Article

Need for Technological Innovation in Dietary Assessment

National Cancer Institute, Division of Cancer Control and Population Sciences, Applied Research Program, Risk Factor Monitoring and Methods Branch, Bethesda, MD 20892-7344, USA.
Journal of the American Dietetic Association (Impact Factor: 3.92). 01/2010; 110(1):48-51. DOI: 10.1016/j.jada.2009.10.008
Source: PubMed
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Available from: Frances E Thompson
    • "These methods are riddled with problems due to underreporting and miscalculation of food consumption [2]. New approaches are required for objective assessment of freeliving food intake linking with daily activity patterns [3]. Increasing research in this direction is performed in recent years [4]. "
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    ABSTRACT: Assessment of food intake has a wide range of applications in public health and lifestyle related chronic disease management. In this paper, we propose a real-time food recognition platform combined with daily activity and energy expenditure estimation. In the proposed method, food recognition is based on hierarchical classification using multiple visual cues, supported by efficient software implementation suitable for real-time mobile device execution. A Fischer Vector representation together with a set of linear classifiers are used to categorize food intake. Daily energy expenditure estimation is achieved by using the built-in inertial motion sensors of the mobile device. The performance of the vision-based food recognition algorithm is compared to the current state-of-the-art, showing improved accuracy and high computational efficiency suitable for real-time feedback. Detailed user studies have also been performed to demonstrate the practical value of the software environment.
    No preview · Conference Paper · Jun 2015
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    • "These methods are riddled with problems due to underreporting and miscalculation of food consumption [2]. New approaches are required for objective assessment of freeliving food intake linking with daily activity patterns [3]. Increasing research in this direction is performed in recent years [4]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Assessment of food intake has a wide range of applications in public health and lifestyle related chronic disease management. In this paper, we propose a real-time food recognition platform combined with daily activity and energy expenditure estimation. In the proposed method, food recognition is based on hierarchical classification using multiple visual cues, supported by efficient software implementation suitable for real-time mobile device execution. A Fischer Vector representation together with a set of linear classifiers are used to categorize food intake. Daily energy expenditure estimation is achieved by using the built-in inertial motion sensors of the mobile device. The performance of the vision-based food recognition algorithm is compared to the current state-of-the-art, showing improved accuracy and high computational efficiency suitable for real-time feedback. Detailed user studies have also been performed to demonstrate the practical value of the software environment.
    Full-text · Conference Paper · Jun 2015
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    • "Other methods for MIB such as food frequency questionnaires and diet diaries are inaccurate due to subjects tending to underreport and miscalculate food consumption [9]. Thus, new approaches for objective and accurate assessment of free-living food intake patterns in humans are necessary for monitoring of eating behavior [10]. "
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    ABSTRACT: Objective monitoring of food intake and ingestive behavior in a free-living environment remains an open problem that has significant implications in study and treatment of obesity and eating disorders. In this paper, a novel wearable sensor system (automatic ingestion monitor, AIM) is presented for objective monitoring of ingestive behavior in free living. The proposed device integrates three sensor modalities that wirelessly interface to a smartphone: a jaw motion sensor, a hand gesture sensor, and an accelerometer. A novel sensor fusion and pattern recognition method was developed for subject-independent food intake recognition. The device and the methodology were validated with data collected from 12 subjects wearing AIM during the course of 24 h in which both the daily activities and the food intake of the subjects were not restricted in any way. Results showed that the system was able to detect food intake with an average accuracy of 89.8%, which suggests that AIM can potentially be used as an instrument to monitor ingestive behavior in free-living individuals.
    Full-text · Article · Jun 2014 · IEEE transactions on bio-medical engineering
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