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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,...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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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. |
format | Online Article Text |
id | pubmed-5298685 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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