Toward Supporting Food Journaling Using Air Quality Data Mining and a Social Robot
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.
KeywordsHealthcare Context-aware computing Social robots
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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