Evidence map›Paper›PMID 40141890›Full record

ArticleMicromachines2025

Design and Testing of a Portable Wireless Multi-Node sEMG System for Synchronous Muscle Signal Acquisition and Gesture Recognition.

Xiaoying Zhu, Chaoxin Li, Xiaoman Liu, Yao Tong, Chang Liu, Kai Guo

Abstract read
In one paragraph

Article in Micromachines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

6 authors.

Xiaoying ZhuDivision of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei 230026, China.ORCID 0009-0004-8382-3439
Chaoxin LiDivision of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei 230026, China.
Xiaoman LiuDepartment of Rehabilitation Medicine, The People's Hospital of Suzhou New District, Suzhou 215011, China.ORCID 0000-0001-8587-6164
Yao TongDivision of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei 230026, China.
Chang LiuDivision of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei 230026, China.
Kai GuoDivision of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei 230026, China.ORCID 0000-0003-2071-6869

Funding

National Key R&D Program of China 2023YFB4706200Natural Science Foundation Project of Chongqing 2024NSCQ-MSX0007Natural Science Foundation Project of Shandong Province ZR2022QH214Science and Technology Development Plan Project of Jilin Province 20240305049YYthe pilot projects for fundamental research in Suzhou SSD2023014
6 · The paper itself

Abstract

Surface electromyography (sEMG) is an important non-invasive method used in muscle function assessment, rehabilitation and human-machine interaction. However, existing commercial devices often lack sufficient channels, making it challenging to simultaneously acquire signals from multiple muscle sites.In this acticle, we design a portable multi-node sEMG acquisition system based on the TCP protocol to overcome the channel limitations of commercial sEMG detection devices. The system employs the STM32L442KCU6 microcontroller as the main control unit, with onboard ADC for analog-to-digital conversion of sEMG signals. Data filtered by analogy filter is transmitted via an ESP8266 WiFi module to the host computer for display and storage. By configuring Bluetooth broadcasting channels, the system can support up to 40 sEMG detection nodes. A gesture recognition algorithm is implemented to identify grasping motions with varying channel configurations. Experimental results demonstrate that with two channels, the Gradient Boosting Decision Tree (GBDT) algorithm achieves a recognition accuracy of 99.4%, effectively detecting grasping motions.

Indexed as

gesture recognitionmulti-channelsurface electromyography signalwireless transmission

Identifiers

PMID40141890
PMCPMC11944688

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.