A Method for Estimating Emotions Using HRV for Vital Data and Its Application to Self-mental care management system

12 pages•Published: September 20, 2022

Koharu Sano, Keiko Ojima, Tagiru Nakamura, Ryotaro Okada and Takafumi Nakanishi

Abstract

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.

Keyphrases: affective computing, ecg, hrv, self mental care, vital data

In: Tokuro Matsuo (editor). Proceedings of 11th International Congress on Advanced Applied Informatics, vol 81, pages 89-100.

Links:	https://easychair.org/publications/paper/7Zp7
	https://doi.org/10.29007/fp2d

BibTeX entry

@inproceedings{IIAIAAI2021-Winter:Method_Estimating_Emotions_Using,
  author    = {Koharu Sano and Keiko Ojima and Tagiru Nakamura and Ryotaro Okada and Takafumi Nakanishi},
  title     = {A Method for Estimating Emotions Using HRV for Vital Data and Its Application to Self-mental care management system},
  booktitle = {Proceedings of 11th International Congress on Advanced Applied Informatics},
  editor    = {Tokuro Matsuo},
  series    = {EPiC Series in Computing},
  volume    = {81},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/7Zp7},
  doi       = {10.29007/fp2d},
  pages     = {89-100},
  year      = {2022}}

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