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Ultra-short-term stress measurement using RGB camera-based remote photoplethysmography with reduced effects of Individual differences in heart rate.

Med Biol Eng Comput

February 2025

Departmen of Human-Centered Artificial Intelligence, Sangmyung University Hongjimun, 2-Gil 20, Jongno-Gu, Seoul, 03016, Republic of Korea.

Article Synopsis
  • Stress is connected to health issues, highlighting the need for effective, non-invasive monitoring techniques, as conventional methods can be uncomfortable and are limited in ultra-short-term stress assessments.
  • This study introduces a novel approach to measure ultra-short-term stress using remote photoplethysmography (rPPG) that segments data based on normal-to-normal intervals (NNIs), which enhances stress predictions by addressing variations in heart rate.
  • The findings show that using NNI counts significantly improves the accuracy of predicting stress indices compared to traditional time-segmented methods, with the Extra Trees Regressor yielding notably higher scores for various NNI intervals.
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