A new design of an electromyography sensor for movement detection in human-machine interaction systems

This study introduces a cost-effective, multi-channel electromyography (EMG) sensor that is capable of simultaneously acquiring electrical activity from up to four muscles. Given the susceptibility of EMG signals to noise sources like power line interference (PLI), muscle cross-talk, and skin movement artifacts, the proposed sensor is designed to enhance EMG signal fidelity. It employs fine-tuned analog filters and a Driven Right Leg (DRL) circuit, which attenuates PLI by applying an inverted and amplified common-mode voltage back into the subject’s body. The sensor uses electronic components, including AD620 instrumentation and TL074 operational amplifiers and an SMD PJ-131 audio jack. It features four independent acquisition channels, processing raw input with low-pass and high-pass filters to form a band-pass filter for signals in the 20–440 Hz, where muscle activity typically occurs. The two-layer PCB, powered by two 9-V batteries, achieves an average SNR of 23 dB. Experimental results confirm its effectiveness in upper limb movements, with significant noise suppression and clear muscle state differentiation. The DRL reference circuit improves SNR to 23 dB, compared to 20 dB with the traditional ground reference. The proposed EMG system costs around 10 euros in components, with lower PCB manufacturing costs at scale, offering a cost-effective alternative.

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Publication Details

Journal
Advances in Mechanical Engineering
Published
2025-05-01
DOI
https://doi.org/10.1177/16878132251338446
Citations
2
Primary Topic
Muscle activation and electromyography studies
Type
article
Field-Weighted Citation Impact
1.35

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article

A new design of an electromyography sensor for movement detection in human-machine interaction systems

Huy-Tuan Pham, Do Xuan Phu, Peiqi Tu, Thi Truc Thao Phan et al.
2 citations
Advances in Mechanical Engineering
Muscle activation and electromyography studies
1.35
article

A new design of an electromyography sensor for movement detection in human-machine interaction systems

Huy-Tuan Pham, Do Xuan Phu, Peiqi Tu, Thi Truc Thao Phan, Khang Hoang Vinh Nguyen
article en
2 citations

Abstract

This study introduces a cost-effective, multi-channel electromyography (EMG) sensor that is capable of simultaneously acquiring electrical activity from up to four muscles. Given the susceptibility of EMG signals to noise sources like power line interference (PLI), muscle cross-talk, and skin movement artifacts, the proposed sensor is designed to enhance EMG signal fidelity. It employs fine-tuned analog filters and a Driven Right Leg (DRL) circuit, which attenuates PLI by applying an inverted and amplified common-mode voltage back into the subject’s body. The sensor uses electronic components, including AD620 instrumentation and TL074 operational amplifiers and an SMD PJ-131 audio jack. It features four independent acquisition channels, processing raw input with low-pass and high-pass filters to form a band-pass filter for signals in the 20–440 Hz, where muscle activity typically occurs. The two-layer PCB, powered by two 9-V batteries, achieves an average SNR of 23 dB. Experimental results confirm its effectiveness in upper limb movements, with significant noise suppression and clear muscle state differentiation. The DRL reference circuit improves SNR to 23 dB, compared to 20 dB with the traditional ground reference. The proposed EMG system costs around 10 euros in components, with lower PCB manufacturing costs at scale, offering a cost-effective alternative.

Advances in Mechanical EngineeringVol. 17(5)
Ho Chi Minh City University of Technology and Engineering (VN), Eindhoven University of Technology (NL), Vietnamese-German University (VN)
Bộ Giáo dục và Ðào tạo
Openalex Percentile: Top 20%
Muscle activation and electromyography studies
1.35
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A new design of an electromyography sensor for movement detection in human-machine interaction systems — Huy-Tuan Pham, Do Xuan Phu, et al. · Advances in Mechanical Engineering (2025) | TGRS Research Map | TGRS