Phase-Based Grasp Classification for Prosthetic Hand Control Using sEMG
Pattern recognition using surface Electromyography (sEMG) applied on prosthesis control has attracted much attention in these years. In most of the existing methods, the sEMG signal during the firmly grasped period is used for grasp classification because good performance can be achieved due to its...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Biosensors |
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| Online Access: | https://www.mdpi.com/2079-6374/12/2/57 |
| _version_ | 1827656471790223360 |
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| author | Shuo Wang Jingjing Zheng Bin Zheng Xianta Jiang |
| author_facet | Shuo Wang Jingjing Zheng Bin Zheng Xianta Jiang |
| author_sort | Shuo Wang |
| collection | DOAJ |
| description | Pattern recognition using surface Electromyography (sEMG) applied on prosthesis control has attracted much attention in these years. In most of the existing methods, the sEMG signal during the firmly grasped period is used for grasp classification because good performance can be achieved due to its relatively stable signal. However, using the only the firmly grasped period may cause a delay to control the prosthetic hand gestures. Regarding this issue, we explored how grasp classification accuracy changes during the reaching and grasping process, and identified the period that can leverage the grasp classification accuracy and the earlier grasp detection. We found that the grasp classification accuracy increased along the hand gradually grasping the object till firmly grasped, and there is a <i>sweet period</i> before firmly grasped period, which could be suitable for early grasp classification with reduced delay. On top of this, we also explored corresponding training strategies for better grasp classification in real-time applications. |
| first_indexed | 2024-03-09T22:28:54Z |
| format | Article |
| id | doaj.art-5e52d9ccc0ad466b8f90b0c1c5e73f61 |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-09T22:28:54Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-5e52d9ccc0ad466b8f90b0c1c5e73f612023-11-23T19:00:32ZengMDPI AGBiosensors2079-63742022-01-011225710.3390/bios12020057Phase-Based Grasp Classification for Prosthetic Hand Control Using sEMGShuo Wang0Jingjing Zheng1Bin Zheng2Xianta Jiang3Department of Computer Science, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, CanadaDepartment of Computer Science, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, CanadaDepartment of Surgery, University of Alberta, Edmonton, AB T6G 2R3, CanadaDepartment of Computer Science, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, CanadaPattern recognition using surface Electromyography (sEMG) applied on prosthesis control has attracted much attention in these years. In most of the existing methods, the sEMG signal during the firmly grasped period is used for grasp classification because good performance can be achieved due to its relatively stable signal. However, using the only the firmly grasped period may cause a delay to control the prosthetic hand gestures. Regarding this issue, we explored how grasp classification accuracy changes during the reaching and grasping process, and identified the period that can leverage the grasp classification accuracy and the earlier grasp detection. We found that the grasp classification accuracy increased along the hand gradually grasping the object till firmly grasped, and there is a <i>sweet period</i> before firmly grasped period, which could be suitable for early grasp classification with reduced delay. On top of this, we also explored corresponding training strategies for better grasp classification in real-time applications.https://www.mdpi.com/2079-6374/12/2/57myoelectric prosthesissEMGgrasp phases analysisgrasp classificationmachine learning |
| spellingShingle | Shuo Wang Jingjing Zheng Bin Zheng Xianta Jiang Phase-Based Grasp Classification for Prosthetic Hand Control Using sEMG Biosensors myoelectric prosthesis sEMG grasp phases analysis grasp classification machine learning |
| title | Phase-Based Grasp Classification for Prosthetic Hand Control Using sEMG |
| title_full | Phase-Based Grasp Classification for Prosthetic Hand Control Using sEMG |
| title_fullStr | Phase-Based Grasp Classification for Prosthetic Hand Control Using sEMG |
| title_full_unstemmed | Phase-Based Grasp Classification for Prosthetic Hand Control Using sEMG |
| title_short | Phase-Based Grasp Classification for Prosthetic Hand Control Using sEMG |
| title_sort | phase based grasp classification for prosthetic hand control using semg |
| topic | myoelectric prosthesis sEMG grasp phases analysis grasp classification machine learning |
| url | https://www.mdpi.com/2079-6374/12/2/57 |
| work_keys_str_mv | AT shuowang phasebasedgraspclassificationforprosthetichandcontrolusingsemg AT jingjingzheng phasebasedgraspclassificationforprosthetichandcontrolusingsemg AT binzheng phasebasedgraspclassificationforprosthetichandcontrolusingsemg AT xiantajiang phasebasedgraspclassificationforprosthetichandcontrolusingsemg |