Using low-Cost sensing to support nutritional awareness

A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications

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Charting past, present, and future research in ubiquitous computing

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Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire

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Food likes and their relative importance in human eating behavior: review and preliminary suggestions for health promotion

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Dairy foods and bone health: examination of the evidence

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Nutritional Epidemiology: Issues and Challenges

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Menu planning by computer

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Interaction techniques for ambiguity resolution in recognition-based interfaces

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Nutrition for a lifetime system: a multimedia system for altering food supermarket shoppers' purchases to meet nutritional guidelines

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Designing computers with people in mind

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The Diet-Aware Dining Table: Observing Dietary Behaviors over a Tabletop Surface

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Enabling Calorie-Aware Cooking in a Smart Kitchen

Chi, P.-Y., Chen, J., Chu, H.-H., & Lo, J. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-68504-3_11 · Cited by 102 · see citing works

Designing for human–food interaction: An introduction to the special issue on ‘food and interaction design’

Comber, R., Choi, J. H.-J., Hoonhout, J., & O'Hara, K. P. (2013) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2013.09.001 · Cited by 90 · see citing works

Ubiquitous Advertising: The Killer Application for the 21st Century

Krumm, J. C. (2010) · IEEE Pervasive Computing · doi:10.1109/mprv.2010.21 · Cited by 74 · see citing works

An offline mobile nutrition monitoring intervention for varying-literacy patients receiving hemodialysis: a pilot study examining usage and usability

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Food and Mood: Just-in-Time Support for Emotional Eating

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Predicting "About-to-Eat" Moments for Just-in-Time Eating Intervention

Rahman, T., Czerwinski, M. P., Gilad-Bachrach, R., & Johns, P. (2016) · International Conference on Digital Health · doi:10.1145/2896338.2896359 · Cited by 64 · see citing works

Embedded Assessment: Overcoming Barriers to Early Detection with Pervasive Computing

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Feasibility of identifying eating moments from first-person images leveraging human computation

Thomaz, E., Parnami, A., Essa, I. A., & Abowd, G. D. (2013) · doi:10.1145/2526667.2526672 · Cited by 62 · see citing works

Collectivistic health promotion tools: Accounting for the relationship between culture, food and nutrition

Parker, A. G., & Grinter, R. E. (2013) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2013.08.008 · Cited by 57 · see citing works

Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System

Majjodi, A. E., Starke, A. D., & Trattner, C. (2022) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3503252.3531312 · Cited by 45 · see citing works

Food Recommendation Using Ontology and Heuristics

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Persuasion not required Improving our understanding of the sociotechnical context of dietary behavioural change

Maitland, J., Chalmers, M., & Siek, K. A. (2009) · Proceedings of the 3d International ICST Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth2009.5928 · Cited by 39 · see citing works

Sensing Technologies and the Player-Middleware for Context-Awareness in Kitchen Environments

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Prototypes in the Wild: Lessons from Three Ubicomp Systems

Carter, S., & Mankoff, J. C. (2005) · IEEE Pervasive Computing · doi:10.1109/mprv.2005.84 · Cited by 35 · see citing works

Homemade cookbooks

Davis, H., Nansen, B., Vetere, F., Robertson, T., Brereton, M. F., Durick, J., & Vaisutis, K. (2014) · Designing Interactive Systems Conference · doi:10.1145/2598510.2598590 · Cited by 28 · see citing works

Automatic classification of daily fluid intake

Lester, J., Tan, D. S., Patel, S., & Brush, A. J. B. (2010) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth2010.8906 · Cited by 27 · see citing works

Recognition of cooking activities through air quality sensor data for supporting food journaling

Gerina, F., Massa, S. M., Moi, F., Recupero, D. R., & Riboni, D. (2020) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-020-00235-9 · Cited by 23 · see citing works

Affecting Our Perception of Satiety by Changing the Size of Virtual Dishes Displayed with a Tabletop Display

Sakurai, S., Narumi, T., Ban, Y., Tanikawa, T., & Hirose, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39420-1_11 · Cited by 20 · see citing works

Illusion cup

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Survey of Sensor-Based Personal Wellness Management Systems

Yumak, Z., & Pu, P. (2013) · BioNanoScience · doi:10.1007/s12668-013-0099-0 · Cited by 17 · see citing works

Towards a Pervasive Kitchen Infrastructure for Measuring Cooking Competence

Wagner, J., Plötz, T., van Halteren, A., Moynihan, P. J., Hoonhout, J., Pham, C., Jackson, D., Ladha, C., Ladha, K., & Olivier, P. L. (2011) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth.2011.246101 · Cited by 14 · see citing works

Research directions in recommender systems for health and well-being

Hauptmann, H., Said, A., & Trattner, C. (2022) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-022-09349-4 · Cited by 13 · see citing works

FoodCoach: Fully Automated Diet Counseling

Wu, J., Mayer, S., Pilz, S., Antille, Y. S., Albert, J. L., Stoll, M., García, K., Fuchs, K., Bally, L., Eichelberger, L., et al. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3540899 · Cited by 11 · see citing works

Kitchen AS-A-PAL: Exploring Smart Objects as Containers, Surfaces and Actuators

Surie, D., Lindgren, H., & Qureshi, A. (2013) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-00566-9_22 · Cited by 11 · see citing works

Mobile Computing and Artificial Intelligence for Diet Management

Mazzei, A., Luca Anselma, A., Michieli, F. D., Bolioli, A., Casu, M., Gerbrandy, J., & Lunardi, I. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23222-5_42 · Cited by 9 · see citing works

Food Assistant for Consumer Behaviour Change through Citizen Science and AI

Casado–Mansilla, D., Gómez–Carmona, O., Fernández–de–Retana, M., Muzzioli, L., Kušar, A., Vandevijvere, S., & López–de–Ipiña, D. (2024) · International Conference on Smart and Sustainable Technologies (SpliTech) · doi:10.23919/splitech61897.2024.10612651 · Cited by 7 · see citing works

An Evaluation of a Meal Planning System: Ease of Use and Perceived Usefulness

Aberg, J. (2009) · Electronic workshops in computing · doi:10.14236/ewic/hci2009.33 · Cited by 7 · see citing works

Toward Supporting Food Journaling Using Air Quality Data Mining and a Social Robot

Gerina, F., Pes, B., Recupero, D. R., & Riboni, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34255-5_22 · Cited by 5 · see citing works

Digital Receipts: Fostering Mobile Payment Adoption

Ho, E. A., Apostu, S., Michahelles, F., & Ilic, A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-03647-2_10 · Cited by 5 · see citing works

Crowd saucing

Linehan, C., Leeman, T., Borrowdale, C., & Lawson, S. W. (2013) · interactions · doi:10.1145/2405716.2405729 · Cited by 4 · see citing works

Monitoring Food Intake in an Aging Population: A Survey on Technological Solutions

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Inferring the Everyday Task Capabilities of Locations

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Product or Item‐Based Recommender System

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Need for development of dietary assessment device for free living environment-a survey

Karthick, K., Sudha, R., & Ramanathan, P. (2015) · International Conference on Electrical, Computer and Communication Technologies (ICECCT) · doi:10.1109/icecct.2015.7226135 · Cited by 2 · see citing works

AIM in Eating Disorders

Kopyto, D., Uhlenberg, L., Zhang, R., Stonawski, V., Horndasch, S., & Amft, O. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_213 · Cited by 2 · see citing works

The challenge of transdisciplinarity: Design methods for agri-food innovation and sustainability

Massari, S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-817821-8.00020-5 · Cited by 2 · see citing works

CheckYourMeal!: diet management with NLG

Luca Anselma, A., Donetti, S., Mazzei, A., & Pirone, A. (2018) · doi:10.18653/v1/w18-6710 · Cited by 2 · see citing works

Opportunities and Challenges of Integrating Food Practice into Clinical Decision-Making

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Sensor Datasets for Human Daily Safety and Well-Being

Cauli, N., Massa, S. M., Recupero, D. R., & Riboni, D. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-38281-9_1 · Cited by 1 · see citing works

Food & Mood: Explorations in Technological Intervention

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AIM in Eating Disorders

Kopyto, D., Uhlenberg, L., Zhang, R., Stonawski, V., Horndasch, S., & Amft, O. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_213-1

Consumer 4.0 on the food market

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Proceedings of the Workshop on Intelligent Interactive Systems and Language Generation (2IS&NLG)

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Detecting Impending Malnutrition of (Pre–) Frail Older Adults in Domestic Smart Home Environments

Friedrich, B. (2024) · Springer eBooks · doi:10.1007/978-3-658-44688-8_3

Speaking of Food: Understanding How People Talk About Food Experiences

Kang, Y., Beşevli, C., Subramanian, S., & Obrist, M. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791474

Household Nutrition Analysis and Food Recommendation U sing Purchase History

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Nudging Grocery Shoppers to Make Healthier Choices

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Situated and mobile displays for reflection on shopping and nutritional choices

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Recipe4Me:a personalized recipe recommender design based on nutrient information

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Adaptive Implicit Interaction for Healthy Nutrition and Food Intake Supervision

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