Abstract
Unhealthy diet is a leading cause of health issues. A powerful means for monitoring and improving nutrition is keeping a food diary. Unfortunately, frail people such as the elderly have a hard time filling food diaries on a continuous basis due to forgetfulness or physical issues. For this reason, in this paper we investigate the integration of nutrition monitoring in a robotic platform. A machine learning module detects cooking activities based on air quality sensor data. When cooking is detected, a social robot interacts with the user to fill the food diary through a conversational interface. We report our experience on the development of a partial prototype of our system. Moreover, we illustrate the results of preliminary experiments with annotated sensor data gathered over one month from a real-world apartment.
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Acknowledgements
This research was partially funded by the EU’s Marie Curie training network PhilHumans - Personal Health Interfaces Leveraging HUman-MAchine Natural interactionS (grant number 812882).
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Gerina, F., Pes, B., Reforgiato Recupero, D., Riboni, D. (2019). Toward Supporting Food Journaling Using Air Quality Data Mining and a Social Robot. In: Chatzigiannakis, I., De Ruyter, B., Mavrommati, I. (eds) Ambient Intelligence. AmI 2019. Lecture Notes in Computer Science(), vol 11912. Springer, Cham. https://doi.org/10.1007/978-3-030-34255-5_22
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DOI: https://doi.org/10.1007/978-3-030-34255-5_22
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