Objective: This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease PD remotely using an accessible standard webcam.
The video frames were processed using the artificial intelligence algorithms tracking the movements of the hands. The developed classifiers were validated on an independent dataset.
Results: We found significant differences in the motor performance of the patients with PD and HSs in all the bradykinesia upper limb motor tasks. The best performing classifiers were unilateral finger tapping and hand movement speed.
The model correlated both with the IMUs for quantitative assessment of motor function and the clinical scales, hence demonstrating concurrent validity with the existing methods.
Conclusions: We present here the proof-of-concept of a novel webcam-based technology to remotely detect the parkinsonian features using artificial intelligence.
