Classification of User Adherence to Home Hand Rehabilitation Technology Using a Feed-Forward Artificial Neural Network
Author:
Affiliation:
1. George Mason University,Department of Bioengineering,Fairfax,VA,USA
2. Flint Rehabilitation Devices LLC,Irvine,CA,USA
3. George Mason University,Department of Bioengineering and Mechanical Engineering,Fairfax,VA,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10313339/10313352/10313490.pdf?arnumber=10313490
Reference22 articles.
1. Design and Control of a Novel Grip Amplifier to Support Pinch Grip with a Minimal Soft Hand Exoskeleton
2. Feasibility of home hand rehabilitation using musicglove after chronic spinal cord injury
3. Using Large-Scale Sensor Data to Test Factors Predictive of Perseverance in Home Movement Rehabilitation: Optimal Challenge and Steady Engagement;muñoz;Front Neurol,2022
4. From smartphones to iPad: Power users’ disposition toward mobile media devices
5. Virtual Reality Therapy for Adults Post-Stroke: A Systematic Review and Meta-Analysis Exploring Virtual Environments and Commercial Games in Therapy
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