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.

近年来，人工智能和计算机视觉的最新技术进展使得步态分析可以在手机等便携设备上进行。本文提出了一种针对摄像机运动具有鲁棒性并提供输出解释的步态分析系统，通过使用运动捕捉系统捕捉的七个特征来区分两种步态，并经验证可靠。

基于可解释性人工智能的膝踝足矫形器患者步态分析的视频摄像机方法

XAI-based gait analysis of patients walking with Knee-Ankle-Foot  orthosis using video cameras

The technique requires the epipolar geometry to be pre-estimated between each
image pair. It exploits the constraints which the camera movement implies, in
order to apply a closed-form correction to the parameters of the input
affinities. Also, it is shown that the rotations and scales obtained by
partially affine-covariant detectors, e.g., AKAZE or SIFT, can be completed to
be full affine frames by the proposed algorithm. It is validated both in
synthetic experiments and on publicly available real-world datasets that the
method always improves the output of the evaluated affine-covariant feature
detectors. As a by-product, these detectors are compared and the ones obtaining
the most accurate affine frames are reported. For demonstrating the
applicability, we show that the proposed technique as a pre-processing step
improves the accuracy of pose estimation for a camera rig, surface normal and
homography estimation.

通过预估每个图像对之间的极线几何关系，构造一种针对部分仿射协变特征检测器的标准仿射框架校正算法，并验证该算法可用于预处理步骤以提高相机姿态估计、表面法线和单应性矩阵等任务的准确性。