A multi-Kalman filter-based approach for decoding arm kinematics from EMG recordings
Abstract Background Remarkable work has been recently introduced to enhance the usage of Electromyography (EMG) signals in operating prosthetic arms. Despite the rapid advancements in this field, providing a reliable, naturalistic myoelectric prosthesis remains a significant challenge. Other challen...
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Language: | English |
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BMC
2022-09-01
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Series: | BioMedical Engineering OnLine |
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Online Access: | https://doi.org/10.1186/s12938-022-01030-6 |
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author | Hend ElMohandes Seif Eldawlatly Josep Marcel Cardona Audí Roman Ruff Klaus-Peter Hoffmann |
author_facet | Hend ElMohandes Seif Eldawlatly Josep Marcel Cardona Audí Roman Ruff Klaus-Peter Hoffmann |
author_sort | Hend ElMohandes |
collection | DOAJ |
description | Abstract Background Remarkable work has been recently introduced to enhance the usage of Electromyography (EMG) signals in operating prosthetic arms. Despite the rapid advancements in this field, providing a reliable, naturalistic myoelectric prosthesis remains a significant challenge. Other challenges include the limited number of allowed movements, lack of simultaneous, continuous control and the high computational power that could be needed for accurate decoding. In this study, we propose an EMG-based multi-Kalman filter approach to decode arm kinematics; specifically, the elbow angle (θ), wrist joint horizontal (X) and vertical (Y) positions in a continuous and simultaneous manner. Results Ten subjects were examined from which we recorded arm kinematics and EMG signals of the biceps, triceps, lateral and anterior deltoid muscles corresponding to a randomized set of movements. The performance of the proposed decoder is assessed using the correlation coefficient (CC) and the normalized root-mean-square error (NRMSE) computed between the actual and the decoded kinematic. Results demonstrate that when training and testing the decoder using same-subject data, an average CC of 0.68 ± 0.1, 0.67 ± 0.12 and 0.64 ± 0.11, and average NRMSE of 0.21 ± 0.06, 0.18 ± 0.03 and 0.24 ± 0.07 were achieved for θ, X, and Y, respectively. When training the decoder using the data of one subject and decoding the data of other subjects, an average CC of 0.61 ± 0.19, 0.61 ± 0.16 and 0.48 ± 0.17, and an average NRMSE of 0.23 ± 0.07, 0.2 ± 0.05 and 0.38 ± 0.15 were achieved for θ, X, and Y, respectively. Conclusions These results suggest the efficacy of the proposed approach and indicates the possibility of obtaining a subject-independent decoder. |
first_indexed | 2024-04-14T01:47:57Z |
format | Article |
id | doaj.art-aa1c9695675e460ea77a756e405250cd |
institution | Directory Open Access Journal |
issn | 1475-925X |
language | English |
last_indexed | 2024-04-14T01:47:57Z |
publishDate | 2022-09-01 |
publisher | BMC |
record_format | Article |
series | BioMedical Engineering OnLine |
spelling | doaj.art-aa1c9695675e460ea77a756e405250cd2022-12-22T02:19:28ZengBMCBioMedical Engineering OnLine1475-925X2022-09-0121111810.1186/s12938-022-01030-6A multi-Kalman filter-based approach for decoding arm kinematics from EMG recordingsHend ElMohandes0Seif Eldawlatly1Josep Marcel Cardona Audí2Roman Ruff3Klaus-Peter Hoffmann4Center of Informatics Science, Nile UniversityComputer and Systems Engineering Dept, Faculty of Engineering, Ain Shams UniversityDepartment of Medical Engineering and Neuroprostheses, Fraunhofer IBMTDepartment of Medical Engineering and Neuroprostheses, Fraunhofer IBMTDepartment of Medical Engineering and Neuroprostheses, Fraunhofer IBMTAbstract Background Remarkable work has been recently introduced to enhance the usage of Electromyography (EMG) signals in operating prosthetic arms. Despite the rapid advancements in this field, providing a reliable, naturalistic myoelectric prosthesis remains a significant challenge. Other challenges include the limited number of allowed movements, lack of simultaneous, continuous control and the high computational power that could be needed for accurate decoding. In this study, we propose an EMG-based multi-Kalman filter approach to decode arm kinematics; specifically, the elbow angle (θ), wrist joint horizontal (X) and vertical (Y) positions in a continuous and simultaneous manner. Results Ten subjects were examined from which we recorded arm kinematics and EMG signals of the biceps, triceps, lateral and anterior deltoid muscles corresponding to a randomized set of movements. The performance of the proposed decoder is assessed using the correlation coefficient (CC) and the normalized root-mean-square error (NRMSE) computed between the actual and the decoded kinematic. Results demonstrate that when training and testing the decoder using same-subject data, an average CC of 0.68 ± 0.1, 0.67 ± 0.12 and 0.64 ± 0.11, and average NRMSE of 0.21 ± 0.06, 0.18 ± 0.03 and 0.24 ± 0.07 were achieved for θ, X, and Y, respectively. When training the decoder using the data of one subject and decoding the data of other subjects, an average CC of 0.61 ± 0.19, 0.61 ± 0.16 and 0.48 ± 0.17, and an average NRMSE of 0.23 ± 0.07, 0.2 ± 0.05 and 0.38 ± 0.15 were achieved for θ, X, and Y, respectively. Conclusions These results suggest the efficacy of the proposed approach and indicates the possibility of obtaining a subject-independent decoder.https://doi.org/10.1186/s12938-022-01030-6Kalman filterDecodingEMGProsthetic arms |
spellingShingle | Hend ElMohandes Seif Eldawlatly Josep Marcel Cardona Audí Roman Ruff Klaus-Peter Hoffmann A multi-Kalman filter-based approach for decoding arm kinematics from EMG recordings BioMedical Engineering OnLine Kalman filter Decoding EMG Prosthetic arms |
title | A multi-Kalman filter-based approach for decoding arm kinematics from EMG recordings |
title_full | A multi-Kalman filter-based approach for decoding arm kinematics from EMG recordings |
title_fullStr | A multi-Kalman filter-based approach for decoding arm kinematics from EMG recordings |
title_full_unstemmed | A multi-Kalman filter-based approach for decoding arm kinematics from EMG recordings |
title_short | A multi-Kalman filter-based approach for decoding arm kinematics from EMG recordings |
title_sort | multi kalman filter based approach for decoding arm kinematics from emg recordings |
topic | Kalman filter Decoding EMG Prosthetic arms |
url | https://doi.org/10.1186/s12938-022-01030-6 |
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