  Recent technological advancements in artificial intelligence and computer
vision have enabled gait analysis on portable devices such as cell phones.
However, most state-of-the-art vision-based systems still impose numerous
constraints for capturing a patient's video, such as using a static camera and
maintaining a specific distance from it. While these constraints are manageable
under professional observation, they pose challenges in home settings. Another
issue with most vision-based systems is their output, typically a
classification label and confidence value, whose reliability is often
questioned by medical professionals. This paper addresses these challenges by
presenting a novel system for gait analysis robust to camera movements and
providing explanations for its output. The study utilizes a dataset comprising
videos of subjects wearing two types of Knee Ankle Foot Orthosis (KAFO), namely
"Locked Knee" and "Semi-flexion," for mobility, along with metadata and ground
truth for explanations. The ground truth highlights the statistical
significance of seven features captured using motion capture systems to
differentiate between the two gaits. To address camera movement challenges, the
proposed system employs super-resolution and pose estimation during
pre-processing. It then identifies the seven features - Stride Length, Step
Length and Duration of single support of orthotic and non-orthotic leg,
Cadence, and Speed - using the skeletal output of pose estimation. These
features train a multi-layer perceptron, with its output explained by
highlighting the features' contribution to classification. While most
state-of-the-art systems struggle with processing the video or training on the
proposed dataset, our system achieves an average accuracy of 94%. The model's
explainability is validated using ground truth and can be considered reliable.
