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Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation
Published on: March 8, 2018
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Evaluating touchless capacitive gesture recognition as an assistive device for upper extremity mobility impairment
Alexander Nelson1, Sandy McCombe Waller2, Ryan Robucci3
1Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR, USA.
Summary
Touchless textile sensors offer a feasible way to control environmental systems for individuals with upper-extremity mobility impairments. Personalization and user feedback are key for successful implementation of these wearable sensors.
Area of Science:
- Biomedical Engineering
- Human-Computer Interaction
- Rehabilitation Technology
Background:
- Investigates the use of capacitive textile sensors embedded in clothing as proximity sensors.
- Focuses on environmental control for individuals with upper-extremity mobility impairments.
Purpose of the Study:
- To explore the feasibility of touchless textile sensors for environmental control.
- To assess the effectiveness of these sensors for individuals with spinal cord injury.
Main Methods:
- Utilized custom visualization for real-time feedback on gesture recognition.
- Conducted a two-stage study with five participants with upper extremity mobility impairment.
- Participants performed gestures mimicking an alphanumeric set for appliance control.
Main Results:
- Achieved average binary gesture classification accuracies exceeding 90%.
- Identified critical insights into participant motion for wearable sensor system design.
Conclusions:
- Personalization of sensor hardware, gesture sets, and algorithms is crucial for this population.
- Explicit user feedback and visualization of control goals enhance gesture learning and system adoption.
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