@inproceedings{f1cbd7d8e9fa4c4a897055824317d455,
title = "Kinect based body posture detection and recognition system",
abstract = "A multi-class human posture detection and recognition algorithm using Kinect based geometric features is presented. The three dimensional skeletal data from the Kinect is converted to a set of angular features. The postures are classified using a support vector machines classifier with polynomial kernel. Detection of posture is done by thresholding the posture probability. The algorithm provided a recognition accuracy of 95.78% when tested using a 10 class dataset containing 6000 posture samples. The precision and recall rates of the detection system are 100% and 98.54% respectively.",
keywords = "Angular features, Depth camera, Human-computer interaction, Natural interaction, Posture detection, Posture recognition",
author = "Pisharady, {Pramod Kumar} and Martin Saerbeck",
year = "2013",
month = jul,
day = "19",
doi = "10.1117/12.2009926",
language = "English (US)",
isbn = "9780819495662",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
booktitle = "International Conference on Graphic and Image Processing, ICGIP 2012",
note = "4th International Conference on Graphic and Image Processing, ICGIP 2012 ; Conference date: 06-10-2012 Through 07-10-2012",
}