Student Projects
Project Descriptions
Additional projects are always available. Please consult our list of ongoing research projects and contact the involved people listed on the top right to enquire about additional projects.
Special projects/internships:
Design and implementation of an alternative control software solution to substitute LabVIEW for the operation of a robotic rehabilitation device
Stroke survivors suffering from hand impairment are often severely limited during activities of daily living. The Rehabilitation Engineering Lab developed ReHandyBot, a haptic device for hand rehabilitation that can be used by patients in the clinic and at home. In this project, the software currently controlling the device (i.e., LabVIEW) will be replaced and re-coded in another language, and the hardware will be adapted accordingly.
Keywords
control, robotics, microcontrollers, embedded systems, LabVIEW, haptics, rehabilitation engineering
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-12-19 , Earliest start: 2026-01-11 , Latest end: 2026-12-31
Organization Rehabilitation Engineering Lab
Hosts Devittori Giada
Topics Engineering and Technology
Investigating the Influence of the Menstrual Cycle on fNIRS-Based Brain Imaging
Functional near-infrared spectroscopy (fNIRS) is increasingly used as a portable, non-invasive brain imaging technique in both research and clinical settings. Despite its growing popularity, a persistent methodological issue across neuroimaging modalities is the underrepresentation or exclusion of women, justified by concerns that hormonal or physiological fluctuations across the menstrual cycle may bias brain imaging measures. Consequently, many studies either exclude women entirely or fail to report menstrual cycle–related variables, limiting the generalizability, reproducibility, and interpretability of findings. While menstrual cycle effects have been explored to some extent in other imaging modalities, such as functional magnetic resonance imaging (fMRI), it remains largely unknown whether and how physiological and hormonal fluctuations across the menstrual cycle influence fNIRS-derived hemodynamic signals, impacting data interpretation, study design, and reproducibility. We are offering a research internship / semester project / Master’s thesis focused on (i) reviewing the current state of the art on menstrual cycle effects in brain imaging, (ii) critically evaluating their implications for fNIRS, and (iii) designing and piloting an experimental protocol to better understand the influence of the menstrual cycle on fNIRS data. The project will be co-supervised by two PhD students with expertise in fNIRS signal processing and its application in neurorehabilitation research.
Keywords
functional near-infrared spectroscopy, fNIRS, menstrual cycle, brain imaging, bibliographic research, pilot study
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2025-12-17 , Earliest start: 2026-02-01 , Latest end: 2026-10-01
Organization Rehabilitation Engineering Lab
Hosts Perrin Cléo
Topics Medical and Health Sciences
Embedded Software Validation Tests for a novel medical device
This project develops a software validation framework and automated test bench to verify the firmware safety and fault-tolerance of an IMU-driven, non-invasive vagus nerve stimulation device. The work focuses on ensuring that the embedded firmware consistently enforces stimulation safety limits, maintains deterministic control-loop timing, and responds correctly to sensor faults and abnormal system conditions. Through simulated IMU inputs, controlled fault injection, and automated stimulation-output monitoring, the project provides structured evidence of safe firmware behavior across normal and stressed operating scenarios.
Keywords
Firmware, embedded, medical device,
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Semester Project , Internship , Master Thesis
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Published since: 2025-12-11 , Earliest start: 2026-01-01 , Latest end: 2026-09-01
Organization Rehabilitation Engineering Lab
Hosts Donegan Dane , Viskaitis Paulius
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Development of Regulatory Documentation for a Novel Neurorehabilitation Device: Preparation for FDA and Swissmedic Compliance
Stroke is a leading cause of long-term disability, affecting millions annually and necessitating innovative approaches to rehabilitation. The Rehabilitation Engineering Laboratory (RELab) at ETH Zurich is developing a novel closed-loop neurorehabilitation device that integrates real-time motion tracking with non-invasive brain stimulation to enhance neural plasticity and promote motor recovery in stroke patients. To advance this technology toward clinical trials, comprehensive regulatory documentation is essential to meet the stringent requirements of the U.S. Food and Drug Administration (FDA) and Swissmedic. This project focuses on preparing an Investigational Device Exemption (IDE) application for the FDA and supporting documentation for Swissmedic compliance, including technical descriptions, risk analyses, and clinical study protocols. The student will conduct literature reviews, draft regulatory documents, and support risk management in accordance with ISO 14971, contributing to the device’s regulatory pathway. This work offers a unique opportunity to gain expertise in medical device regulation, bridging biomedical engineering and neuroscience, and advancing a transformative solution for stroke rehabilitation.
Keywords
regulatory affairs, medical device, non-invasive brain stimulation, FDA, Swissmedic, investigational device exemption, IDE, stroke rehabilitation, compliance
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2025-12-11 , Earliest start: 2026-01-01 , Latest end: 2026-10-01
Organization Rehabilitation Engineering Lab
Hosts Donegan Dane , Viskaitis Paulius
Topics Medical and Health Sciences , Engineering and Technology
Exploring upper limb impairments using explainable AI on Virtual Peg Insertion Test data
This thesis aims to apply explainable AI techniques to analyze time series data from the Virtual Peg Insertion Test (VPIT), uncovering additional metrics that describe upper limb impairments in neurological subjects, such as those with stroke, Parkinson's disease, and multiple sclerosis. By preserving the full dimensionality of the data, the project will identify new patterns and insights to aid in understanding motor dysfunctions and support rehabilitation.
Keywords
Machine learning, rehabilitation, neurology, upper limb, impairment, explainable AI, SHAP, novel technology, assessment, computer vision, artificial intelligence
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Master Thesis
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Published since: 2025-12-05 , Earliest start: 2026-01-05
Organization Rehabilitation Engineering Lab
Hosts Domnik Nadine
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Mapping Statistical and Methodological Practices in Non-Invasive Brain Stimulation for Stroke Rehabilitation Research: Contribution to a Scoping Review
Understanding and predicting how and why individuals respond to non-invasive brain stimulation (NIBS) is one of the major challenges in current neurorehabilitation research. Although a growing number of studies propose predictors of treatment response, the statistical approaches used in this field vary widely. This makes it difficult to compare results, assess reliability, or build robust personalized rehabilitation tools. An ongoing project at the RELab aims to systematically map how statistical and methodological practices are used to define, assess, and predict NIBS response across the rehabilitation literature. This forms the foundation of a larger research project on biomarker-guided neurorehabilitation. This student project involves conducting a scoping review of several studies, examining how researchers define “responders,” what statistical models they use, and how prediction frameworks are validated. This internship is ideal for a motivated student interested in neurorehabilitation, data science, and research methodology.
Keywords
non-invasive brain stimulation, statistical methods, predictors, scoping review, personalized medicine, neurorehabilitation, research methodology, bibliographic research
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Semester Project , Internship
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Published since: 2025-12-03 , Earliest start: 2026-01-01 , Latest end: 2026-08-01
Organization Rehabilitation Engineering Lab
Hosts Perrin Cléo
Topics Medical and Health Sciences
Evaluating Engagement and Adherence to Robot-Assisted Rehabilitation: Can Large Language Model-Based Conversational Agents Support Stroke and TBI Patients During Unsupervised Therapy?
Stroke and traumatic brain injury (TBI) are major causes of long-term disability, often requiring intensive rehabilitation to restore motor function, particularly in the hands. While robotic-assisted therapy and mobile health (mHealth) applications offer promising tools for recovery, sustaining patient engagement outside clinical environments remains a significant challenge. This project explores the use of the RehabCoach app and ReHandyBot device in a hybrid rehabilitation program (i.e., two weeks in-clinic followed by six weeks of unsupervised home therapy). The focus is on collecting and analyzing conversational data between patients and the app’s large language model (LLM)-based conversational agent to evaluate how these interactions influence therapy adherence and engagement.
Keywords
Stroke rehabilitation, traumatic brain injury, mobile health, conversational AI, large language models, therapy adherence, unsupervised rehabilitation
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Semester Project , Master Thesis
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Published since: 2025-11-11 , Earliest start: 2025-11-16 , Latest end: 2026-09-30
Organization Rehabilitation Engineering Lab
Hosts Retevoi Alexandra
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology , Behavioural and Cognitive Sciences
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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Semester Project , Internship , Master Thesis
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Published since: 2025-10-08 , 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
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: 2025-09-02 , Earliest start: 2023-05-09
Organization Rehabilitation Engineering Lab
Hosts Viskaitis Paulius , Donegan Dane
Topics Engineering and Technology
Mechanistic Evaluation of taVNS in Motor Adaptation for Stroke Rehabilitation
This study aims to evaluate the mechanisms by which transauricular vagus nerve stimulation (taVNS) may facilitate motor learning and adaptation, focusing on reticulospinal tract (RST) activation. A set of assessments will be used to build a comprehensive neurophysiological profile, providing insights relevant to developing taVNS-based clinical interventions. In this role, you will engage in study design, data collection, and analysis, gaining hands-on experience in mechanistic neurophysiological research with direct clinical applications.
Keywords
Transauricular vagus nerve stimulation (taVNS) Reticulospinal tract (RST) Motor learning StartReact paradigm Stroke rehabilitation Neurophysiology Multisite EMG Motor pathway activation Clinical neurostimulation
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2025-08-04 , Earliest start: 2024-11-24 , Latest end: 2025-10-26
Organization Rehabilitation Engineering Lab
Hosts Viskaitis Paulius
Topics Medical and Health Sciences , Engineering and Technology
Studies on Mechatronics
If you would like to carry out a Studies on Mechatronics project in our group, please contact the assistant working on, or closest to, the research topic you are interested in.
We currently have an open studies on mechatronics project aiming to evaluate and compare mechatronic designs for sensory/mechanical stimulation and massage of the feet. If interested, please contact .
Your Own Project Ideas?
It is always possible to find a project for motivated students with innovative ideas in the fields of rehabilitation, assistive and healthcare technology as well as haptics and motor learning. Please contact us if you would like to pursue a project which is not on the list above.