Surface electromyography-based gesture recognition for robot hand control

This paper presents a human-machine interaction interface for the teleoperated control of a three-finger robot hand based on surface electromyography. Based on the recorded sEMG signals, motion intention of a human operator is recognized and mapped onto corresponding predefined robot hand grasps. First, the experimental setup and underlying methodology of dataset generation are presented. Namely, 4-channel EMG data were collected from the forearm muscles of 4 healthy subjects as they performed a sequence of predefined grasps. Data processing included data filtering and segmentation, feature extraction both in time and frequency domains, data annotation, and gestures classification. Two types of classifiers (with multiple variations of parameters) were implemented and compared: (i) Support Vector Machines with multiple different kernels and (ii) K-Nearest-Neighbors. The k-cross validation procedure was combined with the holdout method to generate training, validation, and test sets. The performance of each classifier is presented in the form of a confusion matrix and a set of statistical measures, including F-measure. Finally, the integration of the proposed sEMG-based HMI interface and the selected classifier with a robot hand motion controller is presented.