Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video Dataset

Clinical gait analysis (CGA) using computer vision is an emerging field in artificial intelligence facing great challenges in obtaining accessible and annotated real-world data and clear task objectives. This paper lays the groundwork for advancing CGA, presenting vision-based methods and datasets tailored for gait analysis. We introduce the Gait Abnormality in Video Dataset (GAVD) in response to a review of over 150 existing gait-related datasets, designed to address the critical need for a large, clinically annotated gait dataset. GAVD contains 1,874 video sequences, capturing normal, abnormal, and pathological gait patterns. It features RGB data, clinically annotated from publicly available online sources, and encompassing over 400 subjects who have undergone clinical-grade visual screening. The dataset spans diverse environments, such as hospital clinics and uncontrolled urban settings. We validated GAVD through action recognition models, achieving abnormality detection rates of 94% and 92% with Temporal Segment Networks (TSN) and SlowFast networks, respectively. We further demonstrate GAVD’s clinical utility by testing on the Clinical Abnormality Simulated Dataset (CASD) achieving abnormality detection accuracy of 73% and 87%, respectively, with TSN and SlowFast networks. To facilitate further research, we provide a GitHub repository GAVD with URL links and clinically relevant annotation for over 450 videos featuring a variety of gait patterns and unique subjects.