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.

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

Robotics in barrier-free industrial kitchens

The project shall enable all people – regardless of their physical or cognitive abilities – to work in the kitchen

Keywords

Robotics, Inclusion, kitchen work

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Semester Project , Master Thesis

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Published since: 2024-10-02 , Earliest start: 2024-11-04 , Latest end: 2025-12-31

Applications limited to Institute for Biomedical Engineering , Department of Health Sciences and Technology , Institute of Robotics and Intelligent Systems D-MAVT

Organization Sensory-Motor Systems Lab

Hosts Wolf Peter

Topics Medical and Health Sciences , Engineering and Technology

Visceral organ dynamics and locomotion

The project aims to describe vibration of internal organs during jogging and what benefits these vibrations may have for human health.

Keywords

Organ movement, performance optimization, prevention of cardiovascular diseases, mathematical modelling, simulation, motion measurement, multi-body systems, dynamics

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Published since: 2024-10-02 , Earliest start: 2024-11-04 , Latest end: 2025-12-31

Applications limited to ETH Zurich , Hochschulmedizin Zürich , University of Zurich

Organization Sensory-Motor Systems Lab

Hosts Wolf Peter

Topics Engineering and Technology

Causal Machine Learning and Data Fusion with Experts in the Loop for Spinal Cord Injury (SCI)

Causl Discovery aims to find causal relations from data, being increasingly important in various fields such as health science. Despite the growing amount of work on applying causal discovery methods 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 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. Besides, fusing datasets from different sources is essential for holistic discovery and reasoning. This project will also focus on developing methods of machine learning and data fusion over distinct contexts under the scope of SCI. Based on the qualifications of the candidates, we can arrange a subsidy/allowance to cover traveling or living costs.

Keywords

Causal Discovery, Expert Knowledge, Iterative Algorithm, Data Fusion, Spinal Cord Injury

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Semester Project , Internship , Master Thesis

Project Background

Causal graphical models (CGM) can be determined via randomized controlled experiments (and this is the golden standard to determine the causal relations and quantify the causal effects), learned purely from data[1](i.e., structural learning or causal discovery) or elicited by domain experts based on their knowledge and experiences. It is also feasible to learn the CGM from data with prior expert knowledge when data become not reliable due to insufficient sample size or measurement errors. Early works in causal discovery utilized constraint-based approaches, such as the PC algorithm and the FCI algorithm. These methods exploit conditional independence relationships among variables to infer causal structures. They can restrict the output graph space through conditional independence assumptions or background knowledge about causal relationships [2]. Score-based approaches, such as the GES (Greedy Equivalence Search) algorithm, aim to find the best causal structure that optimizes a scoring criterion, e.g., the BIC (Bayesian Information Criterion) score. These methods often incorporate expert knowledge through prior specifications or domain-specific constraints on the model parameters [3, 4, 5]. Recent advancements in causal discovery involve hybrid approaches that combine elements of constraint-based and score-based methods. These approaches aim to leverage the strengths of both approaches to improve the accuracy and efficiency of causal structure learning. They may incorporate expert knowledge through the integration of domain-specific constraints and scoring functions. In addition to observational data, causal discovery methods have also been developed to handle experimental data, where interventions or randomized experiments are conducted. These methods utilize the causal effects of interventions to infer causal relationships. Expert knowledge can be incorporated by designing well-controlled experiments or interventions based on domain expertise. Despite the growing amount of work on structural learning with expert knowledge in scientific fields, few of them inspect the uncertainty of expert knowledge (what if the expert goes wrong?). This is highly important since in medical applications, causal discovery with expert knowledge should be taken cautiously and sometimes experts can give false knowledge, leading to serious mistakes. An approach considering the expert uncertainty during CGM construction will be more robust to potential bias induced by individuals. This idea is made possible by recent progress toward evaluating and falsifying a given CGM from observational data without ground truth[6]. Therefore, we hope to develop an iterative causal discovery method with experts in the loop for continual interaction and calibration between experts and data. In the meantime, we are also trying to develop an interactive user interface for medical experts to input their knowledge and edit the learned graph. References [1] Glymour, C., Zhang, K., & Spirtes, P. (2019). Review of causal discovery methods based on graphical models. Frontiers in genetics, 10, 524. [2] Flores, M. J., Nicholson, A. E., Brunskill, A., Korb, K. B., & Mascaro, S. (2011). Incorporating expert knowledge when learning Bayesian network structure: a medical case study. Artificial intelligence in medicine, 53(3), 181-204. [3] O’Donnell, R. T., Nicholson, A. E., Han, B., Korb, K. B., Alam, M. J., & Hope, L. R. (2006). Causal discovery with prior information. In AI 2006: Advances in Artificial Intelligence: 19th Australian Joint Conference on Artificial Intelligence, Hobart, Australia, December 4-8, 2006. Proceedings 19 (pp. 1162-1167). Springer Berlin Heidelberg. [4] Perković, E., Kalisch, M., & Maathuis, M. H. (2017). Interpreting and using CPDAGs with background knowledge. arXiv preprint arXiv:1707.02171. [5] Kleinegesse, S., Lawrence, A. R., & Chockler, H. (2022). Domain Knowledge in A*-Based Causal Discovery. arXiv preprint arXiv:2208.08247. [6] Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. arXiv preprint arXiv:2305.09565.

Your Task

1. Literature Review on Causal Discovery: You will study classic and state-of-the-art causal discovery methods (preferably on real datasets with mixed-type variables) that incorporate expert knowledge, leading to a systematic review of causal discovery methods in different categories (constraint-based vs. score-based) with different assumptions (e.g., latent variables, causal mechanism/function forms, and cycles) for different settings (e.g., static data or time series). 2. Data Exploration and Analysis: You will explore analyze and datasets collected for HAPI (Hospital-Acquired Pressure Injury) and Hospital Stay after PI Surgeries. The data record hundreds of patients into dozens of mixed-type variables including patients’ demographic features, lab testing values, and health conditions observed during their stay. Apart from clinical data, you will also have access to multivariate time series extracted from bio-signals on SCI people under intervention experiments. We aim to learn the causal relations among these variables of interest from the above data with experts’ knowledge. Additionally, we will probe some public datasets of other fields such as heart diseases and earth science with ground-truth graphs. 3. Methodology Development and Deployment: You will help develop and validate an iterative algorithm for causal discovery with the expert in the loop against benchmarks using the simulated and real datasets. There are two potential exploring directions: 1. How will the correct expert knowledge help reduce the search space of graphs to improve the accuracy and efficiency of the algorithm? 2. How to encode prior knowledge or design a post-modification strategy with uncertainty from experts’ input to increase the robustness of the algorithm? Based on the methodology, an interactive web/application interface for doctors to input their knowledge and edit the learned graph is expected. If possible, you will assist in deriving the mathematical guarantee for the iterative algorithm in terms of convergence and stability properties. 4. Presentation and Documentation: You will prepare a high-quality manuscript for publication with a clear and engaging presentation of the results and methodology.

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Published since: 2024-09-20 , Earliest start: 2024-10-01 , Latest end: 2025-02-28

Organization Sensory-Motor Systems Lab

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

Topics Medical and Health Sciences , Mathematical Sciences , Information, Computing and Communication Sciences

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)

Description

Graphical modeling (GM) was originally proposed as a statistical modeling framework that uses probabilistic graphs to encode conditional dependences and independence among variables of interest [1]. It is straightforward for stakeholders to understand the interaction among different factors and reasons using the structure and relations. Due to the strong ability of GM to represent complex probability distributions, it has been developed and adapted to various fields including earth science [2], biology [3], and healthcare [4] to dissect the structure and sparsity of targeted phenomenon to improve the understanding of mechanism and decision-making process. In digital healthcare and medicine with a high-stake nature, it is imperative to enable a transparent and accessible modeling paradigm such that both modeling technicians and medical experts can comprehend, trust, and improve the digital models for decision-making. Given the transparent structural representation of GM, we aim to develop an explainable Artificial intelligence (xAI) framework based on CGM (Causal Graphical Models, a special type of GM where edges represent causal relations) to cope with the challenges of real-world medical data and improve both the capacity and capability of medical AI. As a validation scenario of our approach, 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 such as pulmonary infections, pressure injuries (PI), autonomic dysreflexia (AD), and cardiovascular dysregulation are highly prevalent among the SCI population, imposing a tremendous risk on the patient’s health and the healthcare system [5]. Through the medical records and multimodal-sensory monitoring system, we expect to end up with the digital twins of SCI individuals that can fuse different data sources, interact with clinical operations and expertise, and guide medical treatment. Your Tasks: 1. Literature Review on Digital Twins and Graphical Models: You will study literature related to graphical models with applications, digital twins with applications, and preferably the combination of both fields (GMDT). 2. Data Exploration and Analysis: You will explore multimodal datasets collected for PI and AD. The PI data record the conditions of hundreds of SCI individuals during their hospital stay. There are mixed-type variables including demographic features, lab testing values, and health conditions observed during their stay. Additionally, you will have access to multivariate time series extracted from bio-signals on SCI people under intervention experiments. 3. Methodology Development and Deployment: You will help define what data to use, design the experiments, develop, and validate both the modeling foundation and implementation pipeline for the GMDT. A graphical user interface is expected to be built based on the current framework we are developing in the lab. 4. Presentation and Documentation: You will prepare a high-quality manuscript for publication with a clear and engaging presentation of the methodology and validation results. References: [1] Glymour, C., Zhang, K. & Spirtes, P. Review of Causal Discovery Methods Based on Graphical Models. Front. Genet. 10, 524 (2019). [2] Runge, J. et al. Inferring causation from time series in Earth system sciences. Nat. Commun. 10, 2553 (2019). [3] Friedman, N. Inferring Cellular Networks Using Probabilistic Graphical Models. Science 303, 799–805 (2004). [4] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nat. Commun. 11, 3923 (2020). [5] Haisma, J. et al. Complications following spinal cord injury: occurrence and risk factors in a longitudinal study during and after inpatient rehabilitation. J. Rehabil. Med. 39 5, 393–8 (2007).

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Published since: 2024-09-20 , Earliest start: 2024-10-01 , Latest end: 2025-02-28

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

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

Description

**Problem Definition** 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. The 12 lead ECG scans are usually noisy and contain a red or black grid depending on the photo format (B / BW). Some ECG images were taken by a camera while the sheet was held in the hand. For these images, the paper is often tilted and the image contains shadow artifacts or wrinkles. Furthermore, the 12 lead signals were not printed in a standardized format. Often, the signals are printed in a 3x4 matrix format onto paper with the lead identifier letters written next to the start loca-tion of the signal. Sometimes, all leads are printed in full length above each other, however. A robust solution for these prob-lems must be developed and a preliminary draft of the pipeline steps is explained in the ‘Your Tasks’ section. **Your Tasks** 1. Conduct extensive literature research on State-of-the-Art ECG digitization systems. 2. Develop an automatic image rotation algorithm for horizontal alignment. 3. Code a network for grid removal. In this step, the grid size should be extracted in pixels for later ECG signal rescaling purposes. 4. Propose a solution for denoising the images after grid-removal. The remaining image should only consist of the lead signals and the lead identifier letters. 5. Apply a pretrained character recognition CV algorithm for character recognition. 6. Detect the relevant image regions for each individual ECG lead signal. 7. Develop an algorithm for conversion of the signal into physical units and potential subsequent post-processing steps. 8. Test the performance of the digitization compared to manually extracted features that have been recorded in the hospital. 9. Digitize the whole dataset including images of more than 8000 patients recorded at 30+ hospital sites across Eu-rope. 10. Make the tool accessible online / via app. (optional) **Your Profile** - Strong experience with Python - Strong background in Computer Vision (preferred) - Background in Signal Processing and Filtering (preferred) - Structured and reliable working style - Ability to work independently on a challenging problem

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Published since: 2024-09-15 , Earliest start: 2024-10-01 , Latest end: 2025-05-31

Organization Spinal Cord Injury & Artificial Intelligence Lab

Hosts Paez Diego, Dr.

Topics Medical and Health Sciences , Information, Computing and Communication Sciences , 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: 2024-09-03 , Earliest start: 2024-09-16 , Latest end: 2025-05-31

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

Master Thesis/ Internship: Robust Classification of the Activities of Daily Living using Unobtrusive Sensors and Transfer Learning

This study examines transfer learning to enhance activity recognition in SCI patients using wearable sensors and existing datasets.

Keywords

Activities of Daily Living, Spinal Cord Injury, Machine Learning, Classification, Deep Learning, Transfer Learning

Labels

Internship , Master Thesis

Description

**Background** Spinal cord injury (SCI) is a severe medical condition that affects millions of individuals worldwide. These patients often suffer from additional health problems, including reduced mobility and difficulties with performing activities of daily living (ADLs). To effectively monitor their progress, a reliable and accurate method for tracking ADLs is necessary. One promising approach is the use of wearable sensors, such as inertial measurement unit (IMU) sensors and pressure mats. These sensors provide a wealth of data on an individual’s movements, allowing for a better understanding of their daily activities [1]. However, intelligently classifying these activities based on the collected data remains a challenge. One established approach to addressing this challenge is using deep learning algorithms for fusing data from different sources. However, these algorithms depend on large datasets, which may be challenging to gather for SCI patients. Transfer learning may provide a solution by utilizing datasets from similar setups to enhance classification performance [2]. **Your Tasks** - Conduct a comprehensive review of the existing activity recognition literature and identify studies employing state-of-the-art network architectures. Particular emphasis should be placed on studies that provide open-source access to trained networks or datasets. - Develop classification models utilizing state-of-the-art network architectures and fuse multi-modal data that has been exclusively acquired for this project. - Utilize transfer learning techniques to retrain pre-existing state-of-the-art human activity classifiers using the project’s dataset. - Develop robust multi-modal feature learning layers for human activity recognition by investigating the transferability of the layers across different applications within the domain. - Write a report highlighting the findings of the project. - Preparation of a manuscript showcasing the project's findings with the intention of submitting it to a prominent and relevant academic journal (optional). **Your Benefits** - Gain unique access and first-hand experience in one of the leading institutions on long-term health management - At the Swiss Paraplegic Centre at Nottwil. - Working with the potential of long-term applications from the results of this thesis work. **Your Profile** - Proven understanding of multi-modal machine learning methods such as deep learning data fusion. - A fundamental understanding of the concept of transfer learning and domain transfer. - Strong experience with Python (preferred). - Structured and reliable working style. - Ability to work independently on a challenging topic. **[References]** [1] Popp, W. L., Schneider, S., Bär, J., Bösch, P., Spengler, C. M., Gassert, R., & Curt, A. (2019). Wearable Sensors in Ambulatory Individuals With a Spinal Cord Injury: From Energy Expenditure Estimation to Activity Recommendations. In Frontiers in Neurology (Vol. 10). Frontiers Media SA. https://doi.org/10.3389/fneur.2019.01092 [2] Pavliuk, O., Mishchuk, M., & Strauss, C. (2023). Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform. In Algorithms (Vol. 16, Issue 2, p. 77). MDPI AG. https://doi.org/10.3390/a16020077

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Published since: 2024-08-26 , Earliest start: 2024-09-23 , Latest end: 2025-05-31

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 , ASPIRE League , Chalmers University of Technology , Champalimaud Foundation , CNRS - Centre national de la recherche scientifique , European Molecular Biology Laboratory , IDEA League , IEE S.A. Luxembourg , Istituto Italiano di Tecnologia , Politecnico di Milano , Research Internships at HU Berlin , Swansea University , 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 , The University of Edinburgh , University of Copenhagen , 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

M.Sc. Thesis: Automating Time Series Signal Analysis through Semi-Supervised Feature Extraction

This thesis develops a feature engineering toolbox for classifying ADLs in spinal cord injury patients using wearable sensor data.

Keywords

Activities of Daily Living, Spinal Cord Injury, Machine Learning, Classification, Signal Processing, Feature Engineering

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Semester Project , Internship , Master Thesis

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Published since: 2024-08-26 , Earliest start: 2024-09-23 , Latest end: 2025-04-30

Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , Forschungsinstitut für biologischen Landbau (FiBL) , Hochschulmedizin Zürich , IBM Research Zurich Lab , Institute for Research in Biomedicine , Lucerne University of Applied Sciences and Arts , NCCR Democracy , NGOs Switzerland , Zurich University of the Arts , Zurich University of Applied Sciences , University of Lucerne , University of Zurich , Wyss Translational Center Zurich , Swiss Institute of Bioinformatics , Swiss National Science Foundation , Sirm Institute for Regenerative Medicine , Eawag , Empa , Friedrich Miescher Institute , Swiss Federal Institute for Forest, Snow and Landscape Research , University of Basel , Università della Svizzera italiana , Université de Neuchâtel , SystemsX.ch , University of Berne , University of Fribourg , University of Geneva , University of Lausanne , University of St. Gallen , Agroscope , Berner Fachhochschule , CERN , Corporates Switzerland , CSEM - Centre Suisse d'Electronique et Microtechnique , Department of Quantitative Biomedicine , Fernfachhochschule , Pädagogische Hochschule St.Gallen , Paul Scherrer Institute , Physikalisch-Meteorologisches Observatorium Davos , 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 , ASPIRE League , Chalmers University of Technology , Champalimaud Foundation , CNRS - Centre national de la recherche scientifique , IDEA League , IEE S.A. Luxembourg , Istituto Italiano di Tecnologia , Max Planck ETH Center for Learning Systems , Technical University of Denmark , Politecnico di Milano , Technion - Israel Institute of Technology , The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI) , The University of Edinburgh , Université de Strasbourg , Universiteit Stellenbosch , University College Dublin , University of Southern Denmark , Vienna Biocenter - Scientific Training

Organization Spinal Cord Injury & Artificial Intelligence Lab

Hosts Paez Diego, Dr.

Topics Information, Computing and Communication Sciences , 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.