Detection of Psychological Risk for Protected Individuals by Using PPG Signals from Smartwatch 


Vol. 50,  No. 4, pp. 572-577, Apr.  2025
10.7840/kics.2025.50.4.572


PDF Full-Text
  Abstract

This paper proposes a machine learning approach to detect dangerous emition using short-term PPG (Photoplethysmogram) signals from a commercial smartwatch. In supervised learning, having accurately annotated training data is essential. However, a key challenge in emition detection problem is the uncertainty regarding how accurately data labeled as ”danger” reflects actual dangerous responses, since participants may react differently to the same experiments. The main contribution of this paper is the development of a feature selection method to remove ambiguously labeled training data, thereby improving the accuracy of the prediction model. In the test, PPG measurements were collected from participants playing a horror VR (Virtual Reality) game, and the proposed method validated the superiority of our proposed approach in comparison with other methods.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Related Articles
  Cite this article

[IEEE Style]

S. Yoo, G. Hwang, J. Yoo, "Detection of Psychological Risk for Protected Individuals by Using PPG Signals from Smartwatch," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 4, pp. 572-577, 2025. DOI: 10.7840/kics.2025.50.4.572.

[ACM Style]

Sohee Yoo, Gyuwon Hwang, and Jaehyun Yoo. 2025. Detection of Psychological Risk for Protected Individuals by Using PPG Signals from Smartwatch. The Journal of Korean Institute of Communications and Information Sciences, 50, 4, (2025), 572-577. DOI: 10.7840/kics.2025.50.4.572.

[KICS Style]

Sohee Yoo, Gyuwon Hwang, Jaehyun Yoo, "Detection of Psychological Risk for Protected Individuals by Using PPG Signals from Smartwatch," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 4, pp. 572-577, 4. 2025. (https://doi.org/10.7840/kics.2025.50.4.572)
Vol. 50, No. 4 Index

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880