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Issue No.02 - April-June (2009 vol.8)
pp: 62-70
Oliver Amft , TU Eindhoven
Automatic dietary monitoring uses sensors to recognize eating behavior, offering vital, continuous infor­mation about a user's food intake. Using this information, administrators can adapt and personalize weight and diet coach­ing programs. The data can also inform nutrition research on diet and eating behaviors.
activity recognition, intake cycle, intake gestures, chewing sounds, swallowing, pervasive healthcare
Oliver Amft, "On-Body Sensing Solutions for Automatic Dietary Monitoring", IEEE Pervasive Computing, vol.8, no. 2, pp. 62-70, April-June 2009, doi:10.1109/MPRV.2009.32
1. R.R. Wing and S. Phelan, "Long-Term Weight Loss Maintenance," American J. Clinical Nutrition, vol. 82, 2005, pp. 222S–225S.
2. J.C. Witschi, "Short-Term Dietary Recall and Recording Methods," Nutritional Epidemiology, vol. 4,Oxford Univ. Press, 1990, pp. 52–68.
3. D.A. Schoeller, "Limitations in the Assessment of Dietary Energy In-take by Self-Report," Metabolism: Clinical and Experimental, vol. 44, no. 2, 1995, pp. 18–22.
4. H. Junker et al., "Gesture Spotting with Body-Worn Inertial Sensors to Detect User Activities," Pattern Recognition, vol. 41, no. 6, 2008, pp. 2010–2024.
5. O. Amft et al., "Analysis of Chewing Sounds for Dietary Monitoring," Proc. 7th Int'l Conf. Ubiquitous Comp., LNCS 3660, Springer Verlag, 2005, pp. 56–72.
6. O. Amft and G. Tröster, "Recognition of Dietary Activity Events Using On-Body Sensors," Artificial Intelligence in Medicine, vol. 42, no. 2, 2008, pp. 121–136.
7. C.S. Lear, J.B. Flanagan, and C.F. Moorrees, "The Frequency Of Deglutition In Man," Archives of Oral Biology, vol. 10, 1965, pp. 83–100.
8. O. Amft and G. Tröster, "Methods for Detection and Classifica-tion of Normal Swallowing from Muscle Activation and Sound," Proc. 1stInt'l Conf. Pervasive Comp. Technologies for Healthcare, IEEE CS Press, 2006, pp. 1–10.
9. T.L. Abell and J.R. Malagelada, "Electrogastrography," Digestive Diseases and Sciences, vol. 33, no. 8, 1988, pp. 982–992.
10. K. Yamaguchi et al., "Evaluation of Gastrointestinal Motility by Computerized Analysis of Abdominal Auscultation Findings," J. Gastroenterology and Hepatology, vol. 21, no. 3, 2006, pp. 510–514.
11. M.S. Westerterp-Plantenga, L. Wouters, and F. ten Hoor, "Deceleration in Cumulative Food Intake Curves, Changes in Body Temperature and Diet-Induced Thermogenesis," Physiology &Behavior, vol. 48, no. 6, 1990, pp. 831–836.
12. H.R. Farshchi, M.A. Taylor, and I.A. Macdonald, "Decreased Thermic Effect of Food after an Irregular Compared with a Regular Meal Pattern in Healthy Lean Women," Int'l J. Obesity and Related Metabolic Disorders, vol. 28, no. 5, 2004, pp. 653–660.
13. H. Hsiao, J. Guan, and M. Weatherly, "Accuracy and Precision of Two In-Shoe Pressure Measurement Systems," Ergonomics, vol. 45, no. 8, 2002, pp. 537–555.
14. D.R. Parker et al., "Postprandial Mesenteric Blood Flow in Humans: Relationship to Endogenous Gastrointestinal Hormone Secretion and Energy Content of Food," Euro. J. Gastroenterology and Hepatology, vol. 7, no. 5, 1995, pp. 435–440.
15. E. Gualdi-Russo and S. Toselli, "Influence of Various Factors on the Measurement of Multifrequency Bioimpedance," Homo, vol. 53, no. 1, 2002, pp. 1–16.
16. O. Amft, M. Kusserow, and G. Tröster, "Probabilistic Parsing of Dietary Activity Events," Proc. Int'l Workshop Wearable and Implantable Body Sensor Networks, vol. 13,Springer Verlag, 2007, pp. 242–247.
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