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...

Full description

Bibliographic Details
Main Authors: Hend ElMohandes, Seif Eldawlatly, Josep Marcel Cardona Audí, Roman Ruff, Klaus-Peter Hoffmann
Format: Article
Language:English
Published: BMC 2022-09-01
Series:BioMedical Engineering OnLine
Subjects:
Online Access:https://doi.org/10.1186/s12938-022-01030-6
_version_ 1817994067521306624
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
work_keys_str_mv AT hendelmohandes amultikalmanfilterbasedapproachfordecodingarmkinematicsfromemgrecordings
AT seifeldawlatly amultikalmanfilterbasedapproachfordecodingarmkinematicsfromemgrecordings
AT josepmarcelcardonaaudi amultikalmanfilterbasedapproachfordecodingarmkinematicsfromemgrecordings
AT romanruff amultikalmanfilterbasedapproachfordecodingarmkinematicsfromemgrecordings
AT klauspeterhoffmann amultikalmanfilterbasedapproachfordecodingarmkinematicsfromemgrecordings
AT hendelmohandes multikalmanfilterbasedapproachfordecodingarmkinematicsfromemgrecordings
AT seifeldawlatly multikalmanfilterbasedapproachfordecodingarmkinematicsfromemgrecordings
AT josepmarcelcardonaaudi multikalmanfilterbasedapproachfordecodingarmkinematicsfromemgrecordings
AT romanruff multikalmanfilterbasedapproachfordecodingarmkinematicsfromemgrecordings
AT klauspeterhoffmann multikalmanfilterbasedapproachfordecodingarmkinematicsfromemgrecordings