Myo Transformer Signal Classification for an Anthropomorphic Robotic Hand

Abstract

The evolution of anthropomorphic robotic hands (ARH) in recent years has been sizable, employing control techniques based on machine learning classifiers for myoelectric signal processing. This work introduces an innovative multi-channel bio-signal transformer (MuCBiT) for surface electromyography (EMG) signal recognition and classification. The proposed MuCBiT is an artificial neural network based on fully connected layers and transformer architecture. The MuCBiT recognizes and classifies EMG signals sensed from electrodes patched over the arm’s surface. The MuCBiT classifier was trained and validated using a collected dataset of four hand gestures across ten users. Despite the smaller size of the dataset, the MuCBiT achieved a prediction accuracy of 86.25%, outperforming traditional machine learning models and other transformer-based classifiers for EMG signal classification. This integrative transformer-based gesture recognition promises notable advancements for ARH development, underscoring prospective improvements in prosthetics and human–robot interaction.

Keywords

• convolutional transformer
• deep learning
• bionic prosthetics
• EMG data classification
• anthropomorphic robotic hand