Machine-learning-based validation of Microsoft Azure Kinect in measuring gait profiles
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The dataset was used to validate Microsoft Azure Kinect in measuring gait profiles, employing machine learning techniques to investigate the impact of residual errors due to environmental, methodological, and processing factors on the accuracy of gait profile assessments. Data were collected from healthy and post-stroke subjects using a motion capture system and a 3D camera-based system with MAK, and corresponding gait profiles were estimated and compiled into a dataset. The estimated gait profiles include spatiotemporal, asymmetry, and body center of mass parameters to capture various normal and pathological gait characteristics. Contact e-mail: claudia.ferraris@cnr.it (Claudia Ferraris) lucavisma@hotmail.com (Luca Vismara) veronica.cimolin@polimi.it (Veronica Cimolin)
本数据集用于验证微软Azure Kinect（Microsoft Azure Kinect）在步态特征测量中的性能表现，采用机器学习技术探究环境、方法学及处理流程因素引发的残余误差对步态特征评估精度的影响。研究数据采集自健康受试者与脑卒中后受试者，所用设备包括运动捕捉系统及搭载MAK的3D摄像系统，并将估算得到的对应步态特征整合为该数据集。所估算的步态特征涵盖时空参数、不对称性参数以及身体质心参数，用以表征各类正常与病理性步态特征。 联系方式： claudia.ferraris@cnr.it（克劳迪娅·费拉里斯） lucavisma@hotmail.com（卢卡·维斯马拉） veronica.cimolin@polimi.it（维罗妮卡·西莫林）
