Cooking procedure recognition and inference in sensor embedded kitchen

Nakauchi, Yasushi; Suzuki, Takuo; Tokumasu, Akira; Murakami, Sho

doi:10.1109/roman.2009.5326050

Cited by 8 publications

(1 citation statement)

References 6 publications

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“…Each interaction of users, utensils and ingredients could be recognized and classified. This way, the system could record and interpret the cooking steps and put them into a semantic and temporal context [13], [8].…”

Section: B Interaction Recognitionmentioning

confidence: 99%

MAMPF: An Intelligent Cooking Agent for Zoneless Stoves

Reichel

Müller

Stamm

et al. 2011

2011 Seventh International Conference on Intelligent Environments

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Abstract-The Multifunctional and Adaptive Meal Preparation Facility (MAMPF) is a versatile and adaptive kitchen system allowing even an inexperienced user to create ambitious dishes. On the hardware side, the system controls hotplates by regulating the temperature of heated regions to turn the attention to the food actually being heated instead of the hotplate. Furthermore, the user interaction with the computer system is adapted to the requirements in a kitchen. On the work flow side, MAMPF converts a recipe into a task tree which is used to guide the user through the cooking process. By knowing the recipe, based on a formal description specially developed for this system, the optimal sequence of actions can be calculated.The system is designed to support the user without limiting spontaneity or creativity and can -if necessary -be used just like any usual stove. Consequentially, the user always retains control over the system, all settings can be overwritten at any point, and the order of the tasks can be dynamically rearranged.In order to prove the correctness of this concept, a prototype has been implemented.

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Section: B Interaction Recognitionmentioning

confidence: 99%

MAMPF: An Intelligent Cooking Agent for Zoneless Stoves

Reichel

Müller

Stamm

et al. 2011

2011 Seventh International Conference on Intelligent Environments

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Enhancing Kitchen Activity Recognition: A Benchmark Study of the Rostock KTA Dataset

Zolfaghari,

Stoev,

Yordanova

2024

IEEE Access

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With the global population aging, the demand for technologies facilitating independent living, especially for those with cognitive impairments, is increasing. This paper addresses this need by conducting a comprehensive evaluation of the Rostock Kitchen Task Assessment dataset, a pivotal resource in kitchen task activity recognition. Our study begins with an in-depth introduction, emphasizing the increasing prevalence of neurodegenerative disorders and the crucial role of assistive technologies. Our contributions encompass a systematic literature review, design and implementation of a working prototype of our envisioned system, refinement of the Rostock Kitchen Task Assessment dataset, creation of a semantically annotated dataset, extraction of statistical features, comparative analysis, and rigorous model performance assessment. The core of our work is the thorough evaluation and benchmarking of different activity recognition approaches using the aforementioned Rostock Kitchen Task Assessment dataset. Our experimental results demonstrate that despite encountering an imbalance problem in the dataset, the fusion of the Hidden Markov Model and Random Forest leads to superior results, achieving a weightedaveraged F 1 -score of 74.10% for all available activities and 81.40% for the most common actions in the Rostock Kitchen Task Assessment dataset. Moreover, through systematic analysis, we identify strengths and suggest potential refinements, thereby advancing the field of kitchen activity recognition. This offers valuable insights for researchers and practitioners in assistive and remote care technologies.

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Visually-aided smart kitchen environment for senior citizens suffering from dementia

Asghar

Pulii

2013

2013 International Joint Conference on Awareness Science and Technology &Amp; Ubi-Media Computing (iCAST 2013 &Amp; UMEDIA 2013

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Cooking procedure recognition and inference in sensor embedded kitchen

Cited by 8 publications

References 6 publications

MAMPF: An Intelligent Cooking Agent for Zoneless Stoves

MAMPF: An Intelligent Cooking Agent for Zoneless Stoves

Enhancing Kitchen Activity Recognition: A Benchmark Study of the Rostock KTA Dataset

Visually-aided smart kitchen environment for senior citizens suffering from dementia

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