Conference Paper

Enabling Calorie-Aware Cooking in a Smart Kitchen.

DOI: 10.1007/978-3-540-68504-3_11 Conference: Persuasive Technology, Third International Conference, PERSUASIVE 2008, Oulu, Finland, June 4-6, 2008. Proceedings
Source: DBLP

ABSTRACT As a daily activity, home cooking is an act of care for family members. Most family cooks are willing to learn healthy cooking. However, learning healthy cooking knowledge and putting the learned knowledge into real cooking practice are often difficult, due to non-trivial nutritional calculation of multiple food ingredients in a cooked meal. This work presents a smart kitchen with UbiComp technology to improve home cooking by providing calorie awareness of food ingredients used in prepared meals during the cooking process. Our kitchen has sensors to track the number of calories in food ingredients, and then provides real-time feedback to users on these values through an awareness display. Our user study suggests that bringing calorie awareness can be an effective means in helping family cooks maintain the healthy level of calories in their prepared meals.

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    ABSTRACT: The paper discusses a kitchen that intelligently senses cooking activities and provides realtime nutritional information helps facilitate healthy cooking by letting family cooks make informed decisions. It creates opportunities to embed pervasive computing in a smart kitchen to facilitate healthy cooking.
    IEEE Pervasive Computing 01/2010; 9:58-65. · 2.06 Impact Factor
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    ABSTRACT: To utilise the vast recipe databases on the In-ternet in intelligent nutritional assistance or recommender systems, it is important to have accurate nutritional data for recipes. Unfortunately, most online recipes have no such data available or have data of suspect quality. In this pa-per we present a system that automatically calculates the nutritional value of recipes sourced from the Internet. This is a challenging problem for several reasons, including lack of formulaic structure in ingredient descriptions, ingredi-ent synonymy, brand names, and unspecific quantities be-ing assigned. We present a system that exploits linguistic properties of ingredient descriptions and nutritional knowl-edge modelled as rules to estimate the nutritional content of recipes. We evaluate the system on a large Internet sourced recipe database (23.5k recipes) and examine performance in terms of ability to recognise ingredients and error in nutri-tional values against values established by human experts. Our results show that our system can match all of the in-gredients for 91% of recipes in the collection and generate nutritional values within a 10% error bound from human assessors for calorie, protein and carbohydrate values. We show that the error is less than that between multiple hu-man assessors and also less than the error reported for dif-ferent standard measures of estimating nutritional intake.
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    Advanced Machine Learning Technologies and Applications, Edited by AboulElla Hassanien, Abdel-BadeehM. Salem, Rabie Ramadan, Tai-hoon Kim, 01/2012: chapter 42: pages 423-429; Springer Berlin Heidelberg., ISBN: 9783642353253

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