Integrating Wearable Sensor Data and Self-reported Diaries for Personalized Affect Forecasting

• DOI: 10.48550/arxiv.2403.13841
• Publication Date: 2024-06-01
• Authors: Yang, Zhongqi; Wang, Yuning; Yamashita, Ken S.; Sabah, Maryam; Khatibi, Elahe; Azimi, Iman; Dutt, Nikil; Borelli, Jessica L.; Rahmani, Amir M.
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; Machine Learning (cs.LG)

Accepted by Connected Health: Applications, Systems and Engineering Technologies (CHASE) 2024<br/>Emotional states, as indicators of affect, are pivotal to overall health, making their accurate prediction before onset crucial. Current studies are primarily centered on immediate short-term affect detection using data from wearable and mobile devices. These studies typically focus on objective sensory measures, often neglecting other forms of self-reported information like diaries and notes. In this paper, we propose a multimodal deep learning model for affect status forecasting. This model combines a transformer encoder with a pre-trained language model, facilitating the integrated analysis of objective metrics and self-reported diaries. To validate our model, we conduct a longitudinal study, enrolling college students and monitoring them over a year, to collect an extensive dataset including physiological, environmental, sleep, metabolic, and physical activity parameters, alongside open-ended textual diaries provided by the participants. Our results demonstrate that the proposed model achieves predictive accuracy of 82.50% for positive affect and 82.76% for negative affect, a full week in advance. The effectiveness of our model is further elevated by its explainability.<br/>