Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements
Abstract Background Myoelectric pattern recognition systems can decode movement intention to drive upper-limb prostheses. Despite recent advances in academic research, the commercial adoption of such systems remains low. This limitation is mainly due to the lack of classification robustness and a si...
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| Format: | Article |
| Language: | English |
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BMC
2017-07-01
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| Series: | Journal of NeuroEngineering and Rehabilitation |
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| Online Access: | http://link.springer.com/article/10.1186/s12984-017-0284-4 |
| _version_ | 1818942953801908224 |
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| author | Agamemnon Krasoulis Iris Kyranou Mustapha Suphi Erden Kianoush Nazarpour Sethu Vijayakumar |
| author_facet | Agamemnon Krasoulis Iris Kyranou Mustapha Suphi Erden Kianoush Nazarpour Sethu Vijayakumar |
| author_sort | Agamemnon Krasoulis |
| collection | DOAJ |
| description | Abstract Background Myoelectric pattern recognition systems can decode movement intention to drive upper-limb prostheses. Despite recent advances in academic research, the commercial adoption of such systems remains low. This limitation is mainly due to the lack of classification robustness and a simultaneous requirement for a large number of electromyogram (EMG) electrodes. We propose to address these two issues by using a multi-modal approach which combines surface electromyography (sEMG) with inertial measurements (IMs) and an appropriate training data collection paradigm. We demonstrate that this can significantly improve classification performance as compared to conventional techniques exclusively based on sEMG signals. Methods We collected and analyzed a large dataset comprising recordings with 20 able-bodied and two amputee participants executing 40 movements. Additionally, we conducted a novel real-time prosthetic hand control experiment with 11 able-bodied subjects and an amputee by using a state-of-the-art commercial prosthetic hand. A systematic performance comparison was carried out to investigate the potential benefit of incorporating IMs in prosthetic hand control. Results The inclusion of IM data improved performance significantly, by increasing classification accuracy (CA) in the offline analysis and improving completion rates (CRs) in the real-time experiment. Our findings were consistent across able-bodied and amputee subjects. Integrating the sEMG electrodes and IM sensors within a single sensor package enabled us to achieve high-level performance by using on average 4-6 sensors. Conclusions The results from our experiments suggest that IMs can form an excellent complimentary source signal for upper-limb myoelectric prostheses. We trust that multi-modal control solutions have the potential of improving the usability of upper-extremity prostheses in real-life applications. |
| first_indexed | 2024-12-20T07:19:38Z |
| format | Article |
| id | doaj.art-d170435ce0bd4c08881fd8e68ffd943c |
| institution | Directory Open Access Journal |
| issn | 1743-0003 |
| language | English |
| last_indexed | 2024-12-20T07:19:38Z |
| publishDate | 2017-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of NeuroEngineering and Rehabilitation |
| spelling | doaj.art-d170435ce0bd4c08881fd8e68ffd943c2022-12-21T19:48:42ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032017-07-0114111410.1186/s12984-017-0284-4Improved prosthetic hand control with concurrent use of myoelectric and inertial measurementsAgamemnon Krasoulis0Iris Kyranou1Mustapha Suphi Erden2Kianoush Nazarpour3Sethu Vijayakumar4Institute of Perception, Action and Behaviour, School of Informatics, University of EdinburghInstitute of Perception, Action and Behaviour, School of Informatics, University of EdinburghSchool of Engineering and Physical Sciences, Heriot Watt UniversitySchool of Electrical and Electronic Engineering, Newcastle UniversityInstitute of Perception, Action and Behaviour, School of Informatics, University of EdinburghAbstract Background Myoelectric pattern recognition systems can decode movement intention to drive upper-limb prostheses. Despite recent advances in academic research, the commercial adoption of such systems remains low. This limitation is mainly due to the lack of classification robustness and a simultaneous requirement for a large number of electromyogram (EMG) electrodes. We propose to address these two issues by using a multi-modal approach which combines surface electromyography (sEMG) with inertial measurements (IMs) and an appropriate training data collection paradigm. We demonstrate that this can significantly improve classification performance as compared to conventional techniques exclusively based on sEMG signals. Methods We collected and analyzed a large dataset comprising recordings with 20 able-bodied and two amputee participants executing 40 movements. Additionally, we conducted a novel real-time prosthetic hand control experiment with 11 able-bodied subjects and an amputee by using a state-of-the-art commercial prosthetic hand. A systematic performance comparison was carried out to investigate the potential benefit of incorporating IMs in prosthetic hand control. Results The inclusion of IM data improved performance significantly, by increasing classification accuracy (CA) in the offline analysis and improving completion rates (CRs) in the real-time experiment. Our findings were consistent across able-bodied and amputee subjects. Integrating the sEMG electrodes and IM sensors within a single sensor package enabled us to achieve high-level performance by using on average 4-6 sensors. Conclusions The results from our experiments suggest that IMs can form an excellent complimentary source signal for upper-limb myoelectric prostheses. We trust that multi-modal control solutions have the potential of improving the usability of upper-extremity prostheses in real-life applications.http://link.springer.com/article/10.1186/s12984-017-0284-4Myoelectric prosthesisMyoelectric controlInertial measurement unitSurface electromyographyHand motion classification |
| spellingShingle | Agamemnon Krasoulis Iris Kyranou Mustapha Suphi Erden Kianoush Nazarpour Sethu Vijayakumar Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements Journal of NeuroEngineering and Rehabilitation Myoelectric prosthesis Myoelectric control Inertial measurement unit Surface electromyography Hand motion classification |
| title | Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements |
| title_full | Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements |
| title_fullStr | Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements |
| title_full_unstemmed | Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements |
| title_short | Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements |
| title_sort | improved prosthetic hand control with concurrent use of myoelectric and inertial measurements |
| topic | Myoelectric prosthesis Myoelectric control Inertial measurement unit Surface electromyography Hand motion classification |
| url | http://link.springer.com/article/10.1186/s12984-017-0284-4 |
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