Enhancing prosthetic control: neural network classification of thumb muscle contraction using HD-sEMG signals

The progression of prosthetic technology, enabling precise thumb control and movement, has reached a stage where noninvasive techniques for capturing bioelectrical signals from muscle activity are preferred over alternative methods. While electromyography's applications extend beyond just inter...

Full description

Saved in:
Bibliographic Details
Main Authors: Suhaimi, Muhammad Mukhlis, Ghazali, Aimi Shazwani, Haja Mohideen, Ahmad Jazlan, Hafizalshah, Muhammad Hariz, Sidek, Shahrul Na'im
Format: Article
Language:English
English
English
Published: IIUM Press 2024
Subjects:
Online Access:http://irep.iium.edu.my/113455/1/113455_Enhancing%20prosthetic%20control.pdf
http://irep.iium.edu.my/113455/7/113455_Enhancing%20prosthetic%20control_SCOPUS.pdf
http://irep.iium.edu.my/113455/8/113455_Enhancing%20prosthetic%20control_WOS.pdf
http://irep.iium.edu.my/113455/
https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/3029
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.iium.irep.113455
record_format dspace
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
English
topic TK7885 Computer engineering
spellingShingle TK7885 Computer engineering
Suhaimi, Muhammad Mukhlis
Ghazali, Aimi Shazwani
Haja Mohideen, Ahmad Jazlan
Hafizalshah, Muhammad Hariz
Sidek, Shahrul Na'im
Enhancing prosthetic control: neural network classification of thumb muscle contraction using HD-sEMG signals
description The progression of prosthetic technology, enabling precise thumb control and movement, has reached a stage where noninvasive techniques for capturing bioelectrical signals from muscle activity are preferred over alternative methods. While electromyography's applications extend beyond just interfacing with prostheses, this initial investigation delves into evaluating various classifiers' accuracy in identifying rest and contraction states of the thumb muscles using extrinsic forearm readings. Employing a High-Density Surface Electromyogram (HD-sEMG) device, bioelectrical signals generated by muscle activity, detectable from the skin's surface, were transformed into contours. A training system for the thumb induced muscle activity in four postures: 0°, 30°, 60°, and 90°. The collection of HD-sEMG signals originating from both the anterior and posterior forearms of seventeen participants has been proficiently classified using a neural network with 100% accuracy and a mean square error (MSE) of 1.4923 x 10-5 based on the testing dataset. This accomplishment in classification was realized by employing the Bayesian regularization backpropagation (trainbr) training technique, integrating seven concealed layers, and adopting a training-validation-testing proportion of 70-15-15. In the realm of future research, an avenue worth exploring involves the potential integration of real-time feedback mechanisms predicated on the recognition of thumb muscle contraction states. This integration could offer an enhanced interaction experience between users and prosthetic devices. ***** Perkembangan teknologi prostetik mengguna pakai kaedah selamat iaitu isyarat bioelektrikal yang diperoleh dari pergerakan otot lebih digemari digunakan berbanding kaedah alternatif. Ini membolehkan kawalan dan pergerakan ibu jari dengan tepat. Sementara aplikasi elektromiografi telah melangkah jauh melebihi antara muka prostesis. Kajian awal ini mengkaji pelbagai ketepatan klasifikasi dalam mengenal pasti keadaan rehat dan kontraksi otot ibu jari menggunakan bacaan lengan bawah ekstrinsik. Dengan menggunakan peranti Elektromiogram Permukaan Kepadatan-Tinggi (HD-sEMG), isyarat bioelektrikal yang terhasil dari pergerakan otot, boleh ditanggalkan dari permukaan kulit, di ubah kepada kontur. Sistem latihan pada ibu jari menghasilkan pergerakan otot dalam empat postur iaitu: 0°, 30°, 60°, dan 90°. Isyarat terkumpul dari HD-sEMG berasal dari kedua-dua lengan tangan anterior dan posterior dari 17 peserta telah diklasifikasi dengan cekap menggunakan rangkaian neural dengan ketepatan 100% dan min kuasa dua ralat (MSE) sebanyak 1.4923 x 10-5 berdasarkan setdata yang diuji. Klasifikasi sempurna ini dicapai dengan menggunakan teknik latihan aturan rambatan-belakang Bayesian (trainbr), mengguna pakai tujuh lapisan tersembunyi dengan gabungan latihan-validasi-ujian mengikut kadar 70-15-15. Pada masa hadapan, pengkaji boleh menerokai potensi integrasi mekanisme tindak balas nyata dalam meramal dan mengenali kontraksi otot ibu jari. Integrasi ini mungkin membolehkan pengalaman interaksi antara peranti prostetik dan pengguna.
format Article
author Suhaimi, Muhammad Mukhlis
Ghazali, Aimi Shazwani
Haja Mohideen, Ahmad Jazlan
Hafizalshah, Muhammad Hariz
Sidek, Shahrul Na'im
author_facet Suhaimi, Muhammad Mukhlis
Ghazali, Aimi Shazwani
Haja Mohideen, Ahmad Jazlan
Hafizalshah, Muhammad Hariz
Sidek, Shahrul Na'im
author_sort Suhaimi, Muhammad Mukhlis
title Enhancing prosthetic control: neural network classification of thumb muscle contraction using HD-sEMG signals
title_short Enhancing prosthetic control: neural network classification of thumb muscle contraction using HD-sEMG signals
title_full Enhancing prosthetic control: neural network classification of thumb muscle contraction using HD-sEMG signals
title_fullStr Enhancing prosthetic control: neural network classification of thumb muscle contraction using HD-sEMG signals
title_full_unstemmed Enhancing prosthetic control: neural network classification of thumb muscle contraction using HD-sEMG signals
title_sort enhancing prosthetic control: neural network classification of thumb muscle contraction using hd-semg signals
publisher IIUM Press
publishDate 2024
url http://irep.iium.edu.my/113455/1/113455_Enhancing%20prosthetic%20control.pdf
http://irep.iium.edu.my/113455/7/113455_Enhancing%20prosthetic%20control_SCOPUS.pdf
http://irep.iium.edu.my/113455/8/113455_Enhancing%20prosthetic%20control_WOS.pdf
http://irep.iium.edu.my/113455/
https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/3029
_version_ 1806688382640717824
spelling my.iium.irep.1134552024-08-06T01:23:49Z http://irep.iium.edu.my/113455/ Enhancing prosthetic control: neural network classification of thumb muscle contraction using HD-sEMG signals Suhaimi, Muhammad Mukhlis Ghazali, Aimi Shazwani Haja Mohideen, Ahmad Jazlan Hafizalshah, Muhammad Hariz Sidek, Shahrul Na'im TK7885 Computer engineering The progression of prosthetic technology, enabling precise thumb control and movement, has reached a stage where noninvasive techniques for capturing bioelectrical signals from muscle activity are preferred over alternative methods. While electromyography's applications extend beyond just interfacing with prostheses, this initial investigation delves into evaluating various classifiers' accuracy in identifying rest and contraction states of the thumb muscles using extrinsic forearm readings. Employing a High-Density Surface Electromyogram (HD-sEMG) device, bioelectrical signals generated by muscle activity, detectable from the skin's surface, were transformed into contours. A training system for the thumb induced muscle activity in four postures: 0°, 30°, 60°, and 90°. The collection of HD-sEMG signals originating from both the anterior and posterior forearms of seventeen participants has been proficiently classified using a neural network with 100% accuracy and a mean square error (MSE) of 1.4923 x 10-5 based on the testing dataset. This accomplishment in classification was realized by employing the Bayesian regularization backpropagation (trainbr) training technique, integrating seven concealed layers, and adopting a training-validation-testing proportion of 70-15-15. In the realm of future research, an avenue worth exploring involves the potential integration of real-time feedback mechanisms predicated on the recognition of thumb muscle contraction states. This integration could offer an enhanced interaction experience between users and prosthetic devices. ***** Perkembangan teknologi prostetik mengguna pakai kaedah selamat iaitu isyarat bioelektrikal yang diperoleh dari pergerakan otot lebih digemari digunakan berbanding kaedah alternatif. Ini membolehkan kawalan dan pergerakan ibu jari dengan tepat. Sementara aplikasi elektromiografi telah melangkah jauh melebihi antara muka prostesis. Kajian awal ini mengkaji pelbagai ketepatan klasifikasi dalam mengenal pasti keadaan rehat dan kontraksi otot ibu jari menggunakan bacaan lengan bawah ekstrinsik. Dengan menggunakan peranti Elektromiogram Permukaan Kepadatan-Tinggi (HD-sEMG), isyarat bioelektrikal yang terhasil dari pergerakan otot, boleh ditanggalkan dari permukaan kulit, di ubah kepada kontur. Sistem latihan pada ibu jari menghasilkan pergerakan otot dalam empat postur iaitu: 0°, 30°, 60°, dan 90°. Isyarat terkumpul dari HD-sEMG berasal dari kedua-dua lengan tangan anterior dan posterior dari 17 peserta telah diklasifikasi dengan cekap menggunakan rangkaian neural dengan ketepatan 100% dan min kuasa dua ralat (MSE) sebanyak 1.4923 x 10-5 berdasarkan setdata yang diuji. Klasifikasi sempurna ini dicapai dengan menggunakan teknik latihan aturan rambatan-belakang Bayesian (trainbr), mengguna pakai tujuh lapisan tersembunyi dengan gabungan latihan-validasi-ujian mengikut kadar 70-15-15. Pada masa hadapan, pengkaji boleh menerokai potensi integrasi mekanisme tindak balas nyata dalam meramal dan mengenali kontraksi otot ibu jari. Integrasi ini mungkin membolehkan pengalaman interaksi antara peranti prostetik dan pengguna. IIUM Press 2024-07-14 Article PeerReviewed application/pdf en http://irep.iium.edu.my/113455/1/113455_Enhancing%20prosthetic%20control.pdf application/pdf en http://irep.iium.edu.my/113455/7/113455_Enhancing%20prosthetic%20control_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/113455/8/113455_Enhancing%20prosthetic%20control_WOS.pdf Suhaimi, Muhammad Mukhlis and Ghazali, Aimi Shazwani and Haja Mohideen, Ahmad Jazlan and Hafizalshah, Muhammad Hariz and Sidek, Shahrul Na'im (2024) Enhancing prosthetic control: neural network classification of thumb muscle contraction using HD-sEMG signals. IIUM Engineering Journal, 25 (2). pp. 338-349. ISSN 1511-788X E-ISSN 2289-7860 https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/3029 10.31436/iiumej.v25i2.3029
score 13.18916