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

被引：4

作者：

Chen, Biao ^{[1
,2
]}

Chen, Chaoyang ^{[2
,3
]}

Hu, Jie ^{[1
]}

Nguyen, Thomas ^{[3
]}

Qi, Jin ^{[1
]}

Yang, Banghua ^{[4
]}

Chen, Dawei ^{[2
]}

Alshahrani, Yousef ^{[2
,5
]}

Zhou, Yang ^{[2
]}

Tsai, Andrew ^{[3
]}

Frush, Todd ^{[3
]}

Goitz, Henry ^{[3
]}

机构：

[1] Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Shanghai, Peoples R China

[2] Wayne State Univ, Dept Biomed Engn, Detroit, MI 48201 USA

[3] Detroit Med Ctr, Orthopaed Surg & Sports Med, Detroit, MI 48201 USA

[4] Shanghai Univ, Res Ctr Brain Comp Engn, Sch Mechatron Engn & Automat, Shanghai, Peoples R China

[5] Taibah Univ, Prosthet & Assist Devices Dept, Medina, Saudi Arabia

来源：

FRONTIERS IN NEUROROBOTICS | 2022年 / 16卷

基金：

中国国家自然科学基金;

关键词：

short-time fourier transform; real-time control; robotic hand; embedded system; frequency domain; fixed bandwidth; myoelectric signal; RECOGNITION; EXOSKELETON; SEMG;

D O I：

10.3389/fnbot.2022.880073

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页数：15

共 42 条

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