Quantitative Assessment of Hand Motor Function for Post-Stroke Rehabilitation Based on HAGCN and Multimodality Fusion
Author:
Affiliation:
1. State Key Laboratory for Control and Management of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
2. China Rehabilitation Research Center, Beijing Bo’ai Hospital, Beijing, China
Funder
National Natural Science Foundation of China
Beijing Municipal Natural Science Foundation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation
Link
http://xplorestaging.ieee.org/ielx7/7333/9695946/09832957.pdf?arnumber=9832957
Reference31 articles.
1. A Multimodal Fusion Model for Estimating Human Hand Force: Comparing surface electromyography and ultrasound signals
2. Quantitative Taxonomy of Hand Kinematics Based on Long Short-Term Memory Neural Network
3. Support Vector Machine-Based Classifier for the Assessment of Finger Movement of Stroke Patients Undergoing Rehabilitation
4. An Automatic Rehabilitation Assessment System for Hand Function Based on Leap Motion and Ensemble Learning
5. Cellphone-Based Automated Fugl-Meyer Assessment to Evaluate Upper Extremity Motor Function After Stroke
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1. Design and Analysis of Rehabilitation Evaluation System for Finger Rehabilitation Robot;International Journal of Automation Technology;2024-09-05
2. Temporal Dynamics and Physical Priori Multimodal Network for Rehabilitation Physical Training Evaluation;IEEE Journal of Biomedical and Health Informatics;2024-09
3. Quantitative Upper Limb Impairment Assessment for Stroke Rehabilitation: A Review;IEEE Sensors Journal;2024-03-15
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