Integrating Wearable Sensor Data and Self-reported Diaries for Personalized Affect Forecasting
<|reference_start|>Integrating Wearable Sensor Data and Self-reported Diaries for Personalized Affect Forecasting: 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.<|reference_end|>
@article{yang2024integrating,
    title={Integrating Wearable Sensor Data and Self-reported Diaries for
  Personalized Affect Forecasting},
    author={Zhongqi Yang, Yuning Wang, Ken S. Yamashita, Maryam Sabah, Elahe
  Khatibi, Iman Azimi, Nikil Dutt, Jessica L. Borelli, and Amir M. Rahmani},
    journal={Smart Health 32 (2024): 100464},
    year={2024},
    doi={10.1016/j.smhl.2024.100464},
    archivePrefix={arXiv},
    eprint={2403.13841},
    primaryClass={cs.LG cs.AI}
}