ETH Competence Center - Competence Center for Rehabilitation Engineering and Science (RESC)

Open Opportunities

First measurements with an instrumented campus board ETH Zurich Sensory-Motor Systems Lab Climbers train their finger strength on the campus board. If the forces at the bars are measured, performance analysis becomes possible. In a recent bachelor thesis, we designed an instrumentation for a campus board which should now be further developed to run first experiments in campus board training. Engineering and Technology, Human Movement and Sports Science Bachelor Thesis, Internship, Master Thesis, Semester Project

MOVEMENTS CLASSIFICATION BASED ON PRESSURE-SENSING MATTRESS DATA ETH Zurich Sensory-Motor Systems Lab This Master Thesis aims to develop an algorithm to classify different movements that could occur during sleep. Data is readily available from previous recordings but can be extended using additional measurements. Engineering and Technology Master Thesis

Quantification and validation of human movement using wearable devices ETH Zurich Neuromuscular Biomechanics Functional motor tasks such as walking are performed through a complex mechanism regulated by the human sensory-motor system (HSMS), such as subcortical regions (Basal ganglia and brain stem). HSMS assemble information from proprioceptive, vestibular, and visual receptors and continually transforms this information into the appropriate motor output with a certain degree of motor variability. The assessments of functional movements are conventionally carried out in a laboratory setting, requiring highly intensive labour and expensive equipment. Several investigations have proposed overcoming the limitations by utilising inertial measurement units (IMU) provide an alternative measuring paradigm, requiring much less labour and data acquisition in different settings. However, the characteristic of IMUs face issues such as phenomena called drift, causing an error in deriving accurate kinematic data to the optical motion capture system. The study is to provide objective quantification to assess the quality of human movement non-invasively by understanding bio-signal from various parts of the body. Biomedical Engineering, Electrical and Electronic Engineering, Interdisciplinary Engineering, Mechanical and Industrial Engineering Internship, Master Thesis

Closed-loop optimization of vagus nerve stimulation for functionally selective neuromodulation ETH Zurich Neuroengineering Lab Development of a closed-loop machine learning algorithm to extend the therapeutical uses of vagus nerve stimulation by improving the selectivity of electrical nerve stimulation, trained on a computational model and validated against experimental data. Biomedical Engineering Master Thesis, Semester Project

Development of machine learning models of peripheral nerve stimulation integrated into an online 3D platform for neuroprosthetic applications ETH Zurich Neuroengineering Lab Computational modeling of the PNS is a complex tool whose domain spans several physical scales. The long time and cost of their execution can be immensely reduced by the use of AI estimators, which will enable the development of an online 3D platform for real-time model manipulation and computation. Biomedical Engineering, Computer Graphics, Simulation and Modelling Master Thesis, Semester Project

Development of smart garments to monitor the frontal plane knee angle for sports and medical applications ETH Zurich Biomedical and Mobile Health Technology Lab The aim of this project is to develop an unobtrusive wearable for the measurement of the frontal plane knee angle. Measurements made by such a device can be used for the assessment of anterior cruciate ligament (ACL) injuries, for rehabilitation, or to prevent injuries in sports. Engineering and Technology, Information, Computing and Communication Sciences Collaboration, Master Thesis, Semester Project

Wearable Technology for Back Movement Monitoring in Patients with Low Back Pain ETH Zurich Biomedical and Mobile Health Technology Lab This project aims to develop light and unobtrusive wearable technologies using thread-like sensors to monitor back movements. The information provided by the developed technologies will be used to address a major health issue, i.e., low back pain, through its prevention or rehabilitation. Engineering and Technology, Information, Computing and Communication Sciences, Medical and Health Sciences Collaboration, Master Thesis, Semester Project

Machine Learning for Healthcare: Signal Quality Assessment of Biosignals ETH Zurich Biomedical and Mobile Health Technology Lab We aim to develop an efficient algorithm able to run in real-time within a mobile phone, to collect high-quality electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Engineering and Technology, Information, Computing and Communication Sciences, Mathematical Sciences, Medical and Health Sciences Bachelor Thesis, Master Thesis, Semester Project

Machine Learning Evaluation of A Multi-User High-Performance Computer ETH Zurich Biomedical and Mobile Health Technology Lab Explore how machine learning algorithms are impacted by the several choices of general-purpose computing capable (not only graphics) of our GPU station to produce a high computation environment with minimal cost. More importantly, our system will be recommended to facilitate computational tasks of research projects for faculty members and students. Engineering and Technology, Information, Computing and Communication Sciences, Mathematical Sciences, Physics Bachelor Thesis, Master Thesis, Semester Project

Accelerometer Data Analysis for Health Monitoring ETH Zurich Biomedical and Mobile Health Technology Lab We aim to develop a machine learning model for accelerometer data collected from a user’s smartphone that can identify the user’s behavior and circadian rhythm. Behavioural and Cognitive Sciences, Engineering and Technology, Information, Computing and Communication Sciences, Mathematical Sciences, Medical and Health Sciences, Physics Bachelor Thesis, Master Thesis, Semester Project