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Sensors 2017, 17(3), 529; doi:10.3390/s17030529

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

Electrical and Computer Engineering Department, McGill University, Montréal, QC H3A 0E9, Canada
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Academic Editor: Stephane Evoy
Received: 15 November 2016 / Revised: 22 February 2017 / Accepted: 28 February 2017 / Published: 7 March 2017
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Canada 2017)
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Abstract

Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results. View Full-Text
Keywords: human activity recognition; machine learning; supervised classification; wearable sensors; sensors heterogeneities human activity recognition; machine learning; supervised classification; wearable sensors; sensors heterogeneities
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Janidarmian, M.; Roshan Fekr, A.; Radecka, K.; Zilic, Z. A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition. Sensors 2017, 17, 529.

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