Preprint Article Version 1 This version is not peer-reviewed

A Comprehensive Study of Activity Recognition Using Accelerometers

Version 1 : Received: 16 March 2018 / Approved: 19 March 2018 / Online: 19 March 2018 (08:42:39 CET)

A peer-reviewed article of this Preprint also exists.

Twomey, N.; Diethe, T.; Fafoutis, X.; Elsts, A.; McConville, R.; Flach, P.; Craddock, I. A Comprehensive Study of Activity Recognition Using Accelerometers. Informatics 2018, 5, 27. Twomey, N.; Diethe, T.; Fafoutis, X.; Elsts, A.; McConville, R.; Flach, P.; Craddock, I. A Comprehensive Study of Activity Recognition Using Accelerometers. Informatics 2018, 5, 27.

Journal reference: Informatics 2018, 5, 27
DOI: 10.3390/informatics5020027

Abstract

This paper serves a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. We examine research that has focused on the selection of activities, the features that are extracted from the accelerometer data, the segmentation of the time-series data, the locations of accelerometers, the selection and configuration trade-offs, the test/retest reliability, and the generalisation performance. Furthermore, we study these questions from an experimental platform and show, somewhat surprisingly, that many disparate experimental configurations yield comparable predictive performance on testing data. Our understanding of these results is that the experimental setup directly and indirectly defines a pathway for context to be delivered to the classifier, and that, in some settings, certain configurations are more optimal than alternatives. We conclude by identifying how the main results of this work can be used in practice, specifically in experimental configurations in challenging experimental conditions.

Subject Areas

activities of daily living; activity recognition; accelerometers; machine learning; sensors

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