Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Algorithms for the detection of chewing behavior in dietary monitoring applications
AU - Schmalz, M.S.
AU - Helal, Sumi
AU - Mendez-Vasquez, A.
PY - 2009
Y1 - 2009
N2 - 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 SPIE.
AB - 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 SPIE.
KW - Behavior detection
KW - Computer vision
KW - Dietary monitoring
KW - Pattern recognition
KW - Cardio-vascular disease
KW - Complexity analysis
KW - Computational costs
KW - Computationally efficient
KW - Distance-based
KW - Embedded processing
KW - Estimated error
KW - Food consumption
KW - Monitoring applications
KW - Moving faces
KW - Periodic motion
KW - Spatiotemporal frequency
KW - Spectral data
KW - Surveillance video
KW - Temporal frequency
KW - Test results
KW - Algorithms
KW - Classifiers
KW - Data processing
KW - Food supply
KW - Learning systems
KW - Security systems
KW - Sensor networks
KW - Signal detection
KW - Spectroscopy
KW - Computational efficiency
U2 - 10.1117/12.829205
DO - 10.1117/12.829205
M3 - Journal article
VL - 7444
JO - Proceedings of SPIE
JF - Proceedings of SPIE
SN - 0277-786X
ER -