Unsupervised clustering of people from 'skeleton' data

This paper investigates the possibility of recognising individual persons from their walking gait using three-dimensional 'skeleton' data from an inexpensive consumer-level sensor, the Microsoft 'Kinect'. In an experimental pilot study it is shown that the K-means algorithm - as...

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Bibliographic Details
Main Authors: Ball, Adrian, Rye, David, Ramos, Fabio, Velonaki, Mari
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Computing methodologies > Machine learning > Learning paradigms > Unsupervised learning > Cluster analysis
Gait analysis
HRI
Humans
Legged locomotion
Pattern recognition
Signal processing algorithms
Skeleton
unsupervised clustering
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Summary:This paper investigates the possibility of recognising individual persons from their walking gait using three-dimensional 'skeleton' data from an inexpensive consumer-level sensor, the Microsoft 'Kinect'. In an experimental pilot study it is shown that the K-means algorithm - as a candidate unsupervised clustering algorithm - is able to cluster gait samples from four persons with a nett accuracy of 43.6%.
ISSN:2167-2121
2167-2148
DOI:10.1145/2157689.2157767