Self-mental Care Management System by Emotion Estimation Method for Heart Rate Variability
Abstract
This study introduces a self-mental care management system utilizing an emotion estimation technique applied to heart rate variability parameters derived from vital data. The escalating prevalence of pandemics has exacerbated the dearth of accessible mental healthcare services, underscoring the heightened significance of investing in mental health programs. However, advancements in sensor technology, marked by enhanced precision and reduced size, facilitate the rapid acquisition of vital data from users. In our approach, we capture electrocardiogram (ECG) data concurrently with the narration of emotionally evocative stories. Through the analysis of the resulting data trends, emotions strongly correlated with the newly acquired ECG data were identified and inferred to be the emotions manifested within the ECG data. Our methodology enables the estimation of user emotions based on ECG data, and is further implemented in an application featuring real-time visualization of users' emotional states through chat icons. The deployment of this application empowers users to monitor emotional fluctuations and effectively manage their mental wellbeing.
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