Student Projects

Currently, the following student projects are available. Please contact the responsible supervisor and apply with your CV and transcripts.

If you are looking for a Master internship HST/BWS or 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.

VR-based Motor-cognitive rehabilitation with multi-modal sensing

This thesis aims to design a pool of virtual-reality based rehabilitation tasks for simultaneous motor-cognitive rehabilitation for stroke patients

Keywords

virtual reality, sEMG, depth sensing camera, rehabilitation,

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Semester Project , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-01-02 , Earliest start: 2025-03-01 , Latest end: 2025-12-31

Organization Sensory-Motor Systems Lab

Hosts Dash Adyasha

Topics Engineering and Technology

BA/Semester/MA thesis: Digital training diary for climbing

Using a fixed video camera to automatically document the number and type of holds used.

Keywords

performance analysis, pose detection

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2024-12-26 , Earliest start: 2025-01-15 , Latest end: 2025-09-30

Applications limited to ETH Zurich

Organization Sensory-Motor Systems Lab

Hosts Wolf Peter

Topics Medical and Health Sciences , Engineering and Technology

Maximum flexion: a problem for markerless tracking?

This internship/research project/semester thesis elaborates why in various of our data collections markerless tracking in maximum flexion became very inaccurate.

Keywords

kinematic analysis, pose detection, robustness

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Semester Project , Internship , Bachelor Thesis

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Published since: 2024-12-22 , Earliest start: 2025-02-18 , Latest end: 2025-10-31

Applications limited to ETH Zurich

Organization Sensory-Motor Systems Lab

Hosts Wolf Peter

Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology

Maximal finger strength: How to assess it?

Various methods have been applied, each probably influenced by different constraints. This internship/master thesis aims to establish a robust standard method.

Keywords

climbing, assessment

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Internship , Master Thesis

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Published since: 2024-12-22 , Earliest start: 2025-01-15 , Latest end: 2025-09-15

Applications limited to ETH Zurich

Organization Sensory-Motor Systems Lab

Hosts Wolf Peter

Topics Medical and Health Sciences , Engineering and Technology

Master Thesis Project – Optical-based flow scanning and calibration for in situ printing

Join us for an exciting project towards the next level in intraoperative robotic adaptability!

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Master Thesis

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Published since: 2024-12-18 , Earliest start: 2025-01-01 , Latest end: 2025-12-31

Organization Sensory-Motor Systems Lab

Hosts Sommerhalder Michael

Topics Medical and Health Sciences , Engineering and Technology

Safe RL for Robot Social Navigation

Developing a constrained RL framework for social navigation, emphasizing explicit safety constraints to reduce reliance on reward tuning.

Keywords

Navigation, Robot Planning, Reinforcement Learning, RL, Social Navigation

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Master Thesis

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Published since: 2024-12-13 , Earliest start: 2025-01-01 , Latest end: 2025-12-31

Organization Spinal Cord Injury & Artificial Intelligence Lab

Hosts Alyassi Rashid , Alyassi Rashid , Alyassi Rashid

Topics Engineering and Technology

Bridging RL-based Robot Navigation & Crowd Simulation

Designing a crowd simulator for realistic human-robot interactions, enabling RL agent training in social navigation tasks.

Keywords

Navigation, Robot Planning, Reinforcement Learning, RL, Social Navigation.

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Master Thesis

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Published since: 2024-12-12 , Earliest start: 2025-01-01 , Latest end: 2025-12-01

Organization Spinal Cord Injury & Artificial Intelligence Lab

Hosts Alyassi Rashid , Alyassi Rashid , Alyassi Rashid

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)

Description

Graphical modeling (GM) refers to a statistical modeling framework that uses probabilistic graphs to encode conditional dependencies and independences among variables of interest [1]. Due to its ability to represent complex probability distributions, GM has been developed and adapted to various fields including earth science [2], biology [3], health [4], and medicine [5] to dissect the structure of targeted phenomena to improve the understanding of the mechanism and decision-making process. Multivariate Time series (MTS), characterized by sequential observations of several variables over time, is pervasive in various disciplines. Understanding variables' temporal evolution and spatial dependencies is essential for uncovering patterns, trends, and anomalies that unfold over time. Time series analysis provides insights into the underlying dynamics of phenomena, enabling researchers and practitioners to make informed predictions, learn temporal dependencies, and formulate effective strategies. The exploration of temporal graphical models (TGMs) in the realm of time series analysis is paramount, given the crucial role that time series data plays in various fields. In this context, TGMs emerge as a sophisticated approach to modeling time series data, capturing the dynamic and evolving relationships among variables. As a validation scenario, we are working with the Swiss Paraplegic Centre (SPZ) to improve the long-term prognosis of individuals with spinal cord injury (SCI). Secondary health conditions (SHC) such as pulmonary infections, pressure injuries (PI), AD, and cardiovascular dysregulation are highly prevalent among the SCI population, imposing a tremendous risk on the patient’s health and the healthcare system [6,7]. Through the medical records and multimodal-sensory monitoring system, we expect to develop an intelligent system that can fuse different data sources, interact with clinical operation and expertise, and guide the medical treatment and decision-making for SHC management of SCI individuals. This project will focus on the TGM of a multivariate time series extracted from a wearable multi-modal sensing system. We aim to learn the high-resolution temporal dynamics together with spatial dependencies among biometrics, based on which the early detection and inference of AD episodes could be enabled. For more details about the task description, please refer to the attached document!

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Published since: 2024-12-11 , Earliest start: 2025-05-01 , Latest end: 2025-10-31

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

Organ-on-Chip Automation Hardware Design: Development and Integration of a Miniature Robotic XY Stage for a Miniature Microscope

Organs-on-Chip (OoC) replicate human organs in vitro but often lack physiological perfusion profiles. This project aims to develop a compact robotic XY stage for integration with OoC perfusion systems, enhancing automation and precision. By improving compatibility and mimicking dynamic blood flow, this innovation advances research and pharmaceutical applications.

Keywords

Organ; Chip; Automation; Hardware; Design; Robot

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Master Thesis

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Published since: 2024-12-02 , Earliest start: 2025-01-01 , Latest end: 2025-12-31

Organization Sensory-Motor Systems Lab

Hosts Sommerhalder Michael

Topics Engineering and Technology

Instruction Space Characterization for Interactive Human-Robot Domains

Human intention drives goal-directed actions, requiring decomposition into hierarchical instructions, from abstract strategies to concrete interactions. Robotics seeks to automate these instructions, but challenges arise in domains like neurorehabilitation, where complex human-robot interactions introduce inefficiencies from incomplete automation, diverse user preferences for strategies, and difficulties to engage with technical parameters. This thesis will explore shared instruction space characteristics across robotic domains, focusing on bottlenecks in automating high-level instructions. Novel metrics will be developed that define and classify instruction layers, strategies, and requirements.

Keywords

Human-robot interaction; intention automation; instruction space; metrics; literature research

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Bachelor Thesis

Project Background

Human intention describes our will to achieve a specific goal that is directed towards other people, our environment, or ourselves. To achieve a specific goal, humans break this intention down into a series of high to low level instructions until a level of abstraction and a time domain are reached where an instruction can initiate a physical interaction. Humans have always endeavoured to develop technologies that make it easier for them to realise their intentions. The emphasis is now on automating instructions that are implemented in robots - and how this is to be realised on higher, abstract instruction layers is the aim of the application. In application domains where the overarching intention involves another human, the number of possible instructions per abstraction layer increases due to the versatility of human-robot interaction, and the number of equivalent strategies increases due to the diversity of the user's personal preferences. This complexity poses three challenges: (i) Time efficiency through robots is hardly given when the ratio between automated and non-automated instructions is low. Automating missing instructions can be cost-inefficient if they are specific to a unique context. (ii) Users having different preferences in selecting strategies are not addressed when the amount of implemented strategies is small compared to the total amount of possible equivalent strategies. This limited perceived usefulness has been identified for rehabilitation robotics in particular and remains unresolved to this day. (iii) Users do not want to set many (sometimes very technical) parameters to implement their preferences, which specifically leads, for example, to therapists being sceptical about the benefit of robotics. By establishing theoretical foundations and meta-analysing instruction spaces of various robotics domains, requirements and guidelines regarding the control architecture, robustness, controllability, operability and safety of robot networks capable of learning will be created.

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Published since: 2024-12-02 , Earliest start: 2025-01-01 , Latest end: 2025-12-31

Applications limited to University of Zurich , ETH Zurich , University of Basel , EPFL - Ecole Polytechnique Fédérale de Lausanne

Organization Sensory-Motor Systems Lab

Hosts Sommerhalder Michael

Topics Information, Computing and Communication Sciences

Master Thesis / Project - SENSEI: Sensor Teaching in Multi-Activity classification from Video and Wearables for Wheelchair Users

In this project, we focus on continuous and quantitative monitoring of activities of daily living (ADL) in SCI individuals with the goal of identifying cardiovascular events and PI-related risk behaviors. ADLs specific to SCI patients and their lifestyles shall be discussed and narrowed down in the scope of this work, therefore an autonomous camera-based system is proposed to classify ADLs. The Current work builds on a previous project where a SlowFast network [1] was trained to identify SCI-specific classes and we aim to further improve the classification and temporal resolution for transferring to wearables' time-series data.

Keywords

Computer vision, activity classification, video processing, Deep Learning, ADL, soft-labelling, probabilistic networks

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Semester Project , Course Project , Internship , Bachelor Thesis , Master Thesis , ETH for Development (ETH4D) (ETHZ) , ETH Zurich (ETHZ)

Project Background

In this project, we will develop an ADL monitoring system for smart wheelchairs using wearable sensors (i.e., camera, inertial measurement units). The core idea is to classify ADL by leveraging different sensing and feature computation modalities to extract relevant and complementary context bits. For example, the presence of and the interaction with distinctive objects within the camera scene provides strong cue about the ongoing user activity [1, 2, 3]. From another perspective, inertial sensors record distinct orientation and acceleration signatures of ADL-related temporal patterns [4]. Moreover, previous literature provides strong evidence that the combination of video cameras and inertial sensors improves the recognition performance compared to one or the other [5, 6]. Based on the general multi-modal activity recognition paradigm, we are seeking a novel solution tailored to the smart wheelchair. The outcome of the study will be key to analyse the expected cardiovascular function at different moments of the day in free living. 1. Bensland, S., Paul, A., Grossmann, L., Eriks-Hogland, I., Riener, R., & Paez-Granados, D. (2023). Healthcare Monitoring for SCI individuals: Learning Activities of Daily Living through a SlowFast Network. IEEE International Conference on System Integration. 2. M. Wang, C. Luo, B. Ni, J. Yuan, J. Wang and S. Yan, "First-Person Daily Activity Recognition With Manipulated Object Proposals and Non-Linear Feature Fusion," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 10, pp. 2946-2955, Oct. 2018 3. G. Schiboni, F. Wasner and O. Amft, "A Privacy-Preserving Wearable Camera Setup for Dietary Event Spotting in Free-Living," 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2018, pp. 872-877 4. Lopez-Nava IH, Muñoz-Meléndez A. Human action recognition based on low- and high-level data from wearable inertial sensors. International Journal of Distributed Sensor Networks. 2019. 5. C. Chen, R. Jafari and N. Kehtarnavaz, "UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor," 2015 IEEE International Conference on Image Processing (ICIP), 2015, pp. 168-172 6. S. K. Yadav, K. Tiwari, H. M. Pandey, S. A. Akbar, “A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions”, Knowledge-Based Systems, 2021, Volume 223

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Published since: 2024-11-29 , Earliest start: 2024-01-15 , Latest end: 2024-11-01

Applications limited to EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich , Zurich University of the Arts , Wyss Translational Center Zurich , University of Zurich , Zurich University of Applied Sciences , CERN , CSEM - Centre Suisse d'Electronique et Microtechnique , Department of Quantitative Biomedicine , Lucerne University of Applied Sciences and Arts , Institute for Research in Biomedicine , IBM Research Zurich Lab , 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 , Swiss Institute of Bioinformatics , Swiss National Science Foundation , Swiss Federal Institute for Forest, Snow and Landscape Research , Institute of Robotics and Intelligent Systems D-MAVT , TU Berlin , TU Darmstadt , TU Dresden , RWTH Aachen University , Technische Universität München , Technische Universität Hamburg , Max Planck Society , University of Oxford , University of Leeds , University of Cambridge , UCL - University College London , National Institute for Medical Research , Imperial College London , Royal College of Art , Empa , Università della Svizzera italiana , Hochschulmedizin Zürich , Hong Kong University of Science and Technology , University of Washington , Tokyo Institute of Technology , The University of Tokyo

Organization Sensory-Motor Systems Lab

Hosts Paez Diego, Dr. , Paez Diego, Dr. , Paez Diego, Dr.

Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Behavioural and Cognitive Sciences

Internships (Industrial or Research) on Body Modelling and Sensing Technology for Health Care in SCI

This hands-on work (internship or semester project) within a clinical setting will bring you close to intelligent health management while exploring multiple data systems. You will experience multimodal data of robotics rehabilitation, general clinical practice, and detailed clinical studies applied in classification and dimensionality reduction.

Keywords

Machine learning, time-series, HR, ECG, BP, wearables, nearables, Medical and health science, healthcare, Android studio, App development

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Semester Project , Internship , Lab Practice , Bachelor Thesis , Master Thesis , Other specific labels , ETH Zurich (ETHZ)

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Published since: 2024-11-29 , Earliest start: 2025-02-03 , Latest end: 2025-12-31

Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , Empa , Eawag , Zurich University of the Arts , 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 , Lucerne University of Applied Sciences and Arts , Institute for Research in Biomedicine , IBM Research Zurich Lab , Swiss Institute of Bioinformatics , CSEM - Centre Suisse d'Electronique et Microtechnique , Corporates Switzerland , CERN , Hochschulmedizin Zürich , Université de Neuchâtel , Università della Svizzera italiana , Swiss National Science Foundation , University of Konstanz , University of Hamburg , University of Erlangen-Nuremberg , University of Cologne , Universität zu Lübeck , Universität Ulm , Universität der Bundeswehr München , TU Dresden , TU Darmstadt , TU Berlin , Technische Universität Hamburg , Max Planck Society , Otto Von Guericke Universitat, Magdeburg , RWTH Aachen University , Ludwig Maximilians Universiy Munich , Humboldt-Universität zu Berlin , European Molecular Biology Laboratory (EMBL) , Eberhard Karls Universität Tübingen , Max Delbruck Center for Molecular Medicine (MDC) , Technische Universität München , Imperial College London , National Institute for Medical Research , Royal College of Art , UCL - University College London , University of Aberdeen , University of Cambridge , University of Manchester , University of Nottingham , University of Oxford , University of Leeds , Delft University of Technology , Maastricht Science Programme , Radboud University Nijmegen , Utrecht University

Organization Sensory-Motor Systems Lab

Hosts Paez Diego, Dr. , Paez Diego, Dr. , Paez Diego, Dr.

Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology

Semester Project / Internship: Anomaly Detection for Biosignals Processing for Robots in Healthcare

Modern wearable and nearable sensors allow for continuous 24-hour health monitoring. In clinical settings, such a richness of data is highly desirable to closely monitor the health status of patients, however, often not exploited due to the lack of common interfaces and the overall complexity of integrating the devices into the clinical routine. Moreover, future treatment devices could benefit from interfacing with these sensors through a common interface to perform closed-loop interventions. In this project, you will develop a novel time-series online synchronization and communication agent adaptable to data changes for interfacing wearable, nearable sensors data with robots in an intelligent system. Together with a research team in Japan, this work will be implemented at several clinical settings.

Keywords

Data system architecture, Robot communication, wearable sensors, Healthcare, wearables, rehabilitation, Machine Learning Models,

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Semester Project , Collaboration , Internship , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)

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Published since: 2024-11-29 , Earliest start: 2024-03-01 , Latest end: 2024-12-01

Applications limited to CERN , Berner Fachhochschule , EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich , Empa , Eawag , IBM Research Zurich Lab , Hochschulmedizin Zürich , Friedrich Miescher Institute , Forschungsinstitut für biologischen Landbau (FiBL) , Paul Scherrer Institute , NCCR Democracy , Lucerne University of Applied Sciences and Arts , Institute for Research in Biomedicine , Swiss National Science Foundation , Swiss Institute of Bioinformatics , Swiss Federal Institute for Forest, Snow and Landscape Research , 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 , Zurich University of the Arts , Zurich University of Applied Sciences , Wyss Translational Center Zurich , Institute of Robotics and Intelligent Systems D-MAVT , Corporates Switzerland , Department of Quantitative Biomedicine , Università della Svizzera italiana , NGOs Switzerland , CSEM - Centre Suisse d'Electronique et Microtechnique , TU Dresden , TU Darmstadt , Technische Universität München , Technische Universität Hamburg , RWTH Aachen University , Universität zu Lübeck , TU Berlin , Max Planck Society , University of Oxford , UCL - University College London , Imperial College London , University of Leeds , University of Cambridge , Delft University of Technology , Politecnico di Milano , The University of Tokyo , Tokyo Institute of Technology

Organization Sensory-Motor Systems Lab

Hosts Paez Diego, Dr. , Paez Diego, Dr. , Paez Diego, Dr.

Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology

Kinematic Modeling of a Robotic Arthroscope

This master thesis focuses on the kinematic modeling of a 4-DoF hand-held robotic arthroscope. The main challenge will be to find fast and accurate forward (and backward) kinematics for a non-holonomic robot.

Keywords

Kinematic Modeling; Surgery; Arthroscope; Robot; Laser; Inverse Kinematics

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Master Thesis

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Published since: 2024-11-14 , Earliest start: 2025-01-01 , Latest end: 2025-12-31

Applications limited to ETH Zurich

Organization Sensory-Motor Systems Lab

Hosts Sommerhalder Michael

Topics Engineering and Technology

Your own project ideas?

It is always possible to find a project for motivated students with own ideas in the fields of assistive healthcare technologies , augmented feedback in motor learning, and sports engineering. Please send an email to the indicated contact person of our current research pillars that most closely matches your idea.