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Sensors 2017, 17(4), 774; doi:10.3390/s17040774

Analysis of Optimal Sensor Positions for Activity Classification and Application on a Different Data Collection Scenario

1
School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathumthani 12000, Thailand
2
National Electronics and Computer Technology Center, Pathumthani 12120, Thailand
3
International College, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
*
Author to whom correspondence should be addressed.
Academic Editors: Giancarlo Fortino, Hassan Ghasemzadeh, Wenfeng Li, Yin Zhang and Luca Benini
Received: 11 January 2017 / Revised: 15 March 2017 / Accepted: 16 March 2017 / Published: 5 April 2017
(This article belongs to the Special Issue Advances in Body Sensor Networks: Sensors, Systems, and Applications)
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Abstract

This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Nineteen features are extracted, and the feature importance is measured by using the Relief-F feature selection algorithm. Eight classification algorithms are evaluated on a dataset collected from young subjects and a dataset collected from elderly subjects, with two different experimental settings. To deal with different sampling rates, signals with a high data rate are down-sampled and a transformation matrix is used for aligning signals to the same coordinate system. The thigh, chest, side of the waist, and front of the waist are the best four sensor positions for the first dataset (young subjects), with average accuracy values greater than 96%. The best model obtained from the first dataset for the side of the waist is validated on the second dataset (elderly subjects). The most appropriate number of features for each sensor position is reported. The results provide a reference for building activity recognition models for different sensor positions, as well as for data acquired from different hardware platforms and subject groups. View Full-Text
Keywords: activity classification; activity monitoring; wearable sensors; sensor positions activity classification; activity monitoring; wearable sensors; sensor positions
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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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MDPI and ACS Style

Pannurat, N.; Thiemjarus, S.; Nantajeewarawat, E.; Anantavrasilp, I. Analysis of Optimal Sensor Positions for Activity Classification and Application on a Different Data Collection Scenario. Sensors 2017, 17, 774.

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