Investigating the Impact of Training Protocols on Myoelectric Pattern Recognition Control in Upper-Limb Amputees
Myoelectric control schemes, pivotal in the control of prosthetic limbs, are often developed and evaluated in ideal laboratory conditions. However, these controlled environments may not fully represent the diverse challenges users face in real-world scenarios. The present study aims to tackle some o...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10942474/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849731376141565952 |
|---|---|
| author | Elaheh Mohammadreza Vinicius Prado da Fonseca Xianta Jiang |
| author_facet | Elaheh Mohammadreza Vinicius Prado da Fonseca Xianta Jiang |
| author_sort | Elaheh Mohammadreza |
| collection | DOAJ |
| description | Myoelectric control schemes, pivotal in the control of prosthetic limbs, are often developed and evaluated in ideal laboratory conditions. However, these controlled environments may not fully represent the diverse challenges users face in real-world scenarios. The present study aims to tackle some of the existing research limitations by exploring the influence of various model training protocols on myoelectric pattern recognition within a semi-autonomous control system, which has been shown to reduce user cognitive load and enhance overall system performance. Specifically, we focus on the effects of limb movement and weight-bearing activities. We investigate the effect of four distinct training protocols in pattern recognition control for upper-limb prostheses, including training without a prosthetic hand, training with a prosthetic hand and static gestures, training with a prosthetic hand and dynamic movements guided by a graphical user interface (GUI), and training with a prosthetic hand having dynamic transfers and unguided. By examining these conditions, we aim to provide an understanding of how different training protocols and different labeling methods influence myoelectric pattern recognition control. Our results, based on experiments conducted with 14 non-disabled and one amputee participant, suggest that introducing the weight of the prosthetic hand and dynamic movements of the arm to the training data improves the accuracy and robustness of the control scheme. Real-time control experiments with a group of five non-disabled and one amputee participant using a multi-DOF prosthetic hand also verify our findings. |
| format | Article |
| id | doaj-art-3ccd65fdff3548419851e261a951e751 |
| institution | DOAJ |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-3ccd65fdff3548419851e261a951e7512025-08-20T03:08:33ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-01331338134810.1109/TNSRE.2025.355510010942474Investigating the Impact of Training Protocols on Myoelectric Pattern Recognition Control in Upper-Limb AmputeesElaheh Mohammadreza0https://orcid.org/0000-0002-5563-2596Vinicius Prado da Fonseca1https://orcid.org/0000-0001-5705-3056Xianta Jiang2https://orcid.org/0000-0002-3219-1871Computer Science Department, Memorial University of Newfoundland, St. John’s, NL, CanadaComputer Science Department, Memorial University of Newfoundland, St. John’s, NL, CanadaComputer Science Department, Memorial University of Newfoundland, St. John’s, NL, CanadaMyoelectric control schemes, pivotal in the control of prosthetic limbs, are often developed and evaluated in ideal laboratory conditions. However, these controlled environments may not fully represent the diverse challenges users face in real-world scenarios. The present study aims to tackle some of the existing research limitations by exploring the influence of various model training protocols on myoelectric pattern recognition within a semi-autonomous control system, which has been shown to reduce user cognitive load and enhance overall system performance. Specifically, we focus on the effects of limb movement and weight-bearing activities. We investigate the effect of four distinct training protocols in pattern recognition control for upper-limb prostheses, including training without a prosthetic hand, training with a prosthetic hand and static gestures, training with a prosthetic hand and dynamic movements guided by a graphical user interface (GUI), and training with a prosthetic hand having dynamic transfers and unguided. By examining these conditions, we aim to provide an understanding of how different training protocols and different labeling methods influence myoelectric pattern recognition control. Our results, based on experiments conducted with 14 non-disabled and one amputee participant, suggest that introducing the weight of the prosthetic hand and dynamic movements of the arm to the training data improves the accuracy and robustness of the control scheme. Real-time control experiments with a group of five non-disabled and one amputee participant using a multi-DOF prosthetic hand also verify our findings.https://ieeexplore.ieee.org/document/10942474/Myoelectric controlpattern recognitionsEMGtraining protocoltransradial prosthetic |
| spellingShingle | Elaheh Mohammadreza Vinicius Prado da Fonseca Xianta Jiang Investigating the Impact of Training Protocols on Myoelectric Pattern Recognition Control in Upper-Limb Amputees IEEE Transactions on Neural Systems and Rehabilitation Engineering Myoelectric control pattern recognition sEMG training protocol transradial prosthetic |
| title | Investigating the Impact of Training Protocols on Myoelectric Pattern Recognition Control in Upper-Limb Amputees |
| title_full | Investigating the Impact of Training Protocols on Myoelectric Pattern Recognition Control in Upper-Limb Amputees |
| title_fullStr | Investigating the Impact of Training Protocols on Myoelectric Pattern Recognition Control in Upper-Limb Amputees |
| title_full_unstemmed | Investigating the Impact of Training Protocols on Myoelectric Pattern Recognition Control in Upper-Limb Amputees |
| title_short | Investigating the Impact of Training Protocols on Myoelectric Pattern Recognition Control in Upper-Limb Amputees |
| title_sort | investigating the impact of training protocols on myoelectric pattern recognition control in upper limb amputees |
| topic | Myoelectric control pattern recognition sEMG training protocol transradial prosthetic |
| url | https://ieeexplore.ieee.org/document/10942474/ |
| work_keys_str_mv | AT elahehmohammadreza investigatingtheimpactoftrainingprotocolsonmyoelectricpatternrecognitioncontrolinupperlimbamputees AT viniciuspradodafonseca investigatingtheimpactoftrainingprotocolsonmyoelectricpatternrecognitioncontrolinupperlimbamputees AT xiantajiang investigatingtheimpactoftrainingprotocolsonmyoelectricpatternrecognitioncontrolinupperlimbamputees |