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Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach

Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
Laboratory of Wearable Technologies and Neuromusculoskeletal Research, School of Mechanical Engineering, Sharif University of Technology, Tehran 11155-9567, Iran
Department of Biomedical Engineering, McGill University, Montréal, QC H3A 2B4, Canada
Control and Intelligent Processing, Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran 14395-515, Iran
Musculoskeletal Rehabilitation Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 6135733133, Iran
Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623-5603, USA
Author to whom correspondence should be addressed.
Academic Editors: Steffen Leonhardt and Daniel Teichmann
Sensors 2017, 17(1), 112;
Received: 5 September 2016 / Revised: 19 December 2016 / Accepted: 20 December 2016 / Published: 8 January 2017
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Human movement analysis is an important part of biomechanics and rehabilitation, for which many measurement systems are introduced. Among these, wearable devices have substantial biomedical applications, primarily since they can be implemented both in indoor and outdoor applications. In this study, a Trunk Motion System (TMS) using printed Body-Worn Sensors (BWS) is designed and developed. TMS can measure three-dimensional (3D) trunk motions, is lightweight, and is a portable and non-invasive system. After the recognition of sensor locations, twelve BWSs were printed on stretchable clothing with the purpose of measuring the 3D trunk movements. To integrate BWSs data, a neural network data fusion algorithm was used. The outcome of this algorithm along with the actual 3D anatomical movements (obtained by Qualisys system) were used to calibrate the TMS. Three healthy participants with different physical characteristics participated in the calibration tests. Seven different tasks (each repeated three times) were performed, involving five planar, and two multiplanar movements. Results showed that the accuracy of TMS system was less than 1.0°, 0.8°, 0.6°, 0.8°, 0.9°, and 1.3° for flexion/extension, left/right lateral bending, left/right axial rotation, and multi-planar motions, respectively. In addition, the accuracy of TMS for the identified movement was less than 2.7°. TMS, developed to monitor and measure the trunk orientations, can have diverse applications in clinical, biomechanical, and ergonomic studies to prevent musculoskeletal injuries, and to determine the impact of interventions. View Full-Text
Keywords: wearable system; body worn sensor; trunk movement; sensor fusion wearable system; body worn sensor; trunk movement; sensor fusion
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MDPI and ACS Style

Mokhlespour Esfahani, M.I.; Zobeiri, O.; Moshiri, B.; Narimani, R.; Mehravar, M.; Rashedi, E.; Parnianpour, M. Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach. Sensors 2017, 17, 112.

AMA Style

Mokhlespour Esfahani MI, Zobeiri O, Moshiri B, Narimani R, Mehravar M, Rashedi E, Parnianpour M. Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach. Sensors. 2017; 17(1):112.

Chicago/Turabian Style

Mokhlespour Esfahani, Mohammad I., Omid Zobeiri, Behzad Moshiri, Roya Narimani, Mohammad Mehravar, Ehsan Rashedi, and Mohamad Parnianpour. 2017. "Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach" Sensors 17, no. 1: 112.

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