Assessing Motor Performance with PCA
- Lookup NU author(s)
- Nils Hammerla
- Dr Thomas Ploetz
- Dr Peter Andras
- Professor Patrick Olivier
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| Author(s) | | Hammerla N, Ploetz T, Andras P, Olivier P |
| Editor(s) | | |
| Publication type | | Conference Proceedings (inc. Abstract) |
| Conference Name | | International Workshop on Frontiers in Activity Recognition using Pervasive Sensing (in conjunction with Pervasive) |
| Conference Location | | San Francisco, California, USA |
| Year of Conference | | 2011 |
| Date | | 12-15 June 2011 |
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| Pages | | |
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| Full text for this publication is not currently held within this repository. Alternative links are provided below where available. |
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| 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 as- sessment represents a significant challenge, as requirements depend on the specific application. We develop an approach to quantify one aspect that many domains share – the ef- ficiency 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, nor- malised 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. |
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