Smart Campus Food Ordering and Recommendation System with Emotion Booster: A First Design

Authors

  • Akeem Olowolayemo Department of Computer Science, International Islamic University Malaysia, Kuala Lumpur, Malaysia
  • Ahmed Faisal Department of Computer Science, KICT, International Islamic University Malaysia, Kuala Lumpur, Malaysia
  • Muhammad Ismail Department of Computer Science, KICT, International Islamic University Malaysia, Kuala Lumpur, Malaysia

DOI:

https://doi.org/10.31436/ijpcc.v10i1.454

Keywords:

food recommendation system, facial emotion detection, user preference, mood detection, food ordering system, food delivery system

Abstract

A healthy food intake is necessary for every person to function to their optimum potential. Apart from keeping the body fit and full of energy, food is also known to boost people’s moods and ward off negative emotions. Typically, people order food, considering their budget, the time of the day as well as what they are craving. Consequently, this study proposes a system that can detect users’ moods depending on their facial expression and accordingly, recommends food that they usually order during that particular emotion or related food, to subsequently improve how they feel. The system keeps track of a user’s budget, the time of the day, the users’ current emotions, and provides recommendations with a view to boosting their mood through foods that they like or through foods that are scientifically proven to help improve their mood. The system is intertwined with a campus food ordering system specifically designed for on-campus food stalls in their respective hostels. This food ordering system allows us to get an insight into the student’s preferences and include them in our recommendations as well as providing a delivery system that allows students to save time from standing in queues usually during rush hour. The usability evaluation conducted to evaluate the system proved successful as all the users that evaluated the system provided positive feedback and most of the tasks assigned to them were satisfactorily completed.

References

M. Singh, “Mood, food and obesity,” Front Psychol, vol. 5, no. AUG, pp. 1–35, 2014, doi: 10.3389/fpsyg.2014.00925.

M. P. Gardner, B. Wansink, J. Kim, and S. B. Park, “Better moods for better eating?: How mood influences food choice,” Journal of Consumer Psychology, vol. 24, no. 3, pp. 320–335, 2014, doi: 10.1016/j.jcps.2014.01.002.

P. Bongers, A. Jansen, R. Havermans, A. Roefs, and C. Nederkoorn, “Happy eating: The underestimated role of overeating in a positive mood,” Appetite, vol. 67, pp. 74–80, Aug. 2013, doi: 10.1016/j.appet.2013.03.017.

Q. Huang, H. Liu, K. Suzuki, S. Ma, and C. Liu, “Linking what we eat to our mood: A review of diet, dietary antioxidants, and depression,” Antioxidants, vol. 8, no. 9. MDPI, Sep. 01, 2019. doi: 10.3390/antiox8090376.

P. N. A. Dharmayani, M. Juergens, M. Allman-Farinelli, and S. Mihrshahi, “Association between fruit and vegetable consumption and depression symptoms in young people and adults aged 15–45: A systematic review of cohort studies,” International Journal of Environmental Research and Public Health, vol. 18, no. 2. MDPI AG, pp. 1–22, Jan. 02, 2021. doi: 10.3390/ijerph18020780.

R. Winzer, K. Sorjonen, and L. Lindberg, “What predicts stable mental health in the 18–29 age group compared to older age groups? Results from the Stockholm public health cohort 2002–2014,” Int J Environ Res Public Health, vol. 15, no. 12, Dec. 2018, doi: 10.3390/ijerph15122859.

S. Halder and K. Layla Khaled, “An Extensive Review on the Relationship between Food and Mood,” 2013. [Online]. Available: www.ijsr.net

M. D. Jakhete and P. C. Mankar, “Implementation of Smart Restaurant with e-menu Card,” Int J Comput Appl, vol. 119, no. 21, pp. 23–27, 2015.

S. Umap, S. Surode, P. Kshirsagar, M. Binekar, and N. Nagpal, “Smart Menu Ordering System in Restaurant,” Int J Sci Res Sci Technol, vol. 7, no. 4, pp. 207–212, 2018, [Online]. Available: www.ijsrst.com

V. Liyanage, A. Ekanayake, H. Premasiri, P. Munasinghe, and S. Thelijjagoda, “Foody - Smart Restaurant Management and Ordering System,” in 2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), 2018, pp. 1–6. doi: 10.1109/R10-HTC.2018.8629835.

F. P. J. Martin, N. Antille, S. Rezzi, and S. Kochhar, “Everyday eating experiences of chocolate and non-chocolate snacks impact postprandial anxiety, energy and emotional states,” Nutrients, vol. 4, no. 6, pp. 554–567, 2012, doi: 10.3390/nu4060554.

K. Mantantzis, F. Schlaghecken, S. I. Sünram-Lea, and E. A. Maylor, “Sugar Rush or Sugar Crash? A Meta-Analysis of Carbohydrate Effects on Mood Konstantinos Mantantzis a Sugar Rush or Sugar Crash? A Meta-Analysis of Carbohydrate Effects on Mood,” Neurosci Biobehav Rev, 2019.

C. Riachi, “How does Food Affect Mood at Work?,” Research & Reviews: Journal of Medical and Health Sciences, Jun. 2016.

H. Saeed, A. Shouman, M. Elfar, M. Shabka, S. Majumdar, and C. Horng-Lung, “Near-field communication sensors and cloud-based smart restaurant management system,” in 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), 2016, pp. 686–691. doi: 10.1109/WF-IoT.2016.7845440.

J. Saravanan, “face-api.js?: A way to build a Face Recognition system in the browser. TheLeanProgrammer | Medium,” Medium. Accessed: Dec. 30, 2023. [Online]. Available: https://medium.com/theleanprogrammer/face-api-js-a-way-to-build-face-recognition-system-in-browser-c1f4ac922657

D. Schiemann, “JavaScript Face Detection with face-api.js,” InfoQ. Accessed: Dec. 30, 2023. [Online]. Available: https://www.infoq.com/news/2020/03/face-api-js/

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Published

2024-01-28

How to Cite

Olowolayemo, A., Faisal, A., & Ismail, M. (2024). Smart Campus Food Ordering and Recommendation System with Emotion Booster: A First Design. International Journal on Perceptive and Cognitive Computing, 10(1), 90–97. https://doi.org/10.31436/ijpcc.v10i1.454

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