Attention-fused residual transformer CNN for robust lower limb movement recognition - A. Anitha; D. Jeraldin Auxillia (Automatika)
Detecting lower limb movements from surface electromyography (sEMG) signals has received more attention, because of its importance in prosthetic control, robotic applications and medical rehabilitation. sEMG signals offer a non-invasive and accurate method to recognize movement intent. Conventional machine-learning approaches depend on manual feature extraction, which consumes more time and is susceptible to noise interference and class imbalance. To address these challenges, a new framework that combines an Attention-Fused Residual-Transformer Convolutional Neural Network (AF-RT-CNN) is proposed. Data augmentation is applied to create more samples for minority classes, addressing class imbalance problems and improving recognition reliability. The AF-RT-CNN architecture combines residual blocks, attention mechanism and Transformer Encoder aiding robust feature extraction, good generalization capability and pattern recognition. The proposed framework was evaluated across 11 healthy individuals and 11 patients with lower limb impairments, across three distinct categories of lower limb movements sit, stand and gait. The method achieved a remarkable accuracy of 99.35% for the healthy group and 99.54% for the pathological group. When combining both groups, the overall accuracy reached 98.74%. The results indicate the effectiveness of the proposed approach in rehabilitation and assistive technology for lower limb motor control.