ETH Competence Center - Competence Center for Rehabilitation Engineering and Science (RESC)Acronym | RESC | Homepage | https://resc.ethz.ch/ | Country | Switzerland | ZIP, City | | Address | | Phone | | Type | Academy | Parent organization | ETH Zurich | Current organization | ETH Competence Center - Competence Center for Rehabilitation Engineering and Science (RESC) | Child organizations | | Members | |
Open OpportunitiesThis 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. - Engineering and Technology, Information, Computing and Communication Sciences, Medical and Health Sciences
- Bachelor Thesis, Master Thesis, Semester Project
| 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. - Computer Vision, Health Information Systems (incl. Surveillance), Health Promotion, Image Processing, Pattern Recognition, Preventive Medicine
- ETH Zurich (ETHZ), Master Thesis
| 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. - Engineering and Technology, Medical and Health Sciences
- Master Thesis, Semester Project
| 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. - Engineering and Technology
- Master Thesis
| 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. - Biomedical Engineering, Clinical Sciences, Electrical and Electronic Engineering, Human Movement and Sports Science, Interdisciplinary Engineering, Mechanical and Industrial Engineering, Neurosciences, Other
- Bachelor Thesis, Internship, Master Thesis, Semester Project
| 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. - Computer Software, Electrical and Electronic Engineering
- Bachelor Thesis, Internship, Master Thesis, Semester Project
| 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. - Biomedical Engineering, Clinical Sciences, Human Movement and Sports Science, Neurosciences, Other, Public Health and Health Services
- Bachelor Thesis, Internship, Master Thesis, Semester Project
| 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. - Engineering and Technology
- Master Thesis
| 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. - Computer Vision, Engineering and Technology, Medical and Health Sciences
- Bachelor Thesis, Internship, Master Thesis, Semester Project
| 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. - Electrical Engineering, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Pattern Recognition, Signal Processing, Simulation and Modelling
- ETH Zurich (ETHZ), Master Thesis, Semester Project
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