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

arXiv:2403.13841v1 Announce Type: new
Abstract: 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 …

abstract arxiv cs.ai cs.lg current data detection devices focus forecasting health making mobile mobile devices personalized pivotal prediction sensor sensory studies type wearable