Quantitative Movement Analysis Using Scaled Information Implied in Monocular Videos

Background: Quantitative movement analysis is significant in clinic decision-making for neurological diseases. However, current methods fail to be widely used due to expensive equipment and professional workers. Methods: To make movement analysis more accessible even in remote areas, this paper provides a quantitative movement analysis framework based monocular camera analyzing gait parameters automatically. A size-aligned method is formulated to recover scaled information uncertain in a single view under scaled restrictions from body size and geometric constraints among subject joints' positions. The image key points are the subject joints' positions in images and the scaling key points are processed by the size-aligned method. The attention mechanism fuses deep representations learned from image key points and scaling key points respectively. The fused representations are decoded to provide final analyses. Results: The suggested learning framework can analyze parameters with strong correlations to the ground truth according to experimental results on a dataset for cerebral palsy: cadence $(r=0.81)$ , speed $(r=0.78)$ , step length $(r=0.78)$ , knee flexion at maximum extension $(r=0.85)$ , gait deviation indexes $(r=0.75)$ , and asymmetric in gait deviation indexes $(r=0.55)$ . Conclusion: The encouraging findings show that it is feasible to quantitatively assess patients' lower limb movement ability using a monocular camera.