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,...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/17/1/112 |
| _version_ | 1798006701574586368 |
|---|---|
| author | Mohammad Iman Mokhlespour Esfahani Omid Zobeiri Behzad Moshiri Roya Narimani Mohammad Mehravar Ehsan Rashedi Mohamad Parnianpour |
| author_facet | Mohammad Iman Mokhlespour Esfahani Omid Zobeiri Behzad Moshiri Roya Narimani Mohammad Mehravar Ehsan Rashedi Mohamad Parnianpour |
| author_sort | Mohammad Iman Mokhlespour Esfahani |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T12:59:54Z |
| format | Article |
| id | doaj.art-dd5ebffb7a2047d48915c3763c898a40 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:59:54Z |
| publishDate | 2017-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-dd5ebffb7a2047d48915c3763c898a402022-12-22T04:22:59ZengMDPI AGSensors1424-82202017-01-0117111210.3390/s17010112s17010112Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion ApproachMohammad Iman Mokhlespour Esfahani0Omid Zobeiri1Behzad Moshiri2Roya Narimani3Mohammad Mehravar4Ehsan Rashedi5Mohamad Parnianpour6Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USADepartment of Biomedical Engineering, McGill University, Montréal, QC H3A 2B4, CanadaControl and Intelligent Processing, Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran 14395-515, IranLaboratory of Wearable Technologies and Neuromusculoskeletal Research, School of Mechanical Engineering, Sharif University of Technology, Tehran 11155-9567, IranMusculoskeletal Rehabilitation Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 6135733133, IranDepartment of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623-5603, USALaboratory of Wearable Technologies and Neuromusculoskeletal Research, School of Mechanical Engineering, Sharif University of Technology, Tehran 11155-9567, IranHuman 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.http://www.mdpi.com/1424-8220/17/1/112wearable systembody worn sensortrunk movementsensor fusion |
| spellingShingle | Mohammad Iman Mokhlespour Esfahani Omid Zobeiri Behzad Moshiri Roya Narimani Mohammad Mehravar Ehsan Rashedi Mohamad Parnianpour Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach Sensors wearable system body worn sensor trunk movement sensor fusion |
| 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 | wearable system body worn sensor trunk movement sensor fusion |
| url | http://www.mdpi.com/1424-8220/17/1/112 |
| work_keys_str_mv | AT mohammadimanmokhlespouresfahani trunkmotionsystemtmsusingprintedbodywornsensorbwsviadatafusionapproach AT omidzobeiri trunkmotionsystemtmsusingprintedbodywornsensorbwsviadatafusionapproach AT behzadmoshiri trunkmotionsystemtmsusingprintedbodywornsensorbwsviadatafusionapproach AT royanarimani trunkmotionsystemtmsusingprintedbodywornsensorbwsviadatafusionapproach AT mohammadmehravar trunkmotionsystemtmsusingprintedbodywornsensorbwsviadatafusionapproach AT ehsanrashedi trunkmotionsystemtmsusingprintedbodywornsensorbwsviadatafusionapproach AT mohamadparnianpour trunkmotionsystemtmsusingprintedbodywornsensorbwsviadatafusionapproach |