“YouTube Funnies”视频分析有助于人类步态和跌倒研究-初步结果和概念验证
Video Analysis of "YouTube Funnies" to Aid the Study of Human Gait and Falls - Preliminary Results and Proof of Concept
摘要：
因为跌倒很有趣，YouTube和其他视频分享网站包含了大量真实生活中的跌倒。我们建议从这些视频中提取步态和平衡信息，以帮助我们更好地了解导致跌倒的一些因素。在一个包含多个（n=14）跌倒/非跌倒的视频中探索概念验证。分析探讨：计算从任意视点捕获的视频中的步态的时空参数;障碍物前最后几步的步态参数与跌倒与未跌倒之间的关系;以及多变量模型在预测存在意外障碍物时的跌倒的预测能力。单应变换校正透视投影失真，并允许一致的跟踪步态参数作为一个人走在场景中的任意方向。合成俯视图允许计算平均步幅长度，合成侧视图允许测量头部的上下运动。在留一法交叉验证中，我们能够正确预测一个人是否会在1 - 4个案例中的11个（78.6%）中跌倒，只需查看在到达障碍物之前的1-4步中的平均步幅和垂直头部运动范围。
英文摘要：
Because falls are funny, YouTube and other video sharing sites contain a large repository of real-life falls. We propose extracting gait and balance information from these videos to help us better understand some of the factors that contribute to falls. Proof-of-concept is explored in a single video containing multiple (n=14) falls/non-falls in the presence of an unexpected obstacle. The analysis explores: computing spatiotemporal parameters of gait in a video captured from an arbitrary viewpoint; the relationship between parameters of gait from the last few steps before the obstacle and falling vs. not falling; and the predictive capacity of a multivariate model in predicting a fall in the presence of an unexpected obstacle. Homography transformations correct the perspective projection distortion and allow for the consistent tracking of gait parameters as an individual walks in an arbitrary direction in the scene. A synthetic top view allows for computing the average stride length and a synthetic side view allows for measuring up and down motions of the head. In leave-one-out cross-validation, we were able to correctly predict whether a person would fall or not in 11 out of the 14 cases (78.6%), just by looking at the average stride length and the range of vertical head motion during the 1-4 most recent steps prior to reaching the obstacle.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
