Optimizing the impact of time domain segmentation techniques on upper limb EMG decoding using multimodal features DOI Creative Commons
Muhammad Faisal, Ikramullah Khosa, Asim Waris

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(5), P. e0322580 - e0322580

Published: May 8, 2025

Neurological disorders, such as stroke, spinal cord injury, and amyotrophic lateral sclerosis, result in significant motor function impairments, affecting millions of individuals worldwide. To address the need for innovative effective interventions, this study investigates efficacy electromyography (EMG) decoding improving outcomes. While existing literature has extensively explored classifier selection feature set optimization, choice preprocessing technique, particularly time-domain windowing techniques, remains understudied posing a knowledge gap. This presents upper limb movement classification by providing comprehensive comparison eight techniques. For purpose, EMG data from volunteers is recorded involving fifteen distinct movements fingers. The rectangular window technique among others emerged most effective, achieving accuracy 99.98% while employing 40 features L-SVM classifier, other classifiers. optimal combination implications development more accurate reliable myoelectric control systems. achieved high demonstrates feasibility using surface signals classification. study’s results have potential to improve reliability prosthetic limbs wearable sensors inform personalized rehabilitation programs. findings can contribute advancement human-computer interaction brain-computer interface technologies.

Language: Английский

Automatic Configuration of a Telerehabilitation Platform for Neurological Patients DOI
Ivana Mostachetti, Andrea Vitali, Daniele Regazzoni

et al.

Lecture notes in mechanical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 117 - 124

Published: Jan. 1, 2025

Language: Английский

Citations

0
Optimizing the impact of time domain segmentation techniques on upper limb EMG decoding using multimodal features DOI Creative Commons
Muhammad Faisal, Ikramullah Khosa, Asim Waris

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(5), P. e0322580 - e0322580

Published: May 8, 2025

Neurological disorders, such as stroke, spinal cord injury, and amyotrophic lateral sclerosis, result in significant motor function impairments, affecting millions of individuals worldwide. To address the need for innovative effective interventions, this study investigates efficacy electromyography (EMG) decoding improving outcomes. While existing literature has extensively explored classifier selection feature set optimization, choice preprocessing technique, particularly time-domain windowing techniques, remains understudied posing a knowledge gap. This presents upper limb movement classification by providing comprehensive comparison eight techniques. For purpose, EMG data from volunteers is recorded involving fifteen distinct movements fingers. The rectangular window technique among others emerged most effective, achieving accuracy 99.98% while employing 40 features L-SVM classifier, other classifiers. optimal combination implications development more accurate reliable myoelectric control systems. achieved high demonstrates feasibility using surface signals classification. study’s results have potential to improve reliability prosthetic limbs wearable sensors inform personalized rehabilitation programs. findings can contribute advancement human-computer interaction brain-computer interface technologies.

Language: Английский

Citations

0