A Smart Kitchen Infrastructure

Ständer, Marcus; Hadjakos, Aristotelis; Lochschmidt, Niklas; Klos, Christian; Renner, Bastian; Mühlhäuser, Max

doi:10.1109/ism.2012.27

Cited by 16 publications

(4 citation statements)

References 7 publications

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“…Plenty of research works and projects have been proposed, such as a smart kitchen based on the Internet of Things (IoT) [ 2 ], user centric smart kitchen [ 3 ], AREasyCooking [ 4 ], and CounterIntelligence [ 5 ]. Projects regarding smart kitchens typically require the use of a large number of sensors to detect kitchen appliances, ingredients, and other objects that are necessary for cooking [ 6 ]. These sensors include temperature sensors, humidity sensors, IR flame sensors, and passive infrared sensors.…”

Section: Related Workmentioning

confidence: 99%

Augmented Reality Based Interactive Cooking Guide

Majil

Yang

2022

Sensors

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Cooking at home is a critical survival skill. We propose a new cooking assistance system in which a user only needs to wear an all-in-one augmented reality (AR) headset without having to install any external sensors or devices in the kitchen. Utilizing the built-in camera and cutting-edge computer vision (CV) technology, the user can direct the AR headset to recognize available food ingredients by simply looking at them. Based on the types of the recognized food ingredients, suitable recipes are suggested accordingly. A step-by-step video tutorial providing details of the selected recipe is then displayed with the AR glasses. The user can conveniently interact with the proposed system using eight kinds of natural hand gestures without needing to touch any devices throughout the entire cooking process. Compared with the deep learning models ResNet and ResNeXt, experimental results show that the YOLOv5 achieves lower accuracy for ingredient recognition, but it can locate and classify multiple ingredients in one shot and make the scanning process easier for users. Twenty participants test the prototype system and provide feedback via two questionnaires. Based on the analysis results, 19 of the 20 participants would recommend others to use the proposed system, and all participants are overall satisfied with the prototype system.

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Section: Related Workmentioning

confidence: 99%

Augmented Reality Based Interactive Cooking Guide

Majil

Yang

2022

Sensors

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“…Many electric appliances are used while cooking, and there is a system using sensor-embedded appliances [10]. The embedded sensors sense user's use of the appliance.…”

Section: Electric Power Consumption Sensormentioning

confidence: 99%

KUSK dataset

Hashimoto

Sasada

Yamakata

et al. 2014

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

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In this paper, we provide a multimodal dataset for understanding cooking activities. To build the dataset, we instructed the subjects to perform cooking according to instructional texts shown on a display one by one. The instructional texts were generated from flow graphs, which were automatically extracted from recipes sampled from a Web site. The main identity of this dataset is the correspondence between the steps automatically extracted from recipes, and real human activities. Typical uses of our dataset are to construct classifiers for understanding human activities in the kitchen, text generation through observing the activities, and so on.

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“…Intelligent technology and algorithms, such as hierarchical task networks and problem solving algorithms, provide a rich foundation for enabling objects to reason about complex tasks and compute on-thefly plans that can be used to guide the user. For example, within the SmartProducts EU project, a solution combining ontologies, dynamic problem solving, and context-aware workflows [Ständer 2012] was developed and applied to dynamically compute meal plans and guide the user through meal preparation.…”

Section: The Simplicity-featurism Paradoxmentioning

confidence: 99%

Introduction to the special issue on interaction with smart objects

Schreiber

Luyten

Mühlhäuser

et al. 2013

ACM Trans. Interact. Intell. Syst.

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Smart objects can be smart because of the information and communication technology that is added to human-made artifacts. It is not, however, the technology itself that makes them smart but rather the way in which the technology is integrated, and their smartness surfaces through how people are able to interact with these objects. Hence, the key challenge for making smart objects successful is to design usable and useful interactions with them. We list five features that can contribute to the smartness of an object, and we discuss how smart objects can help resolve the simplicity-featurism paradox. We conclude by introducing the three articles in this special issue, which dive into various aspects of smart object interaction: augmenting objects with projection, service-oriented interaction with smart objects via a mobile portal, and an analysis of input-output relations in interaction with tangible smart objects.

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A Smart Kitchen Infrastructure

Cited by 16 publications

References 7 publications

Augmented Reality Based Interactive Cooking Guide

Augmented Reality Based Interactive Cooking Guide

KUSK dataset

Introduction to the special issue on interaction with smart objects

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