Instruction transmission of combat robotic arm based on neck muscles surface electromyography signals

Abstract

This paper introduces a novel method for instructing wearable robotic arms based on neck movements to enhance user control. By capturing head movements representing four aiming directions and analysing surface electromyography (sEMG) signals from specific muscles, the study employs the random forest algorithm for movement recognition. Results show that utilising sEMG signals from six muscles improve classification accuracy, and applying the variational mode decomposition (VMD) algorithm enhances feature extraction. Notably, right-side muscles, particularly the sternocleidomastoid muscles, significantly impact movement classification. These findings provide theoretical support for better recognition of neck movements.