Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
November 21, 2025Scientific ReportsOpen Access

Generalizable gesture classification of HDsEMG using volume representations of muscles averaged across multiple individuals

View Full Paper
Ask AI
Bookmark
Share

Authors

JLJonathan LundsbergABAnders BjörkmanNMNebojsa Malesevic

Discussion

Loading...

Member takes

Overview

Analysis shows true positive rates of up to 95% in gesture classification, indicating EMG could effectively generalize gesture recognition.

Key Points

  • True positive rates reached as high as 95.1%, demonstrating effective gesture classification using volume representations of muscles.
  • False positive rates varied from 0% to 24.1%, suggesting the model's reliability in differentiating hand gestures.
  • Evaluation involved high-density surface electromyography data and a leave-one-out approach for inter-subject generalizability.
  • Potential for widespread application as the model requires no individual-specific data for effective gesture classification.

Cite This Study

Lundsberg et al. (2025) studied this question.

synapsesocial.com/papers/6924e3ffc0ce034ddc34f602https://doi.org/10.1038/s41598-025-28215-y
View Full Paper
Ask AI
Bookmark
Share

Also Consider

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

  1. 1Hand Gesture Classification using sEMG Signals and Ensemble Learning2024
  2. 2HD-sEMG Feature Decomposition via Muscle Synergy and Dissimilarity Metric Learning for Robustness Against Unknown Gestures2026
  3. 3Generalizable gesture recognition using magnetomyography2024 · 3 citations
  4. 4Optimization of inter-subject sEMG-based hand gesture recognition tasks using unsupervised domain adaptation techniques2024 · 27 citations
  5. 5Unveiling EMG semantics: a prototype-learning approach to generalizable gesture classification2024 · 3 citations