Evidence map›Paper›PMID 40277571›Full record

ArticleBiosensors2025

Movement Recognition and Muscle Force Estimation of Wrist Based on Electromyographic Signals of Forearm.

Leiyu Zhang, Zhenxing Jiao, Yongzhen Li, Yawei Chang

Abstract read
In one paragraph

Article in Biosensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Leiyu ZhangBeijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China.
Zhenxing JiaoBeijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China.
Yongzhen LiInstitute for Smart Ageing, Beijing Academy of Science and Technology, Beijing 100089, China.
Yawei ChangBeijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China.

Funding

Beijing Natural Science Foundation 3252002National Natural Science Foundation of China 52375279
6 · The paper itself

Abstract

To enhance wrist impairment rehabilitation efficiency, self-rehabilitation training using healthy-side forearm sEMG was introduced, improving patient engagement and proprioception. A sEMG-based movement recognition and muscle force estimation algorithm was proposed to transmit the estimated results to a wrist rehabilitation robot. Dominant eigenvalues of raw forearm EMG signals were selected to construct a movement recognition model that included a BPNN, a voting decision, and an intensified algorithm. An experimental platform for muscle force estimation was established to measure sEMG under various loads. The linear fitting was performed between mean absolute values (

Indexed as

ElectromyographyForearmMuscle, SkeletalWristAdultAlgorithmsFemaleHumansMaleMovementmachine learningmovement recognitionmuscle force estimationwrist rehabilitation robot

Identifiers

PMID40277571
PMCPMC12025209

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.