  Musculoskeletal diseases and cognitive impairments in patients lead to
difficulties in movement as well as negative effects on their psychological
health. Clinical gait analysis, a vital tool for early diagnosis and treatment,
traditionally relies on expensive optical motion capture systems. Recent
advances in computer vision and deep learning have opened the door to more
accessible and cost-effective alternatives. This paper introduces a novel
spatio-temporal Transformer network to estimate critical gait parameters from
RGB videos captured by a single-view camera. Empirical evaluations on a public
dataset of cerebral palsy patients indicate that the proposed framework
surpasses current state-of-the-art approaches and show significant improvements
in predicting general gait parameters (including Walking Speed, Gait Deviation
Index - GDI, and Knee Flexion Angle at Maximum Extension), while utilizing
fewer parameters and alleviating the need for manual feature extraction.
