PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
September 29, 2025Industrial Technology Journal2 citations

Advancements in EMG Signal Processing for Prosthetic Hand Control Applications

Advancements in Electromyography (EMG) Signal Processing and Classification Techniques for Prosthetic Hand Control Applications: A Comprehensive Review

View Full Paper
Ask AI
Bookmark
Share

Authors

WAWafaa N AbdelrazikHIHamed IbrahimAEAhmed El-Bialy

Discussion

Loading...

Member takes

Overview

Comprehensive review reveals deep learning improves classification accuracy in amputee prosthetic control, suggesting better real-time implementation methods are needed.

Key Points

  • Deep learning techniques outperform traditional methods in prosthetic hand control, enhancing user experience.
  • Real-time implementation faces trade-offs, particularly regarding latency and power efficiency despite high accuracy.
  • Integration of multimodal data significantly boosts system robustness, addressing challenges in clinical applications.
  • Adaptive calibration mechanisms are essential for maintaining performance consistency and compensating for electrode stability issues.

Cite This Study

Abdelrazik et al. (2025) studied this question.

synapsesocial.com/papers/68da58dcc1728099cfd113e4https://doi.org/10.21608/itj.2025.402363.1033
View Full Paper
Ask AI
Bookmark
Share