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Lookup NU author(s): Nils Hammerla,
Dr Thomas Ploetz,
Dr Peter Andras,
Professor Patrick OlivierORCiD
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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.
Author(s): Hammerla N, Ploetz T, Andras P, Olivier P
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: International Workshop on Frontiers in Activity Recognition using Pervasive Sensing (in conjunction with Pervasive)
Year of Conference: 2011