A Novel Bilateral Data Fusion Approach for EMG-Driven Deep Learning in Post-Stroke Paretic Gesture Recognition

A Novel Bilateral Data Fusion Approach for EMG-Driven Deep Learning in Post-Stroke Paretic Gesture Recognition

We introduce a hybrid deep learning model for recognizing hand gestures from electromyography (EMG) signals in subacute stroke patients: the one-dimensional convolutional long short-term memory neural network (CNN-LSTM). The proposed network was trained, tested, and cross-validated on seven hand ges...

Full description

Saved in:
Bibliographic Details
Main Authors: Alexey Anastasiev, Hideki Kadone, Aiki Marushima, Hiroki Watanabe, Alexander Zaboronok, Shinya Watanabe, Akira Matsumura, Kenji Suzuki, Yuji Matsumaru, Hiroyuki Nishiyama, Eiichi Ishikawa
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Sensors
Subjects:
stroke
paresis
upper extremity motor impairment
hand gesture recognition
electromyography (EMG)
machine learning
Online Access:https://www.mdpi.com/1424-8220/25/12/3664
Tags: Add Tag
No Tags, Be the first to tag this record!
Login for hold and recall information

Internet

https://www.mdpi.com/1424-8220/25/12/3664

Similar Items