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Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors
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Sensors 2016, 16(6), 800; doi:10.3390/s16060800

How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls?

Department of Intelligent Systems, Jožef Stefan International Postgraduate School, Jožef Stefan Institute, Ljubljana 1000, Slovenia
The first two authors should be regarded as joint first authors.
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Author to whom correspondence should be addressed.
Academic Editor: Kamiar Aminian
Received: 5 February 2016 / Revised: 19 May 2016 / Accepted: 23 May 2016 / Published: 1 June 2016
(This article belongs to the Special Issue Body Worn Behavior Sensing)
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Abstract

Although wearable accelerometers can successfully recognize activities and detect falls, their adoption in real life is low because users do not want to wear additional devices. A possible solution is an accelerometer inside a wrist device/smartwatch. However, wrist placement might perform poorly in terms of accuracy due to frequent random movements of the hand. In this paper we perform a thorough, large-scale evaluation of methods for activity recognition and fall detection on four datasets. On the first two we showed that the left wrist performs better compared to the dominant right one, and also better compared to the elbow and the chest, but worse compared to the ankle, knee and belt. On the third (Opportunity) dataset, our method outperformed the related work, indicating that our feature-preprocessing creates better input data. And finally, on a real-life unlabeled dataset the recognized activities captured the subject’s daily rhythm and activities. Our fall-detection method detected all of the fast falls and minimized the false positives, achieving 85% accuracy on the first dataset. Because the other datasets did not contain fall events, only false positives were evaluated, resulting in 9 for the second, 1 for the third and 15 for the real-life dataset (57 days data). View Full-Text
Keywords: activity recognition; fall detection; wrist; accelerometer; machine learning; classification; feature extraction activity recognition; fall detection; wrist; accelerometer; machine learning; classification; feature extraction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Gjoreski, M.; Gjoreski, H.; Luštrek, M.; Gams, M. How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls? Sensors 2016, 16, 800.

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