| Title: |
A Sensor-Enabled Smart Tray for Understanding Consumer Eating Behavior in a Restaurant |
| Authors: |
Baugier, Benjamin; Boreux, Charles; Jaeger, Simon De; Codutti, Nathan; Barbet, François-Xavier; Barbaut, Antoine; Joomun, Uzeir; Rajan, Sébastien Joseph Guna; Goffin, Dorothée; Haubruge, Eric |
| Source: |
Gastronomy, 3 (2), 9 (2025-06-04) |
| Publisher Information: |
MDPI AG |
| Publication Year: |
2025 |
| Collection: |
University of Liège: ORBi (Open Repository and Bibliography) |
| Subject Terms: |
eating action recognition; behavioral clustering; sensor-based eating assessment; real-life data collection; data analysis; unsupervised classification; Social & behavioral sciences; psychology; Neurosciences & behavior; Life sciences; Sciences sociales & comportementales; psychologie; Neurosciences & comportement; Sciences du vivant |
| Description: |
editorial reviewed ; This preliminary study explores the use of a smart tray prototype equipped with a weight sensor to analyze consumer eating behavior in real-life conditions. Conducted in 2022, at the restaurant “La Confluence” in Namur, with 60 participants, the experiment involved tracking the progression of a standardized meal, “Scallops in sauce with vegetables”. The collected data allowed for the calculation of variables such as bite count, applied force, meal duration, and quantity consumed. Through mathematical processing, insights into individual and group eating patterns were developed, with 39 usable datasets analyzed. Our first results show that the smart tray is capable of estimating the weight consumed and the number of bites with over 97% accuracy. Statistical analysis enabled the identification of four distinct groups of individuals based on five behavioral variables related to eating behavior. The smart tray could be used in hospitality establishments including cafeterias, restaurants, or brasseries, where it could serve as a valuable tool for monitoring meal nutrition. Further improvements will aim to enhance utensil and action recognition through artificial intelligence, which will also support a more detailed characterization of eating behavior. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| ISSN: |
2813-513X |
| Relation: |
https://www.mdpi.com/2813-513X/3/2/9/pdf; urn:issn:2813-513X; https://orbi.uliege.be/handle/2268/334914; info:hdl:2268/334914 |
| DOI: |
10.3390/gastronomy3020009 |
| Availability: |
https://orbi.uliege.be/handle/2268/334914; https://orbi.uliege.be/bitstream/2268/334914/1/gastronomy-03-00009-v2.pdf; https://doi.org/10.3390/gastronomy3020009 |
| Rights: |
open access ; http://purl.org/coar/access_right/c_abf2 ; info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.FF7C8779 |
| Database: |
BASE |