Tracking vigilance fluctuations in real-time: a sliding-window heart rate variability-based machine-learning approach
Tian Xie,
Ning Ma
Abstract:Study Objectives
Heart rate variability (HRV)-based machine learning models hold promise for real-world vigilance evaluation, yet their real-time applicability is limited by lengthy feature extraction times and reliance on subjective benchmarks. This study aimed to improve the objectivity and efficiency of HRV-based vigilance evaluation by associating HRV and behavior metrics through a sliding-window approach.
Methods
Forty-f… Show more
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