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

**Authors:** Fang Qiu, Xiaodong Liu, Xinming Ye

PMC · DOI: 10.3389/fnins.2025.1750792 · Frontiers in Neuroscience · 2025-12-29

## TL;DR

This study shows how placing electrodes on different forearm areas affects how well gestures can be recognized using muscle signals.

## Contribution

The study reveals how electrode placement affects gesture recognition accuracy for different gesture types using HD-sEMG.

## Key findings

- Single-DoF gestures achieved highest accuracy with distal wrist electrode placement.
- Multi-finger gestures performed best with mid-forearm and proximal elbow signals.
- Recognition accuracy varied significantly based on electrode placement and gesture type.

## 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.

## Full-text entities

- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/PMC12794296/full.md

## Figures

10 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12794296/full.md

## References

45 references — full list in the complete paper: https://tomesphere.com/paper/PMC12794296/full.md

---
Source: https://tomesphere.com/paper/PMC12794296