Clinical Research Directory

Based on machine learning and computer vision algorithms, this intervention can map twenty-one distinct anatomical reference points on the hand, wrist, and fingers in three-dimensional space in real-time, within milliseconds. The application utilizes movements previously validated for biomechanical accuracy by a physiotherapist; as the patient performs exercises at home, the system continuously compares their current range of motion (ROM) against reference values using algorithms. The patient receives immediate visual and auditory feedback on-screen regarding the accuracy, speed, and angle of the movement; simultaneously, the system creates an autonomous, asynchronous monitoring mechanism by automatically recording daily changes in ROM and session participation rates (treatment adherence) to a secure, cloud-based database.

Participants in the control group will perform an eight-week specific hand exercise regimen-identical to that of the experimental group in terms of biomechanics, intensity, and physiological parameters-under the face-to-face supervision of a physiotherapist. The exercise protocol for both groups will consist of an evidence-based rehabilitation module incorporating isometric strengthening, flexor and extensor tendon gliding, and active/passive stretching components, aligned with the pathophysiology of SSc.