Muscle Fatigue Analysis and Stress Detection from Surface EMG and ECG Using Deep Learning for Upper-Limb Trauma Rehabilitation

Kassaw, Ewunate; Worassa, Emnet; Fetene, Kinde; Aboye, Genet

doi:10.21203/rs.3.rs-3146192/v1

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…The physiotherapy clinics at Jimma University and Droga Physiotherapy Clinic in Addis Abeba provided the materials needed to carry out the study. A preprint has previously been published [20].…”

Section: For Publicationmentioning

confidence: 99%

Muscle Fatigue Analysis and Stress Detection from Surface EMG and ECG Data Obtained Using Deep Learning for Upper-Limb Trauma Rehabilitation

Kebede

2024

Preprint

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Background The repetitive nature of physical rehabilitation may result in excess muscular fatigue, which can adversely impact an individual's motor function, leading to discomfort or even physical injury. Moreover, individuals who have experienced trauma tend to encounter difficulties concentrating, which can significantly impede their physical capabilities. Regrettably, existing therapeutic approaches do not appear to consider the potential mental exhaustion of patients. This study aimed to create a bidirectional long short-term memory (Bi-LSTM) model for assessing muscle fatigue stage and mental stress conditions during physical rehabilitation of trauma-injured patients. Methods Data corresponding to 188 EMG signals and 223 ECG signals were collected from the Jimma University physiotherapy clinic and prepared for signal processing. Since the 4th-order Butterworth filter performs better than the other filters, it was chosen to denoise the data. The data were then split at a ratio of 60:20:20 to train, validate, and test the data. Finally, the developed Bi-LSTM model was deployed. Results The Bi-LSTM model achieved an accuracy of 95% for multiclass muscle fatigue classification, and 97% accuracy was achieved for the binary classification of mental stress. The GUI provides a setting appropriate for routine model usage. Conclusion The results indicate that monitoring the muscle condition and mental status of traumatized patients can be performed in a clinical setting for effective physical rehabilitation.

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Section: For Publicationmentioning

confidence: 99%

Muscle Fatigue Analysis and Stress Detection from Surface EMG and ECG Data Obtained Using Deep Learning for Upper-Limb Trauma Rehabilitation

Kebede

2024

Preprint

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

Suhaimi,

Ghazali,

Haja Mohideen

et al. 2024

IIUMEJ

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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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Muscle Fatigue Analysis and Stress Detection from Surface EMG and ECG Using Deep Learning for Upper-Limb Trauma Rehabilitation

Cited by 2 publications

References 17 publications

Muscle Fatigue Analysis and Stress Detection from Surface EMG and ECG Data Obtained Using Deep Learning for Upper-Limb Trauma Rehabilitation

Muscle Fatigue Analysis and Stress Detection from Surface EMG and ECG Data Obtained Using Deep Learning for Upper-Limb Trauma Rehabilitation

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

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