Nils Y Hammerla
Assessing motor performance with PCA
Hammerla, Nils Y; Pl�tz, Thomas; Andras, Peter; Olivier, Patrick
Authors
Thomas Pl�tz
Prof Peter Andras P.Andras@napier.ac.uk
Dean of School of Computing Engineering and the Built Environment
Patrick Olivier
Abstract
Information about the motor performance, i.e. how well an activity is performed, is valuable information for a variety of novel applications in Activity Recognition (AR). Its assessment represents a significant challenge, as requirements depend on the specific application. We develop an approach to quantify one aspect that many domains share – the efficiency of motion – that has implications for signals from body-worn or pervasive sensors, as it influences the inherent complexity of the recorded multi-variate time-series. Based on the energy distribution in PCA we infer a single, normalised metric that is intimately linked to signal complexity and allows comparison of (subject-specific) time-series. We evaluate the approach on artificially distorted signals and apply it to a simple kitchen task to show its applicability to real-life data streams.
Citation
Hammerla, N. Y., Plötz, T., Andras, P., & Olivier, P. (2011). Assessing motor performance with PCA. In Proceedings of the International Workshop on Frontiers in Activity Recognition using Pervasive Sensing (18-23)
Presentation Conference Type | Conference Paper (Published) |
---|---|
Conference Name | International Workshop on Frontiers in Activity Recognition using Pervasive Sensing (in conjunction with Pervasive) |
Start Date | Jun 12, 2011 |
Publication Date | 2011 |
Deposit Date | Nov 17, 2021 |
Pages | 18-23 |
Book Title | Proceedings of the International Workshop on Frontiers in Activity Recognition using Pervasive Sensing |
Public URL | http://researchrepository.napier.ac.uk/Output/2809152 |
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