Proposed Eye-Hand Correlation Assessment System: A Novel Approach for Evaluating Coordination

Abstract

Post-stroke patients often experience difficulties with hand-eye coordination, resulting in reduced perceptual accuracy and finger dexterity. These impairments can significantly impact their overall quality of life. This study introduces a new eye-hand correlation assessment system with enhanced assessment metrics for evaluating coordination and upper limb disability. It utilizes a robotic haptic system and correlates eye-gaze with hand and arm movements. The experimental setup integrates a haptic device, eye tracker, and four inertial sensors to measure the upper limb joint angles displacement. The user performs various hand movement patterns in a virtual environment while data is simultaneously collected from the hand, eye, and arm movements. A metric for eye-hand coordination based on the Pearson coefficient has been developed (EHCM) and correlated with the upper limb movements, to detect possible upper limb disabilities. The system was tested with ten healthy volunteers, one of which had a type of upper-limb disability. Most of the EHCM scores showed a very strong correlation for those users without upper-limb disability or known eye-hand coordination problems. Also, the proposed system was preliminary tested with post-stroke patients to identify the limitations and weak points. More tests will be conducted to validate this metric as well as correlate it with a CNN classifier for detecting incorrect wrist-upper limb movements. The main objective of this work was to develop an evaluation tool that assists therapists in quantifying collected data from robot therapy, for assessing the degree of disability in patients.