We use accelerometer data in a smartwatch to detect eating and count individual bites (food intakes). This allows users to precisely monitor their eating habits in terms of when they eat and how many bites they take (how large their meals are). We use deep neural networks to detect individual bites, which are then grouped into meals. The meals are classified into small, big and snacks. This information can be provided to the users, and can serve as the basis for advice on meal scheduling, fighting frequent snacking that can lead to obesity, and similar.
Addressing target audiences and expressing needs
- Grants and Subsidies
We are interested in further development of our result in research/development projects. We seek to increase accuracy, extend scope and similar.
Alternatively, we are interested in making this result more mature and better adapted to commercially relevant applications. This could be done to satisfy the needs of a specific customer (who would fund the adaptation), or for general commercialisation in partnership with a company.
- Public or private funding institutions
- Research and Technology Organisations
- Private Investors
R&D, Technology and Innovation aspects
Our software has been integrated in the WellCo virtual coach prototype. It works well in this context. Next steps can be in several directions: (1) make minor refinements to incorporate in a commercial version of the WellCo system. (2) Identify commercial applications in need of such software, and adapt it to them. (3) Identify datasets to which the developed method can be applied, and refine it in research context. Several of these steps can be done in parallel.
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