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Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements
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 simultaneo...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5505040/ https://www.ncbi.nlm.nih.gov/pubmed/28697795 http://dx.doi.org/10.1186/s12984-017-0284-4 |
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author | Krasoulis, Agamemnon Kyranou, Iris Erden, Mustapha Suphi Nazarpour, Kianoush Vijayakumar, Sethu |
author_facet | Krasoulis, Agamemnon Kyranou, Iris Erden, Mustapha Suphi Nazarpour, Kianoush Vijayakumar, Sethu |
author_sort | Krasoulis, Agamemnon |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12984-017-0284-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5505040 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55050402017-07-12 Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements Krasoulis, Agamemnon Kyranou, Iris Erden, Mustapha Suphi Nazarpour, Kianoush Vijayakumar, Sethu J Neuroeng Rehabil Research 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12984-017-0284-4) contains supplementary material, which is available to authorized users. BioMed Central 2017-07-11 /pmc/articles/PMC5505040/ /pubmed/28697795 http://dx.doi.org/10.1186/s12984-017-0284-4 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Krasoulis, Agamemnon Kyranou, Iris Erden, Mustapha Suphi Nazarpour, Kianoush Vijayakumar, Sethu Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements |
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 | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5505040/ https://www.ncbi.nlm.nih.gov/pubmed/28697795 http://dx.doi.org/10.1186/s12984-017-0284-4 |
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