Classifying Food Items During an Eating Occasion: A Machine Learning Approach with Slope Dynamics for Windowed Kinetic Data
Monitoring food items using simple wristband-mounted wearable devices is feasible and accurate for some foods while unsatisfactory for others. Machine learning tools are necessary to deal with the complexity of signals gathered by the devices, and research is ongoing to improve accuracy further and work on large-scale and real-time implementation and testing.
Authors
- Luca Vedovelli (ORCID: https://orcid.org/0000-0003-4847-2333)
- Ileana Baldi (ORCID: https://orcid.org/0000-0002-8578-9164)
- Corrado Lanera (ORCID: https://orcid.org/0000-0002-0520-7428)
- Darío Gregori (ORCID: https://orcid.org/0000-0001-7906-0580)
- Paola Berchialla (ORCID: https://orcid.org/0000-0001-5835-5638)
- M. Bhuyan (ORCID: https://orcid.org/0000-0001-9387-1187)
Institutions
- University of Padua (IT)
- University of Turin (IT)
Publication Details
- Journal
- Foods
- Published
- 2025-01-16
- DOI
- https://doi.org/10.3390/foods14020276
- Citations
- 1
- Primary Topic
- Advanced Chemical Sensor Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.67
Funders
- Università degli Studi di Padova