Development of a High-SNR Stochastic sEMG Processor in a Multiple Muscle Elbow Joint
In the robotics and rehabilitation engineering fields, surface electromyography (sEMG) signals have been widely studied to estimate muscle activation and utilized as control inputs for robotic devices because of their advantageous noninvasiveness. However, the stochastic property of sEMG results in...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10146313/ |
| _version_ | 1797804087577673728 |
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| author | Handdeut Chang Seulki Kyeong Youngjin Na Yeongjin Kim Jung Kim |
| author_facet | Handdeut Chang Seulki Kyeong Youngjin Na Yeongjin Kim Jung Kim |
| author_sort | Handdeut Chang |
| collection | DOAJ |
| description | In the robotics and rehabilitation engineering fields, surface electromyography (sEMG) signals have been widely studied to estimate muscle activation and utilized as control inputs for robotic devices because of their advantageous noninvasiveness. However, the stochastic property of sEMG results in a low signal-to-noise ratio (SNR) and impedes sEMG from being used as a stable and continuous control input for robotic devices. As a traditional method, time-average filters (e.g., low-pass filters) can improve the SNR of sEMG, but time-average filters suffer from latency problems, making real-time robot control difficult. In this study, we propose a stochastic myoprocessor using a rescaling method extended from a whitening method used in previous studies to enhance the SNR of sEMG without the latency problem that affects traditional time average filter-based myoprocessors. The developed stochastic myoprocessor uses 16 channel electrodes to use the ensemble average, 8 of which are used to measure and decompose deep muscle activation. To validate the performance of the developed myoprocessor, the elbow joint is selected, and the flexion torque is estimated. The experimental results indicate that the estimation results of the developed myoprocessor show an RMS error of 6.17[%], which is an improvement with respect to previous methods. Thus, the rescaling method with multichannel electrodes proposed in this study is promising and can be applied in robotic rehabilitation engineering to generate rapid and accurate control input for robotic devices. |
| first_indexed | 2024-03-13T05:31:52Z |
| format | Article |
| id | doaj.art-cc05f4f0493e424da7fe0486007d063a |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:31:52Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-cc05f4f0493e424da7fe0486007d063a2023-06-14T23:00:08ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102023-01-01312654266410.1109/TNSRE.2023.328141010146313Development of a High-SNR Stochastic sEMG Processor in a Multiple Muscle Elbow JointHanddeut Chang0https://orcid.org/0000-0002-8026-4254Seulki Kyeong1Youngjin Na2https://orcid.org/0000-0002-0393-8622Yeongjin Kim3https://orcid.org/0000-0001-5130-001XJung Kim4https://orcid.org/0000-0002-1825-6325Department of Mechanical Engineering, Incheon National University, Incheon, South KoreaDepartment of Mechanical Engineering, Hannam University, Daejeon, South KoreaDepartment of Mechanical Systems Engineering, Sookmyung Women’s University, Seoul, South KoreaDepartment of Mechanical Engineering, Incheon National University, Incheon, South KoreaKorea Advanced Institute of Science and Technology, Daejeon, South KoreaIn the robotics and rehabilitation engineering fields, surface electromyography (sEMG) signals have been widely studied to estimate muscle activation and utilized as control inputs for robotic devices because of their advantageous noninvasiveness. However, the stochastic property of sEMG results in a low signal-to-noise ratio (SNR) and impedes sEMG from being used as a stable and continuous control input for robotic devices. As a traditional method, time-average filters (e.g., low-pass filters) can improve the SNR of sEMG, but time-average filters suffer from latency problems, making real-time robot control difficult. In this study, we propose a stochastic myoprocessor using a rescaling method extended from a whitening method used in previous studies to enhance the SNR of sEMG without the latency problem that affects traditional time average filter-based myoprocessors. The developed stochastic myoprocessor uses 16 channel electrodes to use the ensemble average, 8 of which are used to measure and decompose deep muscle activation. To validate the performance of the developed myoprocessor, the elbow joint is selected, and the flexion torque is estimated. The experimental results indicate that the estimation results of the developed myoprocessor show an RMS error of 6.17[%], which is an improvement with respect to previous methods. Thus, the rescaling method with multichannel electrodes proposed in this study is promising and can be applied in robotic rehabilitation engineering to generate rapid and accurate control input for robotic devices.https://ieeexplore.ieee.org/document/10146313/Surface electromyographydecompositionelbowtorque estimationrescaling method |
| spellingShingle | Handdeut Chang Seulki Kyeong Youngjin Na Yeongjin Kim Jung Kim Development of a High-SNR Stochastic sEMG Processor in a Multiple Muscle Elbow Joint IEEE Transactions on Neural Systems and Rehabilitation Engineering Surface electromyography decomposition elbow torque estimation rescaling method |
| title | Development of a High-SNR Stochastic sEMG Processor in a Multiple Muscle Elbow Joint |
| title_full | Development of a High-SNR Stochastic sEMG Processor in a Multiple Muscle Elbow Joint |
| title_fullStr | Development of a High-SNR Stochastic sEMG Processor in a Multiple Muscle Elbow Joint |
| title_full_unstemmed | Development of a High-SNR Stochastic sEMG Processor in a Multiple Muscle Elbow Joint |
| title_short | Development of a High-SNR Stochastic sEMG Processor in a Multiple Muscle Elbow Joint |
| title_sort | development of a high snr stochastic semg processor in a multiple muscle elbow joint |
| topic | Surface electromyography decomposition elbow torque estimation rescaling method |
| url | https://ieeexplore.ieee.org/document/10146313/ |
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