Result description
We assess nutrition habits in terms of the intake of fruit, vegetables, salt, fat, fibre, protein, salt, fish and water. We use food frequency questionnaires (FFQs, i.e., »How often do you eat food x?«). They are personalised and optimised so that they help users monitor the goals they are interested in by answering just a few questions. This reduces the burden the FFQs put on the users, which can be substantial as typical FFQs contain dozens of questions. Based on the users’ answers, the system uses machine learning to predict the nutrition quality, so that the score is similar to the score obtained by asnwering full FFQs.
Addressing target audiences and expressing needs
- We are sharing our knowledge
- Grants and Subsidies
- Business Angels
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.
- Others/ No specific audience
- Public or private funding institutions
- 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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