Abstract
Obesity is an increasing problem for modern societies, which implies enormous financial burdens for public health-care systems. There is growing evidence that a lack of cooking and food preparation skills is a substantial barrier to healthier eating for a significant proportion of the population. We present the basis for a technological approach to promoting healthier eating by encouraging people to cook more often. We integrated tri-axial acceleration sensors into kitchen utensils (knifes, scoops, spoons), which allows us to continuously monitor the activities people perform while acting in the kitchen. A recognition framework is described, which discriminates ten typical kitchen activities. It is based on a sliding-window procedure that extracts statistical features for contiguous portions of the sensor data. These frames are fed into a Gaussian mixture density classifier, which provides recognition hypotheses in real-time. We evaluated the activity recognition system by means of practical experiments of unconstrained food preparation. The system achieves classification accuracy of ca. 90% for a dataset that covers 20 persons’ cooking activities.
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References
National Institute for Health and Clinical Excellence. Obesity: the prevention, identification, assessment and management of overweight and obesity in adults and children (May, 2006).
B. A. Swinburn. Increased energy intake alone virtually explains all the increase in body weight in the united states from 1970s to the 2000s. In Proc. European Congress on Obesity, (2009).
G. Block, T. Block, P. Wakimoto, and C. H. Block, Demonstration of an e-mailed worksite nutrition intervention program, Preventing Chronic Disease. 1(4) (Jan, 2004).
S. Jebb, T. Steer, and C. Holmes. The ‘healthy living’ social marketing initiative: A review of the evidence (Mar, 2007).
J. Maitland, M. Chalmers, and K. A. Siek. Persuasion not required – improving our understanding of the sociotechnical context of dietary behavioural change. In Proc. Int. Conf. Pervasive Computing Technologies for Health Care (Feb, 2009).
C. Pham and P. Olivier. Slice&dice: Recognizing food preparation activities using embedded accelerometers. In Proc. Europ. Conf. Ambient Intelligence, pp. 34–43, (2009).
C. Byrd-Bredbenner, Food preparation knowledge and attitudes of young adults: Implications for nutrition practice,Topics in Clinical Nutrition. 19, 154–163, (2004).
World Health Organization. Diet, nutrition and the prevention of chronic diseases. WHO Technical Report Series, number 916, (2003).
E. Winkler and G. Turrell, Confidence to cook vegetables and the buying habits of australian households, Journal of the American Dietetic Association. 109(10), 1759–1768 (Oct., 2009).
N. I. Larson, M. Story, M. E. Eisenbergy, and D. Neumark-Sztainer, Food preparation and purchasing roles among adolescents: associations with sociodemographic characteristics and diet quality, Journal of the American Dietetic Association. 106, 211–218, (2006).
N. Sudo, D. Degeneffe, H. Vue, E. Merkle, J. Kinsey, K. Ghosh, and M. Reicks, Relationship between attitudes and indicators of obesity for midlife women, Health Educ. Behav. 36(6), 1082–1094, (2009).
L. Atallah and G. Yang, The use of pervasive sensing for behaviour profiling – a survey, Pervasive and Mobile Computing. pp. 447–464, (2009).
M. Beetz, J. Bandouch, A. Kirsch, A. Maldonado, and R. B. Rusu. The assistive kitchen—a demonstration scenario for cognitive technical systems. In IEEE 17th Int. Symp. Robot and Human Interactive Communication (RO-MAN), pp. 1–8 (Jan, 2008).
T. Huynh and B. Schiele. Analyzing features for activity recognition. In Proc. Joint Conf. on Smart Objects and AmI, pp. 159–163, (2005).
L. Bonanni, C. Lee, and T. Selker. CounterIntelligence: Augmented Reality Kitchen. In Proc. CHI, pp. 2239–2245, (2005).
P. Chi, J.-H. Chen, H.-H. Chu, and B.-Y. Chen. Enabling nutrition-aware cooking in a smart kitchen. In Proc. CHI – Extended Abstracts on Human Factors in Computing Systems, pp. 2333–Ð2338, (2007).
K. Chang, S.-Y. Liu, H.-H. Chu, J. Hsu, C. Chen, T.-Y. Lin, and P. Huang. Dietary-aware dining table: Observing dietary behaviors over tabletop surface. In Proc. Int. Conf. Pervasive Computing, pp. 366–Ð382, (2006).
Q. T. Tran, G. Calcaterra, and E. D. Mynatt. Cooks collage: Déjà vu display for a home kitchen. In Proc. Int. Conf. Home-Oriented Informatics and Telematics (HOIT), pp. 15–Ð32, (2005).
House_n. http://architecture.mit.edu/house_n/ – visited 9th April 2010.
S. S. Intille, K. Larson, E. Mungia-Tapia, J. S. Beaudin, P. Kaushik, J. Nawyn, and R. Rockinson. Using a live-in laboratory for ubiquitous computing research. In Proc. Int. Conf. Pervasive Computing, pp. 349–365 (Dec, 2006).
E. Mungia-Tapia, S. S. Intille, and K. Larson. Activity recognition in the home setting using simple and ubiquitous sensors. In Proc. Int. Conf. Pervasive Computing, (2004).
L. Bao and S. S. Intille. Activity recognition from user-annotated acceleration data. In Proc. Int. Conf. Pervasive Computing, (2004).
Quality of Life Technology Center – QoLT. http://www.cmu.edu/qolt/ – visited 9th April 2010.
E. H. Spriggs, F. D. L. Torre, and M. Hebert. Temporal segmentation and activity classification from first-person sensing. In IEEE Workshop on Egocentric Vision, CVPR 2009 (June, 2009).
P. Olivier, A. Monk, G. Xu, and J. Hoey. Ambient kitchen: Designing situated services using a high fidelity prototyping environment. In Workshop on Affect & Behaviour Related Assistance in the Support of the Elderly, PETRA-09, (2009).
H. Hoonhout. ExperienceLabs: investigating peopleõs experiences in realistic lab settings. In Proc. Int. Conf. Designing Pleasurable Products and Interfaces (DPPI), (2007).
T. Pl\"{o}tz and G. A. Fink, Pattern recognition methods for advanced stochastic protein sequence analysis using HMMs, Pattern Recognition, Special Issue on Bioinformatics. 39, 2267--2280, (2006)
C. J. Leggetter and P. C. Woodland, Maximum likelihood linear regression for speaker adaptation of continuous density Hidden Markov Models, Computer Speech & Language. pp. 171–185, (1995).
J.-L. Gauvain and C.-H. Lee. Map estimation of continuous density HMM: Theory and applications. In Proc. DARPA Speech and Natural Language Workshop, (1992).
A. Dempster, L. N.M., and D. Rubin, Maximum likelihood from incomplete data via the em algorithm, Journal of the Royal Statistical Society. 39, 1–38, (1977). Series B (methodological).
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Plötz, T., Moynihan, P., Pham, C., Olivier, P. (2011). Activity Recognition and Healthier Food Preparation. In: Chen, L., Nugent, C., Biswas, J., Hoey, J. (eds) Activity Recognition in Pervasive Intelligent Environments. Atlantis Ambient and Pervasive Intelligence, vol 4. Atlantis Press. https://doi.org/10.2991/978-94-91216-05-3_14
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DOI: https://doi.org/10.2991/978-94-91216-05-3_14
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