RESUMEN
Electromyography (EMG) signals are primarily used to control prosthetic hands. Classifying hand gestures efficiently with EMG signals presents numerous challenges. In addition to overcoming these challenges, a successful combination of feature extraction and classification approaches will improve classification accuracy. In the current work, convolutional neural network (CNN) features are used to reduce the redundancy problems associated with time and frequency domain features to improve classification accuracy. The features from the EMG signal are extracted using a CNN and are fed to the 'k' nearest neighbor (KNN) classifier with a different number of neighbors (1NN,3NN,5NN,and 7NN). It results in an ensemble of classifiers that are combined using a hard voting-based classifier. Based on the benchmark Ninapro DB4 database and CapgMyo database, the proposed framework obtained 91.3% classification accuracy on CapgMyo and 89.5% on Ninapro DB4.
RESUMEN
A classification framework for hand gestures using Electromyography (EMG) signals in prosthetic hands is presented. Leveraging the multi-scale characteristics and temporal nature of EMG signals, a Convolutional Neural Network (CNN) is used to extract multi-scale features and classify them with spatial-temporal attention. A multi-scale coarse-grained layer introduced into the input of one-dimensional CNN (1D-CNN) facilitates multi-scale feature extraction. The multi-scale features are fed into the attention layer and subsequently given to the fully connected layer to perform classification. The proposed model achieves classification accuracies of 93.4%, 92.8%, 91.3%, and 94.1% for Ninapro DB1, DB2, DB5, and DB7 respectively, thereby enhancing the confidence of prosthetic hand users.