Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 15 March 2017 / Approved: 16 March 2017 / Online: 16 March 2017 (17:19:01 CET)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Sensors 2017, 17, 774
DOI: 10.3390/s17040774

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. Sixteen 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 that 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 being greater than 95%. 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.

Subject Areas

activity classification; activity monitoring; wearable sensors; sensor positions

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