Integrating wearable sensor data and self-reported diaries for personalized affect forecasting
: Yang, Zhongqi; Wang, Yuning; Yamashita, Ken S.; Khatibi, Elahe; Azimi, Iman; Dutt, Nikil; Borelli, Jessica L.; Rahmani, Amir M.
Publisher: Elsevier
: 2024
Smart Health
: Smart Health
: 100464
: 32
: 2352-6483
: 2352-6491
DOI: https://doi.org/10.1016/j.smhl.2024.100464
: https://doi.org/10.1016/j.smhl.2024.100464
: https://research.utu.fi/converis/portal/detail/Publication/387507382
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.
UTU Research Portal