A Real-Time EMG-Based Fixed-Bandwidth Frequency-Domain Embedded System for Robotic Hand

Abstract

The signals from electromyography (EMG) have been used for volitional control of robotic assistive devices with the challenges of performance improvement. Currently, the most common method of EMG signal processing for robot control is RMS (root mean square)-based algorithm, but system performance accuracy can be affected by noise or artifacts. This study hypothesized that the frequency bandwidths of noise and artifacts are beyond the main EMG signal frequency bandwidth, hence the fixed-bandwidth frequency-domain signal processing methods can filter off the noise and artifacts only by processing the main frequency bandwidth of EMG signals for robot control. The purpose of this study was to develop a cost-effective embedded system and short-time Fourier transform (STFT) method for an EMG-controlled robotic hand. Healthy volunteers were recruited in this study to identify the optimal myoelectric signal frequency bandwidth of muscle contractions. The STFT embedded system was developed using the STM32 microcontroller unit (MCU). The performance of the STFT embedded system was compared with RMS embedded system. The results showed that the optimal myoelectric signal frequency band responding to muscle contractions was between 60 and 80 Hz. The STFT embedded system was more stable than the RMS embedded system in detecting muscle contraction. Onsite calibration was required for RMS embedded system. The average accuracy of the STFT embedded system is 91.55%. This study presents a novel approach for developing a cost-effective and less complex embedded myoelectric signal processing system for robot control.

On the Decoding of Shoulder Joint Intent of Motion From Transient EMG: Feature Evaluation and Classification

“…Firstly, we relied on pattern recognition architectures, i.e. multinomial logistic regression and support vector machine, that can be easily implemented in micro-controllers [32], without requiring the high computational burden typically needed in practical contexts [33], [34].…”

Factors affecting real‐time evaluation of muscle function in smart rehab systems

“…For all time-and feature-domain measures, the measurement cycle was 1 kHz, and all features were calculated in parallel. Related studies [22][23][24][34][35][36][37][38][39] focused on pattern recognition accuracy between movements and predictions. In contrast, we aimed to measure the difference between paralyzed and normal functions.…”

“…Unlike previous RT processing studies [34][35][36][37][38][39], we found optimal values by adjusting window and shift sizes and considered the computer system capabilities related to each. In Table 3, "F" means "fail," "P" means "pass," and the number represents the number of samples.…”

Control technologies of lower limb rehabilitation exoskeleton robots based on surface electromyography: A review

“…Karheily et al used the STFT, the continuous WT, and Stockwell time-frequency domain characterization to classify hand motion, and reported an accuracy of 90.05%, 89.92%, and 90.96%, respectively [24]. Chen et al employed the STFT method to establish a STFT embedded system that detects muscle contraction, and reported an accuracy of up to 91.55% [25].…”