bachelor Thesis , Master Thesis , ETH for Development (ETH4D) (ETHZ) , ETH Zurich (ETHZ)
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Published since: 2024-03-07 , Earliest start: 2024-03-07 , Latest end: 2024-08-01
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Singh Amit
Topics Medical and Health Sciences , Mathematical Sciences , Information, Computing and Communication Sciences , Engineering and Technology , Biology , Physics
Exploring the 3D Mineralization Behavior in Material-Induced Osteoinduction Through a Multiscale Micro-CT Imaging Approach
The project aims at investigating material-induced osteoinduction using the available mouse model of orthotopic or ectopic bone graft substitute application. Through the 3D-3D registration of ex vivo and in vivo multiscale micro-CT images, crucial 3D mineralization of the BGS can be investigated.
Keywords
Femur, Bone Graft Substitute, Critical Size Defect, Osteoinduction, in vivo, micro-CT, 3D-3D Image Registration, Image Analysis, Image Processing, Python, Computational
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Semester Project , Bachelor Thesis
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Published since: 2024-03-06 , Earliest start: 2024-04-01 , Latest end: 2024-12-31
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Lindenmann Sara
Topics Medical and Health Sciences , Engineering and Technology
Towards AI Safety: Adversarial Attack & Defense on Neural Controllers
The project is collaborating between SRI and RSL/CRL lab and aims to investigate the weakness of the neural controller based on the state-of-the-art [3] attacking method.
Keywords
Adversarial attack; safe AI; Reinforcement learning
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Semester Project , Master Thesis
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Published since: 2024-03-06 , Earliest start: 2024-03-06 , Latest end: 2024-09-30
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Robotic Systems Lab
Hosts Shi Fan , Shi Fan , Shi Fan
Topics Information, Computing and Communication Sciences , Engineering and Technology
Robotic 3D printing Microbial Biocement
The project investigates different bio-inks for extruding large-scale 3D printing bio-cementation structures. The extruded paste will host microorganisms such as S.Pasteurii, capable of precipitating calcite (MICP) to create bio-concrete structures. A robotic paste 3D printing platform will be used for the fabrication process; the bio-paste will be precipitated and calcified by the bacterial activity reinforcing the material.
Keywords
Living materials, bio-inks, 3D printing, hydrogel, architecture, bio-cementation, MICP
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Semester Project , Internship , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-03-04 , Earliest start: 2024-03-31 , Latest end: 2024-07-31
Organization Digital Building Technologies
Hosts Antorveza Karen
Topics Engineering and Technology , Biology , Architecture, Urban Environment and Building
Learning Diverse Adversaries to Black-box Learning-based Controller for Quadruped Robots
The project aims to leverage the latest unsupervised skill discovery techniques to validate the state-of-the-art black-box learning-based controllers in diverse ways.
Keywords
Diversity in RL, Trustworthy AI
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Semester Project , Master Thesis
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Published since: 2024-03-02 , Earliest start: 2024-03-02 , Latest end: 2024-08-28
Applications limited to ETH Zurich , [nothing]
Organization Robotic Systems Lab
Hosts Shi Fan , Shi Fan , Shi Fan
Topics Information, Computing and Communication Sciences
Mycelium materials and digital fabrication
The project aims to explore the bio-fabrication of mycelium-based composites and knitted textiles for architecture and construction. Specifically the textile is used as a growing substrate for mycelium material, offering a sustainable and biodegradable building material and structural system that is strong in both tension and compression.
Keywords
mycelium,textiles,performance,construction, bio-fabrication
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Semester Project , Internship , Bachelor Thesis , Master Thesis , Other specific labels , ETH Zurich (ETHZ)
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Published since: 2024-03-01 , Earliest start: 2024-06-01 , Latest end: 2024-12-01
Organization Digital Building Technologies
Hosts Dillenburger Benjamin
Topics Engineering and Technology
Quantification of Dynamic Sitting Behavior Using Pressure Distribution and IMU Sensors
Everyone sits. We spend more time seated than sleeping or walking, and today's human behavior shows this trend is growing. Moovtech technology aims to help recover from sedentary back pain as well as strengthen core and spinal muscles to prevent future health issues. The Moovlab technology (Moovtech) and the revolutionary PVOT dynamic motion chair show high potential for improving people's lives and health, as well as potentially stimulating brain activity and work productivity. Additionally, sitting on a PVOT chair during work hours (home office or corporate setting) has the potential to prevent chronic pains in the long term. The motion initiated by Moovtech simulates pelvic movement when walking. The aim of this internship is to equip this innovative office chair with sensor technology to analyze the sitting behavior and usage of this novel chair. This work will serve as the foundation for planning a larger study and understanding the expected data outcomes.
Keywords
sitting behavior, IMU, pressure distribution, low back pain, dynamic sitting
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Published since: 2024-02-29 , Earliest start: 2024-02-29 , Latest end: 2024-09-30
Organization Sports Biomechanics Group
Hosts Zemp Roland, Dr.
Topics Medical and Health Sciences , Engineering and Technology
Towards interpretable learning pipeline: A visual-assisted workflow for locomotion learning
Current reinforcement learning (RL)-based locomotion controllers have shown promising performance. However we are still not clear about what is learned during the training process. In this project, we investigate the proper metrics and visualisation techniques to interactively steer the locomotion learning tasks.
Keywords
Reinforcement learning; visualization; interpretable AI
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Semester Project , Master Thesis
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Published since: 2024-02-28 , Earliest start: 2024-02-26 , Latest end: 2024-08-26
Organization ETH Competence Center - ETH AI Center
Hosts Zhang Xiaoyu , Shi Fan , Wang April , Shi Fan , Shi Fan
Topics Engineering and Technology
Temporal Graphical Modeling for Understanding and Preventing Autonomic Dysreflexia
This project will be based on the preliminary results obtained from a previous master project in causal graphical modeling of autonomous dysreflexia (AD). The focus of the extension would be two-fold. One is improving the temporal graphical reconstruction for understanding the mechanism of AD. The other one is building a forecasting framework for the early detection and prevention of AD using the graph structure we constructed before. Please refer to the attached document for more details about the task description. Based on the candidate's qualifications, funding/allowance can be provided.
Keywords
Graphical Modeling; Graph Neural Networks; Multivariate Time Series; Spinal Cord Injuries; Autonomic Dysreflexia; Wearable Sensing
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Semester Project , Internship , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-02-28 , Earliest start: 2024-04-01 , Latest end: 2024-10-01
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Paez Diego, Dr. , Li Yanke , Paez Diego, Dr. , Paez Diego, Dr.
Topics Medical and Health Sciences , Information, Computing and Communication Sciences
Disease Onset Forecasting through Graphical Modeling Based Digital Twin from Biomedical Data for Spinal Cord Injury Individuals
This project focuses on developing an explainable Artificial Intelligence (xAI) framework based on graphical modeling (GM), to enhance the capacity and capability of medical AI. Collaborating with the Swiss Paraplegic Centre (SPZ) for validation, our goal is to improve the long-term prognosis of spinal cord injury (SCI) individuals. Through medical records and a multimodal sensory monitoring system, we aim to create digital twins capable of integrating diverse data sources, guiding medical treatment, and addressing common secondary health conditions in the SCI population. The envisioned GM-based digital twin (GMDT) will represent hierarchical relations across demographic features, functional abilities, daily activities, and health conditions for SCI individuals, allowing for downstream tasks such as prediction, causal inference, and counterfactual reasoning. The assimilation and evolution between the physical and digital twins will be implemented under the GM framework, promising advancements in personalized healthcare strategies and improved outcomes for SCI people. Please refer to the attached document for more details about the task description. Based on the candidate's qualifications, funding/allowance can be provided.
Keywords
Graphical Modelling, Digital Twins, Causal Inference, Data Fusion, Multimodal Learning, Physiological Modelling, Spinal Cord Injuries, Digital Healthcare
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Semester Project , Internship , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-02-28 , Earliest start: 2024-03-15 , Latest end: 2024-09-30
Applications limited to TU Dresden , TU Darmstadt , TU Berlin , Technische Universität München , Technische Universität Hamburg , RWTH Aachen University , Max Planck Society , Ludwig Maximilians Universiy Munich , Humboldt-Universität zu Berlin , Eberhard Karls Universität Tübingen , Universität zu Lübeck , Imperial College London , UCL - University College London , University of Oxford , University of Cambridge , Delft University of Technology , Zurich University of Applied Sciences , Wyss Translational Center Zurich , University of Zurich , Swiss Institute of Bioinformatics , IBM Research Zurich Lab , ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , Empa , Corporates Switzerland , Zurich University of the Arts , University of St. Gallen , University of Lausanne , University of Geneva , University of Fribourg , University of Berne , University of Basel , Swiss National Science Foundation , Swiss Federal Institute for Forest, Snow and Landscape Research , Paul Scherrer Institute , CERN , Department of Quantitative Biomedicine , Eawag , University of Konstanz , University of Cologne , University of Erlangen-Nuremberg , University of Hamburg , Universtity of Bayreuth , Universität Ulm , Universität der Bundeswehr München , Social Science Research Center Berlin , National Institute for Medical Research , Royal College of Art , University of Leeds , University of Manchester , University of Nottingham , University of Aberdeen , Utrecht University , Radboud University Nijmegen , Maastricht Science Programme , Stanford University , Yale University , CNRS - Centre national de la recherche scientifique , Massachusetts Institute of Technology , Max Planck ETH Center for Learning Systems , The University of Tokyo , Tsinghua University , Peking University , Politecnico di Milano , Princeton University , Harvard , University of Toronto , University of Copenhagen , University of California, Berkeley , The University of Edinburgh , Technical University of Denmark , The University of Melbourne , The Australian National University , National University of Singapore , Nanyang Technological University
Organization Spinal Cord Injury & Artificial Intelligence Lab
Hosts Li Yanke , Paez Diego, Dr. , Paez Diego, Dr.
Topics Information, Computing and Communication Sciences , Engineering and Technology , Behavioural and Cognitive Sciences
Mechanophores for advanced wearable strain and pressure sensors
The goal of the project is to synthesize and characterize a number of small molecules capable of acting as mechanophore addition to various polymers. These polymers would then be used as wearable strain or pressure sensors.
Keywords
mechanophore, polymer, wearable, sensor, color, strain, pressure
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Master Thesis
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Published since: 2024-02-26 , Earliest start: 2023-09-01 , Latest end: 2024-08-01
Organization Biomedical and Mobile Health Technology Lab
Hosts Shokurov Aleksandr
Topics Engineering and Technology , Chemistry
Investigating compromised bone fracture healing in mouse models using time-lapsed in vivo CT imaging and histological analysis.
Delayed bone healing or failed non-unions account for 5 – 10% of all bone fractures and present a challenging problem in regenerative medicine. The impact of delayed unions or non-unions can be devastating with prolonged rehabilitation, decreased quality of life and significant health care costs. Our lab has conducted fracture healing studies in young and prematurely-aged mouse models with different defect sizes. The aim of this project is to analyse data from mice which exhibit delayed unions and non-unions.
Keywords
Bone, Fracture Healing, Image Processing, Histology
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2024-02-23 , Earliest start: 2024-02-01 , Latest end: 2025-02-01
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Mathavan Neashan
Topics Engineering and Technology
Agent-based modelling of bone regeneration in ageing populations
Are you a motivated Bachelor's or Master's student willing to learn and develop a micro-Mulitphysics Agent-Based (micro-MPA) model to predict adaptation and regeneration of aged bone? This project offers an opportunity to gain valuable work experience in computational modelling within a highly interdisciplinary Lab.
Keywords
agent-based, machine learning, artificial intelligence, modelling, bone mechanobiology, ageing, personalized medicine, medical research, biomechanics
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Semester Project , Internship , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-02-21 , Earliest start: 2024-02-19 , Latest end: 2024-09-30
Applications limited to ETH Zurich
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Kendall Jack
Topics Medical and Health Sciences , Engineering and Technology , Biology
End-to-end Leaning Terrain Cost with Robot Kinematic Constraints
Navigating the unpredictable off-road environment, autonomous robots require a tailored approach to overcome obstacles and optimize pathfinding. Our proposed terrain cost mapping system goes beyond traditional processing by factoring in each robot's specific kinematic abilities. We introduce a novel simulation-based Roll-Out technique to predict a robot's stability over varied terrains, thereby calculating a precise terrain cost. This innovative strategy promises to enhance autonomous navigation by ensuring safe and efficient traversal tailored to individual robotic capabilities.
Keywords
Leaning Terrain Cost; Off-road Navigation; Robot Kinematics
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Semester Project , Master Thesis
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Published since: 2024-02-21 , Earliest start: 2023-11-30 , Latest end: 2024-01-31
Organization Robotic Systems Lab
Hosts Yang Fan
Topics Information, Computing and Communication Sciences
Analyzing and Improving Latent Diffusion Models
Latent diffusion models (LDMs) [1] have recently emerged as a powerful tool for high-quality image generation, offering superior scalability and training efficiency compared to pixel-space diffusion models. While the network architectures of LDMs have received significant attention, other design aspects of these models (for example the forward noise schedule and the autoencoder) remain underexplored. This project aims to enhance the characteristics of LDMs, e.g., quality and efficiency, by investigating various design elements of latent diffusion models.
Keywords
diffusion models
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Semester Project , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-02-20
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Organization Advanced Interactive Technologies
Hosts Sadat Seyedmorteza
Topics Information, Computing and Communication Sciences
Learn to predict intent using commonsense knowledge
From robotics to human-computer interaction, there are numerous real-world tasks that would benefit from practical systems that can anticipate future high-level actions and predict intention and goals based on observation of the past. Intention prediction is not only important for care robots to anticipate people’s actions but is also a key challenge in the design of artificial intelligent systems.
Keywords
intent prediction, video understanding, large language model, nlp
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Semester Project , Master Thesis , CLS Student Project (MPG ETH CLS) , ETH Zurich (ETHZ)
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Published since: 2024-02-17 , Earliest start: 2024-03-01 , Latest end: 2024-08-01
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Organization Advanced Interactive Technologies
Hosts Wang Xi
Topics Information, Computing and Communication Sciences
Embedded algorithms of IMUs in a neurorehabilitation device
The goal of this project is to help develop embedded firmware for a imu based rehabilitation device. This project is part of the SmartVNS project which utilizes movement-gated control of vagus nerve stimulation for stroke rehabilitation.
Keywords
electrical engineering PCB Embedded systems neurorehabilitation
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Semester Project , Master Thesis
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Published since: 2024-02-14 , Earliest start: 2024-01-06 , Latest end: 2024-12-31
Organization Rehabilitation Engineering Lab
Hosts Donegan Dane , Viskaitis Paulius
Topics Medical and Health Sciences , Engineering and Technology
Development of a Clinically Usable Electrode for tVNS
This project aims to develop a clinically usable electrode for transcutaneous vagus nerve stimulation (tVNS) therapy. The objective is to create an electrode that is biocompatible, low-impedance, and easy to use, allowing patients to apply it themselves with minimal setup time. The project involves conducting a literature review, evaluating existing designs, selecting appropriate materials, developing a prototype, and assessing its efficacy and usability in a clinical setting. The outcome will be an electrode that enhances the convenience and effectiveness of tVNS therapy, contributing to improved patient treatment adherence and outcomes.
Keywords
Mechanical engineering, materials engineering, 3D modeling, anatomical modeling, CAD.
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Semester Project , Internship , Master Thesis
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Published since: 2024-02-14 , Earliest start: 2023-06-01
Organization Rehabilitation Engineering Lab
Hosts Viskaitis Paulius , Donegan Dane
Topics Engineering and Technology
Activity and fatigue detection using machine learning based on real-world data from smart clothing
The aim of this project is to use machine learning methods to extract useful information such as activity type and fatigue level from real-world data acquired from our textile-based wearable technology during sport activities.
Keywords
smart clothing, wearable technology, textile sensor, fitness tracking, sports medicine, fatigue, machine learning, artificial intelligence, computer science
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2024-02-14 , Earliest start: 2023-09-15 , Latest end: 2024-05-31
Organization Biomedical and Mobile Health Technology Lab
Hosts Ahmadizadeh Chakaveh
Topics Information, Computing and Communication Sciences , Engineering and Technology
Develop software for wearable technologies
The aim of this project is to develop mobile software to communicate with our already developed textile-based wearable technology and process sensor data for movement monitoring.
Keywords
smart clothing, wearable technology, software development, fitness tracking, sports medicine, mobile application, computer science
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2024-02-14 , Earliest start: 2023-09-15 , Latest end: 2024-05-31
Organization Biomedical and Mobile Health Technology Lab
Hosts Ahmadizadeh Chakaveh
Topics Information, Computing and Communication Sciences , Engineering and Technology
Design data acquisition solution for smart clothing
The aim of this project is to develop and improve wearable electronics solutions for data acquisition from textile-based sensors used in our smart clothing.
Keywords
smart clothing, wearable technology, textile sensor, fitness tracking, sports medicine, PCB, electronics, computer science
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2024-02-14 , Earliest start: 2023-09-15 , Latest end: 2024-05-31
Organization Biomedical and Mobile Health Technology Lab
Hosts Ahmadizadeh Chakaveh
Topics Information, Computing and Communication Sciences , Engineering and Technology
Conduct human gait study with optical motion capture to assess smart clothing for movement monitoring
We aim to conduct a study with human participants to assess the function of our textile-based wearable technology for movement monitoring in clinical and fitness scenarios.
Keywords
smart clothing, wearable technology, textile sensor, fitness tracking, sports medicine, rehabilitation, human study, motion capture, computer science
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2024-02-14 , Earliest start: 2023-09-15 , Latest end: 2024-05-31
Organization Biomedical and Mobile Health Technology Lab
Hosts Ahmadizadeh Chakaveh
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Imposition of non-fixed convex constraints on Neural Networks
This project aims to answer the unsolved question of how to guarantee (in a computationally efficient way) hard convex constraints on the output of a network when the parameters that define the constraints change.
Keywords
Neural networks, convex, constraints, hard, optimization
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Semester Project , Collaboration , Master Thesis
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Published since: 2024-02-13 , Earliest start: 2024-02-13 , Latest end: 2025-03-29
Organization Robotic Systems Lab
Hosts Tordesillas Jesus
Topics Mathematical Sciences , Information, Computing and Communication Sciences
Establishing Volumetrically Bioprinted Human In Vitro Bone Organoid Models
Laboratory-grown miniature bones (organoids) can facilitate the investigation of the biology in healthy and diseased human bone, thereby replacing animal experiments and providing a mechanistic understanding of bone remodeling. The goal of this research is to establish an in vitro technique for volumetric 3D bioprinting of structurally complex human bone organoids. This bone organoid has the potential to enable studying human bone remodeling in the laboratory without the need for animal models.
Keywords
volumetric bioprinting, hydrogels, bone tissue engineering, bone remodeling
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Semester Project , Internship , Master Thesis
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Published since: 2024-02-12 , Earliest start: 2024-01-03 , Latest end: 2024-12-23
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts de Bregje
Topics Engineering and Technology , Biology
Screening Microenvironmental Cues for In Vitro Human Bone Models
3D in vitro models provide a valuable way to study human biology without using animals. However, these models are primarily based on poorly defined animal-derived hydrogels, such as Matrigel or collagen. This limits our detailed understanding of cell-material interactions in bone development, maintenance, and repair. Importantly, these mechanisms are often disrupted in various bone diseases, highlighting the needs for more advanced in vitro models.
Keywords
biomaterials, hydrogels, in vitro models, tissue engineering
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Published since: 2024-02-12 , Earliest start: 2023-10-01 , Latest end: 2024-02-29
Applications limited to ETH Zurich
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Qin Xiao-Hua, Prof. Dr. , Horrer Marion
Topics Medical and Health Sciences , Engineering and Technology , Chemistry , Biology
3D Human Modeling and Performance Capture
Digital capture of human bodies is a rapidly growing research area in computer vision and computer graphics that puts scenarios such as life-like mixed-reality (MR) virtual-social interactions into reach. Therefore, we offer projects for modeling and capturing humans at the intersection of computer vision, computer graphics, and machine learning.
Keywords
computer vision, deep learning, 3D reconstruction, 3D human
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Semester Project , Master Thesis
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Published since: 2024-02-08 , Earliest start: 2024-02-01 , Latest end: 2024-07-31
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Organization Advanced Interactive Technologies
Hosts Guo Chen , Song Jie
Topics Information, Computing and Communication Sciences
Using musical cues to assess risk of fall of older adults in outdoor environments
Currently, individuals at risk of falling are identified through clinic- and lab-based assessment of gait and movement function. These tests evaluate changes in motor skills in a steady environment free of disturbances, while most falls occur during real life environments with disturbances such as obstacles and uneven walking surfaces, thus they are not precise enough for the quantification of fall risk. A sensitive marker for fall risk can therefore be identified through assessing walking behavior in real-life.
Keywords
fall risk, biomechanics, sensors, wearables, music
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Semester Project , Internship , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-02-08 , Earliest start: 2024-02-01 , Latest end: 2024-12-31
Organization Neuromuscular Biomechanics
Hosts Ravi Deepak
Topics Medical and Health Sciences , Engineering and Technology
Towards mitigating fall risk in older adults using auditory noise stimulation
Current approaches available to mitigate fall risk in older adults have prejudiced emphasis on a) protective equipment such as walking aids, footwear etc., b) environmental modification b) physical therapy and exercise programs. Despite the efforts, the world’s population is ageing and falling in older adults are on the rise. As such, development of more effective interventions for reducing fall risk is a global research priority. Our team is working on a new approach based on auditory noise stimulation for inducing improvement in balance during walking and ultimately to reduce fall-risk in older adults.
Keywords
fall risk, balance, auditory noise stimulation, muscle activity
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Semester Project , Internship , Master Thesis
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Published since: 2024-02-08 , Earliest start: 2024-02-01 , Latest end: 2024-12-31
Organization Neuromuscular Biomechanics
Hosts Ravi Deepak
Topics Medical and Health Sciences , Engineering and Technology
Using predictive musculoskeletal modelling to understand the impact of foot kinematics on the knee joint loading during walking.
We will use predictive musculoskeletal modelling tools to understand the impact of foot kinematics (ankle plantar/dorsi flexion) on the knee joint loading during gait.
Keywords
Knee, biomechanics, musculoskeletal modelling, predictive
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Master Thesis
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Published since: 2024-01-29 , Earliest start: 2024-02-01 , Latest end: 2025-01-31
Organization Computational Biomechanics
Hosts Hosseini Seyyed
Topics Engineering and Technology
Human Organoid-on-Chip to Study Rare Bone Disease
To date, there is still very limited progress in developing organoid models for human musculoskeletal tissues such as bone. A major challenge is reconstructing the native bone microenvironment which is structurally and functionally complex. In this project, we leverage interdisciplinary advances in tissue engineering and microtechnologies to generate a microengineered bone-organoid-on-chip platform for both fundamental and translational research in medicine.
Keywords
microfluidics, 3D cell culture, bone, disease modeling, hydrogels
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Semester Project , Internship , Master Thesis
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Published since: 2024-01-17 , Earliest start: 2024-02-01 , Latest end: 2025-03-31
Applications limited to ETH Zurich
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Zauchner Doris , Qin Xiao-Hua, Prof. Dr.
Topics Engineering and Technology , Chemistry , Biology
TMS-based decoding of executed and imagined hand actions
Neurofeedback (NF) is a promising approach for training healthy participants and patients to modulate their motor-related neural activity even in the absence of overt motor output. Motor imagery (MI)-based training, i.e., participants mentally simulate movements, also has beneficial effects on the restoration of impaired motor function. Transcranial magnetic stimulation (TMS) is a non-invasive, low-risk method that is routinely used for psychological or neuroscientific research in human participants. In comparison to electroencephalography, TMS-based NF has great potential to distinguish fine-grained MI tasks such as different hand actions. This is important because daily life activities require complex coordination of hand muscles. A hand function training is critical for individuals with impaired hand function. Our group has developed a new protocol that uses TMS to detect MI-induced motor activity patterns in the primary motor cortex. Here we will use TMS over the primary motor cortex of participants to measure motor evoked potentials (MEPs) in finger muscles during either motor execution or motor imagery of different hand actions, namely holding a bottle, turning a key and opening the hand. Based on the MEPs we provide participants visual feedback. We aim to further develop and validate an online, adaptive classification algorithm that decodes imagined hand actions in healthy volunteers from TMS-evoked MEPs and potentially apply this to stroke survivors. Our group has recently completed the pilot data acquisition investigating the performance of an adaptive classification algorithm for decoding imaged hand actions during TMS-based NF training. We will continue with data collection and include brain MRI scans to further develop and validate this novel TMS-based NF training protocol.
Keywords
Transcranial magnetic stimulation (TMS), electromyography (EMG), functional magnetic resonance imaging (fMRI), machine learning
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Master Thesis
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Published since: 2024-01-14 , Earliest start: 2024-05-01 , Latest end: 2024-12-31
Organization Neural Control of Movement Lab
Hosts Cheng Hsiao-Ju
Topics Medical and Health Sciences
Generative AI for Food Amount Estimation – Synthesize 3D Food Images
This thesis aims to enhance food volume estimation in healthcare settings, a critical factor in patient nutrition management. The project involves developing a machine learning system capable of synthesizing 3D food images. The enhanced accuracy of volume estimations will be achieved through an expanded training set enriched with real-world food data from hospitals. This research, conducted by an ETH team, is poised to significantly impact patient care in facilities, particularly in addressing malnutrition. The initiative is not just academic but also a stepping stone towards a potential startup venture, emphasizing the project's practical applicability and future growth potential.
Keywords
3D Image Synthesis, Generative AI, Representation Learning, Volume Estimation, Machine Learning, Healthcare, Startup, Nutritional Management, Data Augmentation, Depth Imaging, AI in Healthcare, Food Volume Analysis, Deep Learning
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Master Thesis
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Published since: 2023-12-26 , Earliest start: 2024-01-01 , Latest end: 2024-10-31
Organization Laboratory of Exercise and Health (De Bock group)
Hosts Iten Raban
Topics Information, Computing and Communication Sciences
DELTA
This project investigates the possibility to use low budget sensors such as webcams and IMUs to measure movement of stroke patients and quantify the movement quality. This low cost approach will allow to scale the solutions and bring instrumented solutions into clinical application. Integral part of this project is to develop and validate algorithms, create user-friendly apps and translate the new technology into clinical application. This project is a collaboration between ETH and cereneo foundation and is thus based in Zurich and Vitznau/Hertenstein.
Keywords
AI, markerless motioncapture, stroke, assessments, computer vision, low budget, IMU
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Semester Project , Course Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2023-12-13 , Earliest start: 2023-12-18
Organization Rehabilitation Engineering Lab
Hosts Unger Tim
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology , Behavioural and Cognitive Sciences