Improving Myoelectric Hand Gesture Recognition using Multiple High-density Maps

The identification of human motion intention through electromyography (EMG) signals is an important area of development in human–robot interaction. This technology aids amputees in controlling their prosthetic limbs in a more intuitive manner, facilitating the execution of daily activities. However, hand amputees face challenges in using dexterous prostheses due to control difficulties and low robustness in real-life situations. This study aims to enhance the accuracy of EMG gesture recognition by extracting spatial characteristics via multiple high density (HD) maps. A total of five HD-maps are generated utilizing the root mean square value (RMS), mean absolute value (MAV), zero crossings (ZC), sign slope changes (SSC), and waveform length (WL) features. The influence of each distinct HD-map, along with the synergistic effect of numerous HD-maps in the extraction of intensity features, is assessed with regard to its impact on classification accuracy. Three machine learning classifiers are employed to categorize nine hand movements of the Ninapro (DB5) dataset. The results show that features extracted from the combination of multiple HD-maps (CMHD) achieved a high accuracy in comparison to those of individual HD-maps. Moreover, the proposed features are superior to those of conventional TD features. The error rate is reduced by approximately 7.76% relative to time domain (TD) features. The results obtained confirm the significance of spatial features extracted from multiple HD-maps that ensure consistent information in different EMG channels