Spectral Image-Based Multiday Surface Electromyography Classification of Hand Motions Using CNN for Human–Computer Interaction

Physiological signals such as electromyography (EMG) have been used in human–computer interaction (HCI) for medical applications. Wearable prostheses, such as robotic limbs, have seen a surge in popularity because of technological advancements in myoelectric interfaces. In spite of encouraging achievements with pattern-recognition-based control systems, user acceptability of prosthetic hands still needs improvement in control robustness. The purpose of this research is to compare multiday surface EMG (sEMG) recordings and measure the performance of convolutional neural network (CNN) to enhance myoelectric control. The performance metrics used in this study are accuracy, macro weighted precision (MWP), macro weighted recall (MWR), and macro ${F}1$ -score for eight able-bodied (healthy and nondisabled) and four amputee subjects. Using Mel-spectrogram-based sEMG data from both the able-bodied (healthy and nondisabled) and amputee participants, our proposed CNN has achieved a mean classification accuracy of 99.42% ± 0.42% and 98.00% ± 0.58% for the able-bodied (healthy and nondisabled) and amputee subjects for the within-day analysis, respectively. The proposed CNN outperformed other classifiers ( ${p} \leq0.05$ ) in the between-day analysis for twofold (65.88% ± 10.1% and 58.37% ± 9.11%) and for sevenfold validation (88.73% ± 1.43% and 77.35% ± 2.72%) using sEMG recordings from the able-bodied (healthy and nondisabled) and amputee subjects, respectively. The proposed CNN is compared with the pretrained transfer learning (TL) models and has achieved higher accuracy with lower computational cost. The results demonstrate that CNN can considerably increase the effectiveness of the pattern recognition myoelectric control schemes and can extract deep information from EMG data.