Enhancing Myoelectric Signal Classification Through Conditional Spectral Moments and Wavelet-Enhanced Time-Domain Descriptors

This study underscores the imperative role of feature extraction and classification in the domain of electromyography (EMG) signals. The efficacy of myoelectric pattern recognition hinges upon the judicious selection of pertinent features. In this research endeavor, we introduce a novel approach that leverages two distinct feature sets: conditional spectral moments and refined time -domain descriptors, tailored to augment the precision of myoelectric signal classification. The conditional spectral moments, derived from the timefrequency distribution of EMG signals, encapsulate nuanced variations in muscle activity and movement dynamics. This augmentation facilitates seamless differentiation of hand gestures, a pivotal advancement in the context of prosthetic applications. Concurrently, we enhance the time -domain descriptors by convolving them with wavelet filter coefficients, thereby extracting both spatial and temporal characteristics of muscle activity. The extracted features are subjected to classification using Support Vector Machines (SVM), K -Nearest Neighbor (KNN), Decision Tree (DT), and Ensemble Bagging classifiers. To elucidate the efficacy of our proposed features, comprehensive experiments are conducted employing the benchmark databases Ninapro DB1 (comprising 52 classes) and DB2 (comprising 49 classes). Our findings underscore the superiority of the proposed features, particularly when applied in conjunction with ensemble classifiers. Specifically, the classification accuracies of the modified time -domain descriptor feature exhibit substantial enhancements, achieving 87.1% and 85.3% accuracy rates for Ninapro DB1 and DB2, respectively. Notably, the conditional spectral moments outperform with remarkable classification accuracies of 92.9% and 90.8% for Ninapro DB1 and DB2, respectively. This marked improvement in EMG signal classification corroborates the enhanced precision in decoding hand movements, thus imparting significant advancements in the realm of hand prosthesis control.