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Proceedings Paper

Algorithms for the detection of chewing behavior in dietary monitoring applications
Author(s): Mark S. Schmalz; Abdelsalam Helal; Andres Mendez-Vasquez
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Paper Abstract

The detection of food consumption is key to the implementation of successful behavior modification in support of dietary monitoring and therapy, for example, during the course of controlling obesity, diabetes, or cardiovascular disease. Since the vast majority of humans consume food via mastication (chewing), we have designed an algorithm that automatically detects chewing behaviors in surveillance video of a person eating. Our algorithm first detects the mouth region, then computes the spatiotemporal frequency spectrum of a small perioral region (including the mouth). Spectral data are analyzed to determine the presence of periodic motion that characterizes chewing. A classifier is then applied to discriminate different types of chewing behaviors. Our algorithm was tested on seven volunteers, whose behaviors included chewing with mouth open, chewing with mouth closed, talking, static face presentation (control case), and moving face presentation. Early test results show that the chewing behaviors induce a temporal frequency peak at 0.5Hz to 2.5Hz, which is readily detected using a distance-based classifier. Computational cost is analyzed for implementation on embedded processing nodes, for example, in a healthcare sensor network. Complexity analysis emphasizes the relationship between the work and space estimates of the algorithm, and its estimated error. It is shown that chewing detection is possible within a computationally efficient, accurate, and subject-independent framework.

Paper Details

Date Published: 3 September 2009
PDF: 11 pages
Proc. SPIE 7444, Mathematics for Signal and Information Processing, 74440E (3 September 2009); doi: 10.1117/12.829205
Show Author Affiliations
Mark S. Schmalz, Univ. of Florida (United States)
Abdelsalam Helal, Univ. of Florida (United States)
Andres Mendez-Vasquez, CINVESTAV Guadalajara (Mexico)


Published in SPIE Proceedings Vol. 7444:
Mathematics for Signal and Information Processing
Franklin T. Luk; Mark S. Schmalz; Gerhard X. Ritter; Junior Barrera; Jaakko T. Astola, Editor(s)

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