Master’s Thesis – Spatiotemporal Gait Parameters from Wrist-Worn Accelerometers
Masterthesis / Biomedical Engineering / AI in Medicine
Status: Open
Time frame: start date: October 2025 or upon agreement.
Master’s Thesis – Spatiotemporal Gait Parameters from Wrist-Worn Accelerometers
Project background
Gait is an important biomarker for neurodegenerative diseases (e.g., Parkinson’s disease, Alzheimer’s disease). Since most consumer and research wearables contain accelerometers and large datasets are available, developing robust methods that work across settngs (indoor/outdoor) could enable scalable gait monitoring. Walking indoors and outdoors represents different neurological aspects. Outdoor walking, typically involving longer and more continuous strides, reflects core gait mechanics, whereas indoor walking often includes dual- or even triple-task elements, requiring complex neurological networks to work in tandem. Deep learning (DL) models for predicting spatiotemporal gait parameters are a relatively new development (Brand et al., 2024, 2025; Yuan et al., 2024). Many questions, such as model performance in different settings (e.g., indoor vs. outdoor), incorporating a biomechanical model, or including demographic information to improve prediction, remain unanswered.
Aim
This thesis aims to develop and validate methods for predicting spatiotemporal gait parameters from wrist accelerometers by combining classical signal processing with DL models, trained with foundation models. The study will investigate whether these daily-life trained models generalize to indoor and outdoor settngs, and whether performance can be improved by training separate models for each condition. The influence ofdemographic variables (sex, height, weight) on prediction accuracy will also be assessed.
Materials and Methods
The project will draw on existing large wrist accelerometer datasets, complemented by a small custom dataset collected indoors and outdoors for validation. After preprocessing and feature extraction, hybrid models will be trained using both signal-based features and AI-based time-series representations. Comparisons will be made between general and condition-specific models, with and without demographic variables, and evaluated against reference gait measures using standard performance metrics.
Nature of the Thesis
Method development (signal processing algorithms, machine learning models): 60%
Data collection and analysis (acquisition, preprocessing, evaluation): 40%
Requirements
Strong programming skills in Python
Experience with Numpy, Pandas/Polars, PyTorch
Familiarity with wearable sensor data and time-series analysis is advantageous
Basic knowledge of statistics.
Institutes
Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern
Supervisors
Dr. Michael Single (michael.single@unibe.ch)
Aaron Colombo (aaron.colombo@unibe.ch)
Workplace
ARTORG Center (SITEM, Freiburgstrasse 3) on the Insel hospital campus.
Language: English
Contact: Aaron Colombo
Application: apply to aaron.colombo@unibe.ch with the subject:
CV
Course transcripts
A short motivation letter
References:
Brand, Y. E., Buchman, A. S., Kluge, F., Palmerini, L., Becker, C., Cerea;, A., Maetzler, W., Vereijken, B., Yarnall, A. J., Rochester, L., Din, S. D., Mueller, A., Hausdorff, J. M., & Perlman, O. (2025). Continuous Assessment of Daily-Living Gait Using Self-Supervised Learning of Wrist-Worn Accelerometer Data (p. 2025.05.21.25328061). medRxiv. https://doi.org/10.1101/2025.05.21.25328061
Brand, Y. E., Kluge, F., Palmerini, L., Paraschiv-Ionescu, A., Becker, C., Cerea;, A., Maetzler, W., Sharrack, B., Vereijken, B., Yarnall, A. J., Rochester, L., Del Din, S., Muller, A., Buchman, A. S., Hausdorff, J. M., & Perlman, O. (2024). Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults. Scientific Reports, 14(1), 20854. https://doi.org/10.1038/s41598-024-71491-3
Yuan, H., Chan, S., Creagh, A. P., Tong, C., Acquah, A., Clieon, D. A., & Doherty, A. (2024). Self-supervised learning for human activity recognition using 700,000 person-days of wearable data. Npj Digital Medicine, 7(1), 1–10. https://doi.org/10.1038/s41746-024-01062-3