NDLI: Prolonged sitting detection for office workers syndrome prevention using kinect

Content Provider	IEEE Xplore Digital Library
Author	Paliyawan, P. Nukoolkit, C. Mongkolnam, P.
Copyright Year	2014
Description	Author affiliation: Sch. of Inf. Technol., King Mongkut's Univ. of Technol. Thonburi, Bangkok, Thailand (Paliyawan, P.; Nukoolkit, C.; Mongkolnam, P.)
Abstract	This research has focused on detection of prolonged sitting of office workers by performing data mining classification on the real-time skeleton data stream captured by a single Kinect camera set up in an office worker's work station area. The system classifies the input stream into sequences of stills or moves. The performance of several classification methods such as decision tree, neural network, naive Bayes, and k-Nearest Neighbors are compared in order to acquire the optimal classifier. The proposed system can effectively monitor the user's postures with 98% accuracy and give the user real-time feedback based on the three levels of healthy in ergonomics. In addition, the proposed work includes development of an alerting device using a microcontroller, and provision of data visualization for a daily summary report.
Starting Page	1
Ending Page	6
File Size	1847976
Page Count	6
File Format	PDF
ISBN	9781479929931
DOI	10.1109/ECTICon.2014.6839785
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-14
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Ergonomics Human Gesture Recognition Classification Cameras Real-time systems Feeds Office Workers Syndrome Kinect Camera Joints Monitoring Health and Medical Informatics
Content Type	Text
Resource Type	Article

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