In the past few years, the general work habits of people have changed dramatically, raising concerns about their well-being. Numerous health-related problems have been observed from their health records such as obesity, diabetes or heart diseases, and unhealthy eating is one of its factors. But these problems can be prevented if people start eating healthy food. The population, in general, is also realizing that healthy eating is important for their well-being. However, they usually resist because they assume that healthy food is not tasty and they do not want to comprise their taste preferences. Moreover, they have various other considerations that become barriers for them while selecting a healthy recipe. These are:(1) their complex, restrained needs (i.e., allergies and nutritional goals), (2) their strict lifestyle or dietary preferences (i.e., their desire to eat only vegan or vegetarian food), (3) lack of knowledge about how to choose healthy recipes while exploiting their taste preferences, (4) choosing recipes that maximize the use of available ingredients in their kitchen. Numerous researchers have been working in this field and developed various applications and systems to suggest healthy recipes. Apart from unhealthy eating, household food wastage has become a public problem, and some of the causes, which trigger it are users’ taste preferences (i.e., disliking of the food), and not cooking food before ingredients expiry dates. Thus, we propose a personalized recipes recommender system as a proof of concept called CAPRECIPES, which is based on context-awareness. It tackles the aforementioned barriers and improves the users’ experiences by providing the recommendations of personalized recipes with minimal efforts while exploiting their dynamically changing contexts. CAPRECIPES also helps in the reduction of food wastage as it first shows the recipes, which contain the ingredients that are expiring soon and matches with users’ taste preferences. It also considers that recipes do not violate users’ health restrictions and nutritional goals, and use the maximum number of available ingredients in users’ kitchen. The proposed system gathers users’ taste preferences by exploiting two third-party social media applications (i.e., Facebook and YouTube) and collaborative-based filtering algorithm. This thesis also explores various natural language processing techniques such as text analysis and parts of speech tagging to identify the recipes’ preferences and to find the most relevant match for each recipe or ingredient having different names. / Graduate

The term “IoT” has evolved to encompass a wide range of diffuse concepts, but the common thread among the myriad definitions has been the convergence of technology to bring <i>advanced conveniences</i> to our every day, but complicated, lives. A long-term focus of the Collaborative Robotics Lab, and a particular focus of many with interests in consumer assistance, has been the kitchen, which acts as the “nerve center” of the home in many cultures. However, despite the grand vision of revolutionizing the kitchen and improving our lifestyles with technology, what today’s IoT-integrated appliances and kitchen-focused conveniences offer is mainly limited to a remote control. While remote control is certainly convenient, it still requires human planning in both cognitive and physical loads in performing cooking activities. The goal of this thesis is to build a framework of the network of IoT-enabled kitchen appliances, <i>TupperwareEarth</i> for the “Internet of Kitchen Things” integrated with an inference engine that utilizes ontology as a knowledge database. From simple clustering of sensor data to recommender systems that employ crowd-sourced preference data, the cognitive burden is reduced with proactive suggestions to high-level queries based upon the current kitchen state. Through the progression of the studies in the “Internet of Kitchen Things,” <i>TupperwareEarth </i>aims to reduce human planning that involves both cognitive and physical loads of burden by inferring solutions to the activities of daily kitchen living using ontological semantics.

Detta kandidatarbete kommer undersöka hur vi designar en smart applikation i köket för att bidra till ett minskat matslöseri. Vi går in i undersökningen med frågan “Hur kan en smart applikation i köket göra vardagen trevligare och lättare, samt bidra till ett minskat matslöseri?”. Vi forskar inom matslöseri, smarta hem, smarta kylskåp och QR-koder. Utifrån denna forskning designar vi upp en prototyp för ett smart kök i form av en applikation. Vi bildar en egen uppfattning på konceptet det smarta köket. Det kommer finnas en genomgång på hur vi gestaltar vår lösning till frågeställningen och vilka metoder som använts för att komma fram till slutresultatet. / This Bachelor Thesis will examine how to design a smart appliance in the kitchen to help reduce food waste. We go into the survey with the question "How can a smart application in the kitchen make the living day more enjoyable and easier, as well as contribute to a reduced food waste?". We research in food waste, smart homes, smart refrigerators and QR codes. Based on this research, we design a prototype for a smart kitchen in the form of a mobile application. We form our own perception of the concept of the smart kitchen. There will be a review of how we shape our solution to the question and the methods used to arrive at the final result.