Inferring Mood-While-Eating with Smartphone Sensing and Community-Based Model Personalization

• URL: http://arxiv.org/abs/2306.00723v2
• Date: Sat, 9 Dec 2023 21:58:40 GMT
• Title: Inferring Mood-While-Eating with Smartphone Sensing and Community-Based Model Personalization
• Authors: Wageesha Bangamuarachchi and Anju Chamantha and Lakmal Meegahapola and Haeeun Kim and Salvador Ruiz-Correa and Indika Perera and Daniel Gatica-Perez
• Abstract summary: Phone sensor data have been used to characterize both eating behavior and mood, independently, in the context of mobile food diaries and mobile health applications. Our results indicate that generic mood inference models decline in performance in certain contexts, such as when eating. To address these limitations, we employed a novel community-based approach for personalization by building models with data from a set of similar users to a target user.
• Score: 4.245223529153532
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The interplay between mood and eating has been the subject of extensive research within the fields of nutrition and behavioral science, indicating a strong connection between the two. Further, phone sensor data have been used to characterize both eating behavior and mood, independently, in the context of mobile food diaries and mobile health applications. However, limitations within the current body of literature include: i) the lack of investigation around the generalization of mood inference models trained with passive sensor data from a range of everyday life situations, to specific contexts such as eating, ii) no prior studies that use sensor data to study the intersection of mood and eating, and iii) the inadequate examination of model personalization techniques within limited label settings, as we commonly experience in mood inference. In this study, we sought to examine everyday eating behavior and mood using two datasets of college students in Mexico (N_mex = 84, 1843 mood-while-eating reports) and eight countries (N_mul = 678, 329K mood reports incl. 24K mood-while-eating reports), containing both passive smartphone sensing and self-report data. Our results indicate that generic mood inference models decline in performance in certain contexts, such as when eating. Additionally, we found that population-level (non-personalized) and hybrid (partially personalized) modeling techniques were inadequate for the commonly used three-class mood inference task (positive, neutral, negative). Furthermore, we found that user-level modeling was challenging for the majority of participants due to a lack of sufficient labels and data from the negative class. To address these limitations, we employed a novel community-based approach for personalization by building models with data from a set of similar users to a target user.

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