research-article

Decoding Arm Kinematics from EMG Signals Using Kalman Filter

Authors:

Hend ElMohandes,

Seif Eldawlatly,

Josep Marcel Cardona Audí,

Klaus-Peter HoffmannAuthors Info & Claims

ICBET '18: Proceedings of the 2018 8th International Conference on Biomedical Engineering and Technology

Pages 96 - 102

https://doi.org/10.1145/3208955.3208966

Published: 23 April 2018 Publication History

Abstract

Myoelectric control of prosthetic arms provides a new hope for providing naturalistic movements to amputees. Extensive work has been made in recent years to use Electromyography (EMG) signals to enhance the operation of prosthetic arms. In this paper, we propose an EMG Kalman filter-based model, where we identify the relationship between the joint angles and recorded EMG signals. EMG signals were recorded from biceps and triceps muscles and used to train a Kalman filter decoder. We assessed the performance of the decoder by computing the correlation and the normalized root mean-square error (NRMSE) between the decoded and actual joint angles. When decoding using biceps EMG only, an average correlation of 0.61 was obtained with a NRMSE of 0.35. For triceps EMG only, an average correlation of 0.5 was obtained with a NRMSE of 0.5. Finally, when decoding the EMG of both the biceps and triceps muscles, the average correlation increased to 0.87 while the average NRMSE decreased to 0.18. These results outperform recent studies in similar applications which indicates the efficacy of the proposed decoder in decoding joint angles from recorded EMG. This could help in enhancing the control of prosthetic arms.

References

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Cited By

Zhang HPeng BTian LSamuel OLi G(2024)Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface ElectromyographyCyborg and Bionic Systems10.34133/cbsystems.0094Online publication date: 12-Jan-2024
https://doi.org/10.34133/cbsystems.0094
Zhang HZhou XYang ZTian LZheng YLi G(2023)Trace Finger Kinematics from Surface Electromyography by Using Kalman Decoding Method2022 IEEE International Conference on Cyborg and Bionic Systems (CBS)10.1109/CBS55922.2023.10115330(153-158)Online publication date: 24-Mar-2023
https://doi.org/10.1109/CBS55922.2023.10115330
Rani GHashmi MGupta A(2023)Surface Electromyography and Artificial Intelligence for Human Activity Recognition—A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future ImplementationIEEE Access10.1109/ACCESS.2023.331650911(105140-105169)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3316509
Show More Cited By

Index Terms

Decoding Arm Kinematics from EMG Signals Using Kalman Filter
1. Applied computing
  1. Life and medical sciences

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cover image ACM Other conferences

ICBET '18: Proceedings of the 2018 8th International Conference on Biomedical Engineering and Technology

April 2018

128 pages

ISBN:9781450363693

DOI:10.1145/3208955

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 23 April 2018

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Deutscher Akademischer Austauschdienst (DAAD) German Egyptian Research short term scholarship (GERSS)

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ICBET '18

ICBET '18: 2018 8th International Conference on Biomedical Engineering and Technology

April 23 - 25, 2018

Bali, Indonesia

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Cited By

Zhang HPeng BTian LSamuel OLi G(2024)Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface ElectromyographyCyborg and Bionic Systems10.34133/cbsystems.0094Online publication date: 12-Jan-2024
https://doi.org/10.34133/cbsystems.0094
Zhang HZhou XYang ZTian LZheng YLi G(2023)Trace Finger Kinematics from Surface Electromyography by Using Kalman Decoding Method2022 IEEE International Conference on Cyborg and Bionic Systems (CBS)10.1109/CBS55922.2023.10115330(153-158)Online publication date: 24-Mar-2023
https://doi.org/10.1109/CBS55922.2023.10115330
Rani GHashmi MGupta A(2023)Surface Electromyography and Artificial Intelligence for Human Activity Recognition—A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future ImplementationIEEE Access10.1109/ACCESS.2023.331650911(105140-105169)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3316509
Gao SGong JChen BZhang BLuo FYerabakan MPan YHu B(2022)Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and InterpretationAdvanced Intelligent Systems10.1002/aisy.2022000634:10Online publication date: 14-Sep-2022
https://doi.org/10.1002/aisy.202200063
Hafid ABenouar SCherrih HAli BTalha M(2021)EMG & EIMG measurement for Arm & Hand motions using custom made instrumentation based on Raspberry PI2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH)10.1109/IHSH51661.2021.9378716(54-58)Online publication date: 9-Feb-2021
https://doi.org/10.1109/IHSH51661.2021.9378716
Gamal MMousa MEldawlatly SElbasiouny S(2021)In-silico Development and Assessment of a Kalman Filter Motor Decoder for Prosthetic Hand ControlComputers in Biology and Medicine10.1016/j.compbiomed.2021.104353(104353)Online publication date: Mar-2021
https://doi.org/10.1016/j.compbiomed.2021.104353
Singha AMugelan R(2020)A Novel Algorithm for Dynamic Control and Modeling of Myoelectric Signals for ProstheticsNanoelectronics, Circuits and Communication Systems10.1007/978-981-15-7486-3_35(383-392)Online publication date: 18-Nov-2020
https://doi.org/10.1007/978-981-15-7486-3_35

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