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.
| Original language | English |
|---|---|
| Pages (from-to) | 1119-1126 |
| Number of pages | 8 |
| Journal | Journal of Medical Imaging and Health Informatics |
| Volume | 6 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2016 Aug 1 |
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All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging
- Health Informatics
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A hybrid approach to human posture classification during TV watching. / Chan, Jonathan H.; Visutarrom, Thammarsat; Cho, Sung-Bae; Engchuan, Worrawat; Mongolnam, Pornchai; Fong, Simon.
In: Journal of Medical Imaging and Health Informatics, Vol. 6, No. 4, 01.08.2016, p. 1119-1126.Research output: Contribution to journal › Article
TY - JOUR
T1 - A hybrid approach to human posture classification during TV watching
AU - Chan, Jonathan H.
AU - Visutarrom, Thammarsat
AU - Cho, Sung-Bae
AU - Engchuan, Worrawat
AU - Mongolnam, Pornchai
AU - Fong, Simon
PY - 2016/8/1
Y1 - 2016/8/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84988376944&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84988376944&partnerID=8YFLogxK
U2 - 10.1166/jmihi.2016.1809
DO - 10.1166/jmihi.2016.1809
M3 - Article
VL - 6
SP - 1119
EP - 1126
JO - Journal of Medical Imaging and Health Informatics
JF - Journal of Medical Imaging and Health Informatics
SN - 2156-7018
IS - 4
ER -