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A Data-Driven Approach to Quantifying Natural Human Walking

By Christopher Batty and Daniel Eaton


Our goal in this project was to implement and validate the results of recent work employing statistical techniques to automatically determine a “naturalness” measure of human motion data. Using a training set of motion capture data (that in effect embodies our definition of naturalness), we learn several models to represent this natural motion, and test them on a variety of hand-selected positive and negative examples. The models we consider are the Naive Bayes model, mixtures of Gaussians, and hidden Markov models. We restrict our study to walking motions, but nonetheless achieve convincing and meaningful results, which are illustrated using ROC curves. In addition, we mention shortcomings of the original paper, and provide a few suggestions for further work

Year: 2008
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