Markerless Motion Capture Enhances Clinical Assessments: Preliminary Validation with the Box and Blocks Test

Precision rehabilitation, therapy tailored to patient-specific impairments, is hindered by the low resolution of clinical assessments and the limited uptake of high resolution technological solutions that are too cumbersome for regular use. To address this, we developed C-MoRe, a phone-based system that applies computer vision to clinical assessments to produce quantitative metrics of upper extremity motor function. We tested C-MoRe in 7 chronic stroke participants performing the Box and Block Test (BBT), which scores the number of blocks participants can transfer over a divider in 1 minute. We used ML models to identify assessment and hand landmarks, and developed a custom algorithm to autoscore the assessment and quantify movement duration, amplitude, and velocity during grasping and transfer movements. Our algorithm had high fidelity in block counting (98.4 % accuracy) and identifying task movement phases (ICC $>0.99$) compared to human raters. Movement velocity, grasp, and transfer duration were sensitive to functional differences between limbs, and grasp duration was significantly related to finger proprioception, a known predictor of therapy outcomes. Thus, C-MoRe can provide meaningful, quantitative measures of movement quality in BBT, and its simplicity may enable widespread use and the creation of large databases needed for predictive modeling.