  Clinical gait analysis (CGA) using computer vision is an emerging field in
artificial intelligence that faces barriers of accessible, real-world data, and
clear task objectives. This paper lays the foundation for current developments
in CGA as well as vision-based methods and datasets suitable for gait analysis.
We introduce The Gait Abnormality in Video Dataset (GAVD) in response to our
review of over 150 current gait-related computer vision datasets, which
highlighted the need for a large and accessible gait dataset clinically
annotated for CGA. GAVD stands out as the largest video gait dataset,
comprising 1874 sequences of normal, abnormal and pathological gaits.
Additionally, GAVD includes clinically annotated RGB data sourced from publicly
available content on online platforms. It also encompasses over 400 subjects
who have undergone clinical grade visual screening to represent a diverse range
of abnormal gait patterns, captured in various settings, including hospital
clinics and urban uncontrolled outdoor environments. We demonstrate the
validity of the dataset and utility of action recognition models for CGA using
pretrained models Temporal Segment Networks(TSN) and SlowFast network to
achieve video abnormality detection of 94% and 92% respectively when tested on
GAVD dataset. A GitHub repository https://github.com/Rahmyyy/GAVD consisting of
convenient URL links, and clinically relevant annotation for CGA is provided
for over 450 online videos, featuring diverse subjects performing a range of
normal, pathological, and abnormal gait patterns.
