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

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,...

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Autores principales: Mokhlespour Esfahani, Mohammad Iman, Zobeiri, Omid, Moshiri, Behzad, Narimani, Roya, Mehravar, Mohammad, Rashedi, Ehsan, Parnianpour, Mohamad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5298685/
https://www.ncbi.nlm.nih.gov/pubmed/28075342
http://dx.doi.org/10.3390/s17010112
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author Mokhlespour Esfahani, Mohammad Iman
Zobeiri, Omid
Moshiri, Behzad
Narimani, Roya
Mehravar, Mohammad
Rashedi, Ehsan
Parnianpour, Mohamad
author_facet Mokhlespour Esfahani, Mohammad Iman
Zobeiri, Omid
Moshiri, Behzad
Narimani, Roya
Mehravar, Mohammad
Rashedi, Ehsan
Parnianpour, Mohamad
author_sort Mokhlespour Esfahani, Mohammad Iman
collection PubMed
description 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.
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spelling pubmed-52986852017-02-10 Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach Mokhlespour Esfahani, Mohammad Iman Zobeiri, Omid Moshiri, Behzad Narimani, Roya Mehravar, Mohammad Rashedi, Ehsan Parnianpour, Mohamad Sensors (Basel) Article 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. MDPI 2017-01-08 /pmc/articles/PMC5298685/ /pubmed/28075342 http://dx.doi.org/10.3390/s17010112 Text en © 2017 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mokhlespour Esfahani, Mohammad Iman
Zobeiri, Omid
Moshiri, Behzad
Narimani, Roya
Mehravar, Mohammad
Rashedi, Ehsan
Parnianpour, Mohamad
Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach
title Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach
title_full Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach
title_fullStr Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach
title_full_unstemmed Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach
title_short Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach
title_sort trunk motion system (tms) using printed body worn sensor (bws) via data fusion approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5298685/
https://www.ncbi.nlm.nih.gov/pubmed/28075342
http://dx.doi.org/10.3390/s17010112
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