Supervised vs.unsupervised walking extraction – How does the recognition gets effected by sensor displacement?

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Bachelor Thesis

Wearable sensor technology, particularly inertial measurement sensors are used in various motion analysis including applications in sport, medicine, and rehabilitation of patients after stroke. Wearability and sensor placement are related and influence measurements, particularly sensor offsets are critical for correct pattern recognition and subsequent motion data analysis.
In this project we aim to investigate the effect of misaligned sensors on the recognition of walking in free-living in patients after stroke. In particular we aim to analyse different supervised and unsupervised machine learning techniques to evaluate differences in recognition accuracy. We will simulate orientation offsets, implement and evaluate different algorithms including a rule-based unsupervised approach, and supervised approaches, e.g. one-class-classifier.


Project type Bachelor Thesis
Work distribution 20% Theory, 70% Programming and Data Analysis, 10% Experiment
Requirements Signal processing, Python / MATLAB, Machine learning
Starting date Summer term 2018

Adrian Derungs

Friedrich-Alexander-Universität Erlangen-Nürnberg