Citation

Chang, Yoong Choon and Chua, Jia Luen and Jaward, Mohamed Hisham and Parkkinen, Jussi and Wong, Kok Sheik (2015) Vision-based hand grasping posture recognition in drinking activity. In: 2014 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2014. IEEE, pp. 185-190. ISBN 978-147996120-7

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Abstract

Drinking activity recognition is not a well-researched area in the human activity recognition area. In this paper, a novel technique to recognize the hand grasping posture in drinking activities is proposed. The proposed method aims to overcome the accuracy issue of Kinect in detecting the correct hand position during drinking activities and no training is required to recognize the grasping posture. Instead, the proposed technique directly extracts the unique features of the grasp posture by using a special Haar-like feature on the input image. By comparing the difference between the total pixel values of each region to a set of thresholds, the grasping posture of the hand can be detected and distinguished from other non-grasping postures or non-hand images. Experimental results indicate that the proposed technique is able to achieve a relatively high accuracy (88% true positive rate and 20% false positive rate) in detecting and recognizing the normal hand grasping posture, which mainly appears in drinking activities where someone is holding a cup.

Item Type:	Book Section
Additional Information:	2014 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2014; Kuching, Sarawak; Malaysia; 1 December 2014 through 4 December 2014
Subjects:	T Technology > T Technology (General) T Technology > TJ Mechanical Engineering and Machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	08 Apr 2015 05:52
Last Modified:	13 May 2015 05:24
URII:	http://shdl.mmu.edu.my/id/eprint/6162

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