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Musculoskeletal diseases and cognitive impairments in patients lead todifficulties in movement as well as negative effects on their psychologicalhealth. Clinical gait analysis, a vital tool for early diagnosis and treatment,traditionally relies on expensive optical motion capture systems. Recentadvances in computer vision and deep learning have opened the door to moreaccessible and cost-effective alternatives. This paper introduces a novelspatio-temporal Transformer network to estimate critical gait parameters fromRGB videos captured by a single-view camera. Empirical evaluations on a publicdataset of cerebral palsy patients indicate that the proposed frameworksurpasses current state-of-the-art approaches and show significant improvementsin predicting general gait parameters (including Walking Speed, Gait DeviationIndex - GDI, and Knee Flexion Angle at Maximum Extension), while utilizingfewer parameters and alleviating the need for manual feature extraction.

Learning to Estimate Critical Gait Parameters from Single-View RGB Videos with Transformer-Based Attention Network