A Case Series in Position-Aware Myoelectric Prosthesis Control Using Recurrent Convolutional Neural Network Classification with Transfer Learning
Author:
Affiliation:
1. University of Alberta,Department of Biomedical Engineering,Edmonton,AB,Canada,T6G 2V2
2. University of Alberta,Division of Physical Medicine and Rehabilitation,Department of Medicine,Edmonton,AB,Canada,T6G 2R3
Funder
NSERC
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10304677/10304678/10304787.pdf?arnumber=10304787
Reference21 articles.
1. A transradial modular adaptable platform for evaluating prosthetic feedback and control strategies;hallworth;Proc MEC,0
2. Myoelectric prosthesis users and non-disabled individuals wearing a simulated prosthesis exhibit similar compensatory movement strategies
3. Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features
4. Preliminary Evaluation of the Effect of Mechanotactile Feedback Location on Myoelectric Prosthesis Performance Using a Sensorized Prosthetic Hand
5. Limb loading enhances skill transfer between augmented and physical reality tasks during limb loss rehabilitation
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1. A multifaceted suite of metrics for comparative myoelectric prosthesis controller research;PLOS ONE;2024-05-13
2. Myoelectric Prosthesis Control using Recurrent Convolutional Neural Network Regression Mitigates the Limb Position Effect;2024-02-08
3. Wearable Sensor to Monitor Quality of Upper Limb Task Practice for Stroke Survivors at Home;Sensors;2024-01-16
4. A multifaceted suite of metrics for comparative myoelectric prosthesis controller research;2023-08-29
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