Evidence map›Paper›PMID 41531754›Full record

ArticleFrontiers in neuroscience2025

Influence of electrode placement on the recognition of different gesture categories using high-density sEMG.

Fang Qiu, Xiaodong Liu, Xinming Ye

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

2 citing papers in PubMed.

  1. Article
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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

3 authors.

Fang QiuSchool of Physical Education and Health, Shanghai University of International Business and Economics, Shanghai, China.
Xiaodong LiuInstitute of Physical Education, Guangxi University of Science and Technology, Liuzhou, China.
Xinming YeSchool of Sports Science and Engineering, East China University of Science and Technology, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-density surface electromyography (HD-sEMG)-based gesture recognition serves as a critical interface for human-computer interaction (HCI). However, recognition accuracy exhibits a significant dependency on gesture complexity and electrode positioning. To address this, we systematically investigated the relationship between gesture types and sEMG electrode placement locations through intra-subject, inter-day, and inter-subject validation protocols. Two distinct gesture categories were analyzed, i.e., single-degree-of-freedom (single-DoF) gestures and daily-used multi-finger synergistic gestures. Using an open-access gesture dataset, HD-sEMG signals were acquired from three forearm regions: the distal wrist, mid-forearm, and proximal elbow, separately. Classification results using support vector machine (SVM) revealed that single-DoF gestures achieved peak accuracy with distal wrist signals (98.63% for intra-subject, 79.73% for inter-day, and 75.47% for inter-subject validation protocols), whereas daily-used gestures performed optimally with signals from the mid-forearm and proximal elbow regions. These findings demonstrate the specific relationship between electrode placement and gesture type, providing valuable insights for EMG-HCI design and sensor placement strategies based on the nature of the target gesture.

Indexed as

gesture categoryhand gesture recognitionHD-sEMGhuman-machine interactionsampling positionSVM

Identifiers

PMID41531754
PMCPMC12794296

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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.