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Open AccessArticle

A Comprehensive Study of Activity Recognition Using Accelerometers

School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UK
Amazon Research, Cambridge CB3 0RD, UK
Author to whom correspondence should be addressed.
Work done prior to joining Amazon.
These authors contributed equally to this work.
Informatics 2018, 5(2), 27;
Received: 16 March 2018 / Revised: 22 May 2018 / Accepted: 25 May 2018 / Published: 30 May 2018
(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)
This paper serves as a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. The paper is particularly focused on long-term activity recognition in real-world settings. In these environments, data collection is not a trivial matter; thus, there are performance trade-offs between prediction accuracy, which is not the sole system objective, and keeping the maintenance overhead at minimum levels. 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. View Full-Text
Keywords: activities of daily living; activity recognition; accelerometers; machine learning; sensors activities of daily living; activity recognition; accelerometers; machine learning; sensors
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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.

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