» Articles » PMID: 36557346

Study on Flexible SEMG Acquisition System and Its Application in Muscle Strength Evaluation and Hand Rehabilitation

Overview
Publisher MDPI
Date 2022 Dec 23
PMID 36557346
Authors
Affiliations
Soon will be listed here.
Abstract

Wearable devices based on surface electromyography (sEMG) to detect muscle activity can be used to assess muscle strength with the development of hand rehabilitation applications. However, conventional acquisition devices are usually complicated to operate and poorly comfortable for more medical and scientific application scenarios. Here, we report a flexible sEMG acquisition system that combines a graphene-based flexible electrode with a signal acquisition flexible printed circuit (FPC) board. Our system utilizes a polydimethylsiloxane (PDMS) substrate combined with graphene transfer technology to develop a flexible sEMG sensor. The single-lead sEMG acquisition system was designed and the FPC board was fabricated considering the requirements of flexible bending and twisting. We demonstrate the above design approach and extend this flexible sEMG acquisition system to applications for assessing muscle strength and hand rehabilitation training using a long- and short-term memory network training model trained to predict muscle strength, with 98.81% accuracy in the test set. The device exhibited good flexion and comfort characteristics. In general, the ability to accurately and imperceptibly monitor surface electromyography (EMG) signals is critical for medical professionals and patients.

Citing Articles

High-Accuracy Clock Synchronization in Low-Power Wireless sEMG Sensors.

Biagetti G, Sulis M, Falaschetti L, Crippa P Sensors (Basel). 2025; 25(3).

PMID: 39943395 PMC: 11821185. DOI: 10.3390/s25030756.


A Review on Assisted Living Using Wearable Devices.

Iadarola G, Mengarelli A, Crippa P, Fioretti S, Spinsante S Sensors (Basel). 2024; 24(23).

PMID: 39685975 PMC: 11644744. DOI: 10.3390/s24237439.


Graphene-Doped Piezoelectric Transducers by Kriging Optimal Model for Detecting Various Types of Laryngeal Movements.

Lee M, Pan C, Juan S, Wen Z, Xu J, Janesha U Micromachines (Basel). 2024; 15(10).

PMID: 39459087 PMC: 11509151. DOI: 10.3390/mi15101213.


Dynamic Analysis and Experimental Study of Lasso Transmission for Hand Rehabilitation Robot.

Lu J, Guo K, Yang H Micromachines (Basel). 2023; 14(4).

PMID: 37421091 PMC: 10146587. DOI: 10.3390/mi14040858.


Empowering Hand Rehabilitation with AI-Powered Gesture Recognition: A Study of an sEMG-Based System.

Guo K, Orban M, Lu J, Al-Quraishi M, Yang H, Elsamanty M Bioengineering (Basel). 2023; 10(5).

PMID: 37237627 PMC: 10215961. DOI: 10.3390/bioengineering10050557.


References
1.
Stefanou T, Guiraud D, Fattal C, Azevedo-Coste C, Fonseca L . Frequency-Domain sEMG Classification Using a Single Sensor. Sensors (Basel). 2022; 22(5). PMC: 8914710. DOI: 10.3390/s22051939. View

2.
Merletti R, Botter A, Troiano A, Merlo E, Minetto M . Technology and instrumentation for detection and conditioning of the surface electromyographic signal: state of the art. Clin Biomech (Bristol). 2008; 24(2):122-34. DOI: 10.1016/j.clinbiomech.2008.08.006. View

3.
Mokri C, Bamdad M, Abolghasemi V . Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques. Med Biol Eng Comput. 2022; 60(3):683-699. PMC: 8854337. DOI: 10.1007/s11517-021-02466-z. View

4.
Wininger M, Kim N, Craelius W . Pressure signature of forearm as predictor of grip force. J Rehabil Res Dev. 2008; 45(6):883-92. DOI: 10.1682/jrrd.2007.11.0187. View

5.
Phutane U, Liphardt A, Braunig J, Penner J, Klebl M, Tascilar K . Evaluation of Optical and Radar Based Motion Capturing Technologies for Characterizing Hand Movement in Rheumatoid Arthritis-A Pilot Study. Sensors (Basel). 2021; 21(4). PMC: 7914794. DOI: 10.3390/s21041208. View