An emotion estimation method using heart rate variability parameters successfully displayed relevant facial expression icons for positive stories and normal states, but not for negative stories.
A novel method using HRV parameters from ECG data can estimate user emotions in real-time, potentially aiding in self-mental care management.
In this paper, we present an emotion estimation method using heart rate variability parameters of vital data. Recently, as sensors have become more precise and smaller, it has been possible to obtain users' vital data in real-time quickly. In our method, ECG (electrocardiogram) data are measured beforehand while listening to a story with voice narration that evokes emotions and based on the trends obtained through the measurement, the emotions that have a high correlation with the newly acquired ECG data are estimated to be the emotions expressed in the ECG data. With the implementation of our method, it is possible to estimate the user's emotions based on ECG data. In this paper, we also represent the application of our method to chat icons that see users' emotions in real-time. By realizing this application, users will see the changes in their emotions and control their mental health.
Sano et al. (Tue,) conducted a other in Healthy (n=5). Emotion estimation method using HRV parameters (SDNN, RMSSD, CVSD) was evaluated on Display of relevant facial expression icons corresponding to elicited emotions. An emotion estimation method using heart rate variability parameters successfully displayed relevant facial expression icons for positive stories and normal states, but not for negative stories.