This paper presents the current state of an ongoing project which aims to study, develop and evaluate an automatic framework able to track and monitor the dietary habits of people involved in a smoke quitting protocol. The system will periodically acquire images of the food consumed by the users, which will be analysed by modern food recognition algorithms able to extract and infer semantic information from food images. The extracted information, together with other contextual data, will be exploited to perform advanced inferences and to make correlations between eating habits and smoke quitting process steps, providing specific information to the clinicians about the response to the quitting protocol that are directly related to observable changes in eating habits.
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Journal: International Conference on Image Processing and Vision Engeneering (IMPROVE 2021)
Authors (in alph. order): Alessandro Ortis, Mazhar Hussain, Oliver Giudica, Pasquale Caponnetto, Riccardo Polosa, Roberto Leotta, Sebastiano Battiato
