Video Analysis of "YouTube Funnies" to Aid the Study of Human Gait and Falls - Preliminary Results and Proof of Concept
Babak Taati, Pranay Lohia, Avril Mansfield, Ahmed Ashraf

TL;DR
This study demonstrates the feasibility of extracting gait and balance information from YouTube videos to predict falls, using perspective correction and multivariate modeling, with promising preliminary results.
Contribution
It introduces a novel method for analyzing arbitrary-view videos to predict falls, leveraging homography transformations and gait parameters from real-world videos.
Findings
Correctly predicted falls in 78.6% of cases
Homography enables consistent gait tracking from arbitrary viewpoints
Gait parameters can predict falls before obstacle contact
Abstract
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…
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Taxonomy
TopicsGait Recognition and Analysis · Human Pose and Action Recognition · Diabetic Foot Ulcer Assessment and Management
