Postural classification using kinect
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Publication Details
Author list: Visutarrom T., Mongkolnam P., Chan J.H.
Publisher: Hindawi
Publication year: 2014
Start page: 403
End page: 408
Number of pages: 6
ISBN: 9781479949632
ISSN: 0146-9428
eISSN: 1745-4557
Languages: English-Great Britain (EN-GB)
Abstract
This research focuses on the comparison of posture recognition, using a data mining classification approach on the skeleton data stream obtained from Kinect camera. We classified four standard postures including Stand, Sit, Sit on floor and Lie Down. We compared six classifiers, namely, decision tree, neural network, na๏ve Bayes, support vector machine, logistic regression and random forest in order to find a suitable classifier. Our best results can correctly classify the postures with 97.88% accuracy, 97.40% sensitivity, and 0.991 ROC area under curve using Max-Min normalization with a decision tree classifier on four transformed attributes. Our future work will use the knowledge obtained to classify a wider range of postures of the elderly while watching television, to be a part of a bigger effort to monitor and study elderly behavior at home. ฉ 2014 IEEE.
Keywords
Postural classification
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