WeightGait Dataset

Here we introduce the WeightGait dataset: a dataset developed for the purposes of facilitating vision-based gait assessment methodologies with more realistic conditions comparable to real world use. The motivation for this dataset is to create a testing environment for gait assessment algorithms that is closer to the realities of application. To accomplish this, unlike other similar datasets, we do two main things uniquely: We simulate overlapping abnormalities, for a total of 9 different combinations of abnormality detailed below. The background and equipment used are imperfect and noisy to simulate the similar hardship experienced when trying to install a gait monitor into someone's home. This means cheap recording equipment for scalability resulting in relatively low-frames per recording. It also means slight feet/head clipping at times, only a single camera view to detect depth and no curation to the background or the clothing/walking speed of the participants. The original 2D joint positions are estimated on the original videos using a lightweight implementation of the algorithm given in the paper 'HigherHRNet'.本研究介绍WeightGait数据集：该数据集专为推进基于视觉的步态评估方法研发，旨在打造更贴近真实应用场景的测试条件，其核心研发目标即为步态评估算法构建更贴合实际落地场景的测试环境。为实现这一目标，与其他同类数据集不同，本数据集采用两项独有的设计：其一，模拟重叠式步态异常，共计9种不同的异常组合，详情如下；其二，构建不完善且带有噪声的背景环境与采集设备，以模拟在家庭环境中部署步态监测设备时可能遭遇的实际困境。具体而言，为兼顾可扩展性而采用了低成本采集设备，导致单段录制的帧率相对偏低；录制过程中偶尔会出现足部或头部被画面裁切的情况，仅依靠单摄像头视角获取深度信息，且未对背景、参与者着装或步行速度进行刻意调校。原始视频中的二维关节点位，通过基于论文《HigherHRNet》所提出算法的轻量级实现方案进行估计。

搜集汇总

背景与挑战

背景概述

WeightGait Dataset是一个专为基于视觉的步态评估设计的数据集，旨在模拟真实世界应用条件，其独特之处在于模拟了9种重叠异常组合，并使用不完美、嘈杂的设备与背景以反映家庭安装的挑战性，包括低帧率、轻微剪辑和单视角限制。数据通过轻量级HigherHRNet算法从原始视频中估计2D关节位置生成，适用于测试算法在非理想环境下的鲁棒性。