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July 23, 2026SensorsOpen Access

A Co-Located sEMG-pFMG Dataset for Hand Gesture Recognition Under Varying Arm-Position Conditions

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Authors

SZShen ZhangHZHao ZhouRTRayane Tchantchane

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Overview

Randomized trial investigates hand gesture recognition in varying arm positions, suggesting improved algorithms for wearable technology.

Key Points

  • This research aims to create a dataset that captures sEMG and pFMG signals for hand gesture recognition across different arm postures.
  • Developed a custom co-located sEMG-pFMG armband for simultaneous signal capture.
  • Collected data under controlled static and dynamic arm postures.
  • Conducted signal quality assessments and baseline gesture recognition experiments using machine learning classifiers.
  • Demonstrated stable signal acquisition with high signal-to-noise ratios across gestures and sensing channels.
  • Provided qualitative examples showing modality-specific signal characteristics during static and dynamic conditions.
  • Established reproducible benchmarks for gesture recognition algorithms based on the collected dataset.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6a61af56faa9903c5116a36dhttps://doi.org/10.3390/s26144626
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1MyoPose: position-limb-robust neuromechanical features for enhanced hand gesture recognition in colocated sEMG–pFMG armbands2025 · 5 citations
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  3. 3Generalizable gesture recognition using magnetomyography2024 · 3 citations
  4. 4Finger joint angle and gesture estimation under natural conditions with a soft printed electrode array2025
  5. 5Study protocol for collecting synchronized multimodal EMG, forearm kinematics and finger joint-angles data in healthy adults and transradial amputees2026