Teleoperated Robotic Arm Movement Using EMG Signal With Wearable MYO Armband

TL;DR

This study demonstrates real-time control of a 5DoF robotic arm using surface EMG signals from a wearable Myo armband, employing pattern recognition with classifiers to achieve high accuracy in recognizing hand movements.

Contribution

It introduces a system that accurately classifies seven hand movements using EMG signals and compares classifiers to optimize real-time robotic arm control.

Findings

SVM classifier achieved 96.57% accuracy

Pattern recognition system effectively distinguishes hand movements

Wireless Myo armband successfully records EMG signals for control

Abstract

The main purpose of this research is to move the robotic arm (5DoF) in real-time, based on the surface Electromyography (sEMG) signals, as obtained from the wireless Myo gesture armband to distinguish seven hand movements. The sEMG signals are biomedical signals that estimate and record the electrical signals produced in muscles through their contraction and relaxation, representing neuromuscular activities. Therefore, controlling the robotic arm via the muscles of the human arm using sEMG signals is considered to be one of the most significant methods. The wireless Myo gesture armband is used to record sEMG signals from the forearm. In order to analyze these signals, the pattern recognition system is employed, which consists of three main parts: segmentation, feature extraction, and classification. Overlap technique is chosen for segmenting part of the signal. Six time domain features (MAV, WL, RMS, AR, ZC, and SSC) are extracted from each segment. The classifiers (SVM, LDA, and KNN) are employed to enable comparison between them in order to obtain optimum accuracy of the system. The results show that the SVM achieves higher system accuracy at 96.57 %, compared to LDA reaching 96.01 %, and 92.67 % accuracy achieved by KNN.