Eye-Hand Coordination Assessment Metrics Using a Multi-Platform Haptic System with Eye-Tracking and Motion Capture...

Area of Science:

• Robotics and Human-Computer Interaction
• Rehabilitation Engineering
• Biomedical Engineering

Background:

• Effective assessment of eye-hand coordination is crucial for individuals with disabilities.
• Existing methods may lack precision or comprehensive data capture.
• Developing advanced systems can improve rehabilitation outcomes.

Purpose of the Study:

• To develop and refine a system for assessing and improving eye-hand coordination.
• To establish assessment metrics by correlating haptic device input with eye-gaze and upper limb movements.
• To create a versatile system adaptable to various haptic devices and user needs.

Main Methods:

• Mapping a robotic haptic device (Omni) to a virtual environment.
• Utilizing a Vicon motion capture system to record upper limb movements.
• Developing a multi-platform haptic system with a GUI and B-spline calculated haptic tunnels.
• Correlating haptic device and eye-gaze coordinates using the Pearson coefficient.

Main Results:

• The system successfully recorded trajectory, time, and upper limb motion data.
• Two types of haptic tunnels (mid-path and smoothest-path) were presented and evaluated.
• The Pearson coefficient effectively correlated haptic device and eye-gaze data.

Conclusions:

• The developed system provides a novel approach to assessing eye-hand coordination.
• This technology has the potential to significantly aid in the rehabilitation of individuals with disabilities.
• Further refinement can enhance the system's efficacy in both assessment and training.