Evidence map›Paper›PMID 35845759›Full record

ArticleFrontiers in neurorobotics2022

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

Biao Chen, Chaoyang Chen, Jie Hu, Thomas Nguyen, Jin Qi, Banghua Yang, Dawei Chen, Yousef Alshahrani, Yang Zhou, Andrew Tsai and 2 more

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In one paragraph

Article in Frontiers in neurorobotics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Biao ChenState Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China.
Chaoyang ChenDepartment of Biomedical Engineering, Wayne State University, Detroit, MI, United States.
Jie HuState Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China.
Thomas NguyenOrthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI, United States.
Jin QiState Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China.
Banghua YangResearch Center of Brain Computer Engineering, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China.
Dawei ChenDepartment of Biomedical Engineering, Wayne State University, Detroit, MI, United States.
Yousef AlshahraniDepartment of Biomedical Engineering, Wayne State University, Detroit, MI, United States.
Yang ZhouDepartment of Biomedical Engineering, Wayne State University, Detroit, MI, United States.
Andrew TsaiOrthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI, United States.
Todd FrushOrthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI, United States.
Henry GoitzOrthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

embedded systemfixed bandwidthfrequency domainmyoelectric signalreal-time controlrobotic handshort-time fourier transform

Identifiers

PMID35845759
PMCPMC9280080

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.