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Sensors 2015, 15(1), 2181-2204; doi:10.3390/s150102181

An Activity Recognition Model Using Inertial Sensor Nodes in a Wireless Sensor Network for Frozen Shoulder Rehabilitation Exercises

1
Health Risk Management Department, China Medical University, 91 Hsueh-Shih Rd., Taichung 40402, Taiwan
2
Department of Information and Telecommunications Engineering, Ming Chuan University, 5 De-Ming Rd., Gui Shan, Taoyuan 333, Taiwan
3
Department of Communications Engineering, Yuan Ze University, 135 Yuan-Tung Rd., Chung-Li, Taoyuan 32003, Taiwan
*
Author to whom correspondence should be addressed.
Received: 8 July 2014 / Revised: 2 December 2014 / Accepted: 12 January 2015 / Published: 19 January 2015
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
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Abstract

This paper proposes a model for recognizing motions performed during rehabilitation exercises for frozen shoulder conditions. The model consists of wearable wireless sensor network (WSN) inertial sensor nodes, which were developed for this study, and enables the ubiquitous measurement of bodily motions. The model employs the back propagation neural network (BPNN) algorithm to compute motion data that are formed in the WSN packets; herein, six types of rehabilitation exercises were recognized. The packets sent by each node are converted into six components of acceleration and angular velocity according to three axes. Motor features such as basic acceleration, angular velocity, and derivative tilt angle were input into the training procedure of the BPNN algorithm. In measurements of thirteen volunteers, the accelerations and included angles of nodes were adopted from possible features to demonstrate the procedure. Five exercises involving simple swinging and stretching movements were recognized with an accuracy of 85%–95%; however, the accuracy with which exercises entailing spiral rotations were recognized approximately 60%. Thus, a characteristic space and enveloped spectrum improving derivative features were suggested to enable identifying customized parameters. Finally, a real-time monitoring interface was developed for practical implementation. The proposed model can be applied in ubiquitous healthcare self-management to recognize rehabilitation exercises. View Full-Text
Keywords: back propagation neural network (BPNN); frozen shoulder; inertial sensor node (ISN); rehabilitation activity; ubiquitous health care (UHC); wireless sensor network (WSN) back propagation neural network (BPNN); frozen shoulder; inertial sensor node (ISN); rehabilitation activity; ubiquitous health care (UHC); wireless sensor network (WSN)
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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

Lin, H.-C.; Chiang, S.-Y.; Lee, K.; Kan, Y.-C. An Activity Recognition Model Using Inertial Sensor Nodes in a Wireless Sensor Network for Frozen Shoulder Rehabilitation Exercises. Sensors 2015, 15, 2181-2204.

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