Browse by author
Lookup NU author(s): Nils Hammerla, Dr Thomas Ploetz, Dr Peter Andras, Professor Patrick OlivierORCiD
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
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
