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3 September 2009 Algorithms for the detection of chewing behavior in dietary monitoring applications
Mark S. Schmalz, Abdelsalam Helal, Andres Mendez-Vasquez
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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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mark S. Schmalz, Abdelsalam Helal, and Andres Mendez-Vasquez "Algorithms for the detection of chewing behavior in dietary monitoring applications", Proc. SPIE 7444, Mathematics for Signal and Information Processing, 74440E (3 September 2009); https://doi.org/10.1117/12.829205
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Cited by 3 scholarly publications.
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KEYWORDS
Mouth

Video

Detection and tracking algorithms

Electromyography

Digital signal processing

Error analysis

Motion analysis

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