Publication: Feature set optimization for physical activity recognition using genetic algorithms
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2015-07
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Association For Computing Machinery (ACM)
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Abstract
Physical activity is recognized as one of the key factors for a healthy life due to its beneficial effects. The range of physical activities is very broad, and not all of them require the same effort to be performed nor have the same effects on health. For this reason, automatically recognizing the physical activity performed by a user (or patient) turns out to be an interesting research field, mainly because of two reasons: (1) it increases personal awareness about the activity being performed and its consequences on health, allowing to receive proper credit (e.g. social recognition) for the effort; and (2) it allows doctors to perform continuous remote patient monitoring. This paper proposes a new approach for improving activity recognition by describing an activity recognition chain (ARC) that is optimized by means of genetic algorithms.This optimization process determines the most suitable and informative set of features that turns out into higher recognition accuracy while reducing the total number of sensors required to track the user activity. These improvements can be translated into lower costs in hardware and less intrusive devices for the patients. In this work, for the assessment of the proposed approach versus other techniques and for replication purposes, a publicly available dataset on physical activity (PAMAP2) has been used. Experiments are designed and conducted to evaluate the proposed ARC by using leave-one-subject-out cross validation and results are encouraging, reaching an average classification accuracy of about 94%.
Description
Proceeding of: Genetic and Evolutionary Computation Conference (GECCO 2015)
Keywords
Physical activity, Activity recognition, Activity recognition chain, Genetic algorithms, Feature selection, Classification
Bibliographic citation
GECCO Companion '15: Proceedings of the companion publication of the 2015 Annual Conference on Genetic and Evolutionary Computation. ACM . Pp. 1311-1318