Enhancing Prosthetic Control: Neural Network Classification of Thumb Muscle Contraction Using HD-sEMG Signals

Muhammad Mukhlis Suhaimi; Aimi Shazwani  Ghazali; Ahmad Jazlan Haja Mohideen; Muhammad Hariz Hafizalshah; Shahrul Na'im Sidek

doi:10.31436/iiumej.v25i2.3029

Authors

Muhammad Mukhlis Suhaimi International Islamic University Malaysia
Aimi Shazwani Ghazali IIUM https://orcid.org/0000-0001-5042-3536
Ahmad Jazlan Haja Mohideen International Islamic University Malaysia https://orcid.org/0000-0002-4910-456X
Muhammad Hariz Hafizalshah https://orcid.org/0000-0003-0805-8031
Shahrul Na'im Sidek International Islamic University Malaysia https://orcid.org/0000-0002-3204-1347

DOI:

https://doi.org/10.31436/iiumej.v25i2.3029

Keywords:

Thumb Posture, High-Density Surface Electromyogram (HD-sEMG), Forearm Muscle, Classification

Abstract

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.

ABSTRAK: 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.

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