A hybrid approach to human posture classification during TV watching

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Publication Details

Author list: Chan J.H., Visutarrom T., Cho S.-B., Engchuan W., Mongolnam P., Fong S.

Publisher: American Scientific Publishers

Publication year: 2016

Journal: Journal of Medical Imaging and Health Informatics (2156-7018)

Volume number: 6

Issue number: 4

Start page: 1119

End page: 1126

Number of pages: 8

ISSN: 2156-7018

eISSN: 2156-7026

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988376944&doi=10.1166%2fjmihi.2016.1809&partnerID=40&md5=f1c723d1f6384a3aa3faf6a3fa01c710

Languages: English-Great Britain (EN-GB)

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Abstract

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. Copyright ฉ 2016 American Scientific Publishers All rights reserved.

Keywords

Benchmarking, Human posture classification, Hybrid approach