A hybrid approach to human posture classification during TV watching

Jonathan H. Chan, Thammarsat Visutarrom, Sung Bae Cho, Worrawat Engchuan, Pornchai Mongolnam, Simon Fong

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Human posture classification in near real time is a significant challenge in various fields of research. Recently, the use of the Microsoft Kinect system for 3D skeleton detection has shown to be of promise. This work compares four common classifiers and the use of a hybrid approach for classification. The results show that the use of a hybrid genetic algorithm and random forest classifier is able to provide fast and robust human posture classification. Finally, to aid in further development of posture detection, a comprehensive human posture data set while watching television has been generated in this work for benchmarking purpose and made available publicly at http://dlab.sit.kmutt.ac.th/index.php/human-posture-datasets.

Original languageEnglish
Pages (from-to)1119-1126
Number of pages8
JournalJournal of Medical Imaging and Health Informatics
Issue number4
Publication statusPublished - 2016 Aug

Bibliographical note

Publisher Copyright:
Copyright © 2016 American Scientific Publishers All rights reserved.

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging
  • Health Informatics


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