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Because falls are funny, YouTube and other video sharing sites contain alarge repository of real-life falls. We propose extracting gait and balanceinformation from these videos to help us better understand some of the factorsthat contribute to falls. Proof-of-concept is explored in a single videocontaining multiple (n=14) falls/non-falls in the presence of an unexpectedobstacle. The analysis explores: computing spatiotemporal parameters of gait ina video captured from an arbitrary viewpoint; the relationship betweenparameters of gait from the last few steps before the obstacle and falling vs.not falling; and the predictive capacity of a multivariate model in predictinga fall in the presence of an unexpected obstacle. Homography transformationscorrect the perspective projection distortion and allow for the consistenttracking of gait parameters as an individual walks in an arbitrary direction inthe scene. A synthetic top view allows for computing the average stride lengthand 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 aperson would fall or not in 11 out of the 14 cases (78.6%), just by looking atthe average stride length and the range of vertical head motion during the 1-4most recent steps prior to reaching the obstacle.

Video Analysis of "YouTube Funnies" to Aid the Study of Human Gait and Falls - Preliminary Results and Proof of Concept