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

: 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.

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