Wireless Motion Capture System for Upper Limb Rehabilitation
This work is devoted to the presentation of a Wireless Sensor System implementation for upper limb rehabilitation to function as a complementary system for a patient’s progress supervision during rehabilitation exercises. A cost effective motion capture sensor node composed by a 9 Degrees-of-Freedom...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Applied System Innovation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-5577/4/1/14 |
| _version_ | 1797396225987706880 |
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| author | Ourania Tsilomitrou Konstantinos Gkountas Nikolaos Evangeliou Evangelos Dermatas |
| author_facet | Ourania Tsilomitrou Konstantinos Gkountas Nikolaos Evangeliou Evangelos Dermatas |
| author_sort | Ourania Tsilomitrou |
| collection | DOAJ |
| description | This work is devoted to the presentation of a Wireless Sensor System implementation for upper limb rehabilitation to function as a complementary system for a patient’s progress supervision during rehabilitation exercises. A cost effective motion capture sensor node composed by a 9 Degrees-of-Freedom (DoF) Inertial Measurement Unit (IMU) is mounted on the patient’s upper limb segments and sends wirelessly the corresponding measured signals to a base station. The sensor orientation and the upper limb individual segments movement in 3-Dimensional (3D) space are derived by processing the sensors’ raw data. For the latter purpose, a biomechanical model which resembles that of a kinematic model of a robotic arm based on the Denavit-Hartenberg (DH) configuration is used to approximate in real time the upper limb movements. The joint angles of the upper limb model are estimated from the extracted sensor node’s orientation angles. The experimental results of a human performing common rehabilitation exercises using the proposed motion capture sensor node are compared with the ones using an off-the-shelf sensor. This comparison results to very low error rates with the root mean square error (RMSE) being about <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.02</mn></mrow></semantics></math></inline-formula> m. |
| first_indexed | 2024-03-09T00:48:16Z |
| format | Article |
| id | doaj.art-130cf3a783a740ab9be1d6b7146615c1 |
| institution | Directory Open Access Journal |
| issn | 2571-5577 |
| language | English |
| last_indexed | 2024-03-09T00:48:16Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied System Innovation |
| spelling | doaj.art-130cf3a783a740ab9be1d6b7146615c12023-12-11T17:22:05ZengMDPI AGApplied System Innovation2571-55772021-02-01411410.3390/asi4010014Wireless Motion Capture System for Upper Limb RehabilitationOurania Tsilomitrou0Konstantinos Gkountas1Nikolaos Evangeliou2Evangelos Dermatas3Electrical and Computer Engineering Department, University of Patras, 26500 Rio, GreeceElectrical and Computer Engineering Department, University of Patras, 26500 Rio, GreeceElectrical and Computer Engineering Department, University of Patras, 26500 Rio, GreeceElectrical and Computer Engineering Department, University of Patras, 26500 Rio, GreeceThis work is devoted to the presentation of a Wireless Sensor System implementation for upper limb rehabilitation to function as a complementary system for a patient’s progress supervision during rehabilitation exercises. A cost effective motion capture sensor node composed by a 9 Degrees-of-Freedom (DoF) Inertial Measurement Unit (IMU) is mounted on the patient’s upper limb segments and sends wirelessly the corresponding measured signals to a base station. The sensor orientation and the upper limb individual segments movement in 3-Dimensional (3D) space are derived by processing the sensors’ raw data. For the latter purpose, a biomechanical model which resembles that of a kinematic model of a robotic arm based on the Denavit-Hartenberg (DH) configuration is used to approximate in real time the upper limb movements. The joint angles of the upper limb model are estimated from the extracted sensor node’s orientation angles. The experimental results of a human performing common rehabilitation exercises using the proposed motion capture sensor node are compared with the ones using an off-the-shelf sensor. This comparison results to very low error rates with the root mean square error (RMSE) being about <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.02</mn></mrow></semantics></math></inline-formula> m.https://www.mdpi.com/2571-5577/4/1/14wireless sensorsupper limb kinematic modelupper limb rehabilitationphysiotherapy |
| spellingShingle | Ourania Tsilomitrou Konstantinos Gkountas Nikolaos Evangeliou Evangelos Dermatas Wireless Motion Capture System for Upper Limb Rehabilitation Applied System Innovation wireless sensors upper limb kinematic model upper limb rehabilitation physiotherapy |
| title | Wireless Motion Capture System for Upper Limb Rehabilitation |
| title_full | Wireless Motion Capture System for Upper Limb Rehabilitation |
| title_fullStr | Wireless Motion Capture System for Upper Limb Rehabilitation |
| title_full_unstemmed | Wireless Motion Capture System for Upper Limb Rehabilitation |
| title_short | Wireless Motion Capture System for Upper Limb Rehabilitation |
| title_sort | wireless motion capture system for upper limb rehabilitation |
| topic | wireless sensors upper limb kinematic model upper limb rehabilitation physiotherapy |
| url | https://www.mdpi.com/2571-5577/4/1/14 |
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