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Multi-Sensor Activity Monitoring: Combination of Models with Class-Specific Voting

1
School of Instrument Science and Engineer, Southeast University, Nanjing 210096, China
2
GE Global Research, Niskayuna, NY 12309, USA
3
Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
*
Author to whom correspondence should be addressed.
Information 2019, 10(6), 197; https://doi.org/10.3390/info10060197
Received: 20 March 2019 / Revised: 22 April 2019 / Accepted: 8 May 2019 / Published: 4 June 2019
(This article belongs to the Special Issue Activity Monitoring by Multiple Distributed Sensing)
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Abstract

This paper presents a multi-sensor model combination system with class-specific voting for physical activity monitoring, which combines multiple classifiers obtained by splicing sensor data from different nodes into new data frames to improve the diversity of model inputs. Data obtained from a wearable multi-sensor wireless integrated measurement system (WIMS) consisting of two accelerometers and one ventilation sensor have been analysed to identify 10 different activity types of varying intensities performed by 110 voluntary participants. It is noted that each classifier shows better performance on some specific activity classes. Through class-specific weighted majority voting, the recognition accuracy of 10 PA types has been improved from 86% to 92% compared with the non-combination approach. Furthermore, the combination method has shown to be effective in reducing the subject-to-subject variability (standard deviation of recognition accuracies across subjects) in activity recognition and has better performance in monitoring physical activities of varying intensities than traditional homogeneous classifiers. View Full-Text
Keywords: activity monitoring; support vector machine; multi-sensor combination; weighted voting activity monitoring; support vector machine; multi-sensor combination; weighted voting
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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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Mo, L.; Zeng, L.; Liu, S.; Gao, R.X. Multi-Sensor Activity Monitoring: Combination of Models with Class-Specific Voting. Information 2019, 10, 197.

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