Design of high-accuracy eight-channel surface electromyography acquisition system and its application

Mi, Chao; Zhou, Tiantong; Wei, Bin; Wang, Yi; Zou, Ling

doi:10.1140/epjst/e2018-800001-8

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Table 6 demonstrates the comparison between this work and other research work. In the wireless transmission system, the proposed four-channel discrete acquisition terminal is more flexible and can better monitor the interactions and influences between muscles at different locations than the integrated acquisition terminal [ 30 , 57 ]. The proposed system is a system not limited by the wiring between the acquisition electrodes and the processing circuitry.…”

Section: Discussionmentioning

confidence: 99%

A Surface Electromyography (sEMG) System Applied for Grip Force Monitoring

Wu,

Tian,

Zhang

et al. 2024

Sensors

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Muscles play an indispensable role in human life. Surface electromyography (sEMG), as a non-invasive method, is crucial for monitoring muscle status. It is characterized by its real-time, portable nature and is extensively utilized in sports and rehabilitation sciences. This study proposed a wireless acquisition system based on multi-channel sEMG for objective monitoring of grip force. The system consists of an sEMG acquisition module containing four-channel discrete terminals and a host computer receiver module, using Bluetooth wireless transmission. The system is portable, wearable, low-cost, and easy to operate. Leveraging the system, an experiment for grip force prediction was designed, employing the bald eagle search (BES) algorithm to enhance the Random Forest (RF) algorithm. This approach established a grip force prediction model based on dual-channel sEMG signals. As tested, the performance of acquisition terminal proceeded as follows: the gain was up to 1125 times, and the common mode rejection ratio (CMRR) remained high in the sEMG signal band range (96.94 dB (100 Hz), 84.12 dB (500 Hz)), while the performance of the grip force prediction algorithm had an R2 of 0.9215, an MAE of 1.0637, and an MSE of 1.7479. The proposed system demonstrates excellent performance in real-time signal acquisition and grip force prediction, proving to be an effective muscle status monitoring tool for rehabilitation, training, disease condition surveillance and scientific fitness applications.

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Section: Discussionmentioning

confidence: 99%

A Surface Electromyography (sEMG) System Applied for Grip Force Monitoring

Wu,

Tian,

Zhang

et al. 2024

Sensors

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show abstract

“…These events represent themselves as post-stroke epileptic seizures in the form of spontaneous high-amplitude oscillations, which appear during the first 2-3 hours after induced focal cerebral ischemia. The authors of paper [29] have developed a low-cost and portable eightchannel EMG acquisition system which consists of the related analogue front end (AFE), main control unit (MCU) and power supply system. In paper [30], fractional fuzzy entropy algorithm has been designed by combing the concept of fractional information and fuzzy entropy algorithm.…”

Section: The European Physical Journal Special Topicsmentioning

confidence: 99%