Image-recognition-based system for precise hand function evaluation

Abstract

Hand dysfunction caused by hand injuries, strokes, or other neurological degenerative diseases such as cervical spondylosis is being increasingly reported. Currently, hand function assessments for diagnosis or rehabilitation are primarily based on qualitative scales, which are subjective and may vary considerably depending on the expertise of the attending clinician. Although wearable sensors and computer vision techniques have been proposed to obtain quantitative hand movement information, both have limitations. In this study, a multiview video tracking and recording system was set up using high-speed cameras and mapping of actual hand movements. The state-of-the-art software DeepLabCut was used to obtain precise 2D and 3D finger joint positions. Kinematic parameters, such as movement count, period, phase, and Pearson coefficient were used to characterize hand movement based on the relative distance-time curves of finger joints. Experimental results in a clinical setting showed that this video-based image-recognition neural network method can accurately distinguish healthy from dysfunctional hand movements. The proposed system is inexpensive, easy to set up and use, and exhibits high accuracy. Thus, it can revolutionize medical hand motion analysis and spur the development of automated quantitative systems for early hand-related disorder detection.