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Sensors 2016, 16(10), 1713; doi:10.3390/s16101713

Hierarchical Activity Recognition Using Smart Watches and RGB-Depth Cameras

1
College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
2
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Academic Editor: Kamiar Aminian
Received: 12 July 2016 / Revised: 25 September 2016 / Accepted: 7 October 2016 / Published: 15 October 2016
(This article belongs to the Special Issue Body Worn Behavior Sensing)
View Full-Text   |   Download PDF [4729 KB, uploaded 15 October 2016]   |  

Abstract

Human activity recognition is important for healthcare and lifestyle evaluation. In this paper, a novel method for activity recognition by jointly considering motion sensor data recorded by wearable smart watches and image data captured by RGB-Depth (RGB-D) cameras is presented. A normalized cross correlation based mapping method is implemented to establish association between motion sensor data with corresponding image data from the same person in multi-person situations. Further, to improve the performance and accuracy of recognition, a hierarchical structure embedded with an automatic group selection method is proposed. Through this method, if the number of activities to be classified is changed, the structure will be changed correspondingly without interaction. Our comparative experiments against the single data source and single layer methods have shown that our method is more accurate and robust. View Full-Text
Keywords: activity recognition; wearable device; RGB-D; hierarchical structure activity recognition; wearable device; RGB-D; hierarchical structure
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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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Li, Z.; Wei, Z.; Huang, L.; Zhang, S.; Nie, J. Hierarchical Activity Recognition Using Smart Watches and RGB-Depth Cameras. Sensors 2016, 16, 1713.

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