Comparing Robotic and Computer Vision Assessments of Unilateral and Bilateral Reaching in Healthy Adults

Abstract

Assessment of reaching is foundational to upper limb neurorehabilitation. Current neurorehabilitation needs have increased the demand for quantitative clinical assessments of bilateral coordination. Robotics and computer vision for motion tracking are two means to provide relevant quantitative metrics but have many differences including the dimensionality of reaching movements (planar versus three-dimensional) and data acquisition. We do not know how consistent measures of bilateral coordination performance are between these different assessments. In this study, we examined how one robotic and one computer vision method can identify differences between symmetrical and asymmetrical reaching, and the correlations in movement time, and hand lag between these two approaches. Thirty healthy young adults completed four reaching games using the Kinarm exoskeleton robot and a custom developed augmented reality assessment using computer vision. We found that both approaches were able to detect well-established movement time and hand lag differences between symmetrical and asymmetrical reaching, with the differences between symmetrical and asymmetrical being larger with the computer vision approach. Moderate correlations were found between approaches for unilateral and symmetric reaching in both movement time and hand lags; however, no significant correlations were found between approaches for asymmetric reaching. Our results show that reaching task performance differs between robotic and computer vision-based assessment, however, both approaches provide quantitative metrics of unilateral and bilateral reaching that are consistent with prior research. There are benefits and tradeoffs to each approach, and this study informs how clinicians and researchers can consider the methodological differences when determining which assessment method to use.