Optimizing the impact of time domain segmentation techniques on upper limb EMG decoding using multimodal features

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