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Factors Influencing Heart Rate

Factors Influencing Heart Rate

The heart rate, or pulse rate, is a vital indicator of cardiovascular health. It reflects the number of times the heart beats per minute. Various physiological and environmental factors influence heart rate, increasing or decreasing cardiac output. Understanding these factors is crucial for assessing heart function and identifying potential health issues.
Let us explore the significant factors affecting heart rate, including age, body temperature, posture, acute pain, chemical influences,...

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Updated: May 13, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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Published on: December 15, 2023

A Deep Neural Network Based on Two-Stage Training for Estimating Heart Rate Variability From Camera Videos.

Lan Lan¹, Jin Yin^2,3, Haohan Zhang⁴

¹Information Management and Data Center, Beijing Tiantan Hospital Capital Medical University Beijing China.

Health Care Science

|March 2, 2026

Summary

This study introduces a novel deep learning method for noncontact heart rate variability (HRV) estimation using camera videos. The approach enhances accuracy, offering a comfortable alternative to traditional monitoring equipment.

Keywords:

camera cardiovascular disease deep learning heart rate variability pretraining

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Area of Science:

Biomedical Engineering
Artificial Intelligence
Cardiology

Background:

Heart rate variability (HRV) is a key prognostic indicator for cardiovascular diseases.
Current contact-based monitoring equipment, while accurate, causes adverse effects with prolonged use.
Noncontact HRV assessment is desirable for broader application and patient comfort.

Purpose of the Study:

To develop and validate a noncontact method for accurate heart rate variability (HRV) estimation.
To improve HRV detection accuracy using camera video analysis.
To enable HRV assessment in diverse scenarios without physical contact.

Main Methods:

A novel deep learning approach utilizing camera videos for heartbeat detection.
Facial segmentation, light balance scoring, and signal filtering using a Hamming window.
A transformer-based neural network for signal refinement and estimation of heart rate (HR) and HRV.

Main Results:

The method demonstrated higher accuracy in HR estimation compared to traditional and state-of-the-art filtering techniques.
Accuracy for HR estimation was superior under low light balance scores (0.867-0.983) versus high scores (0.667-0.750).
The proposed method achieved the lowest root mean square error for time-domain HRV and the highest correlation index for frequency-domain HRV.

Conclusions:

Optimizing light balance significantly improves HRV estimation accuracy.
Large-scale pretraining and a two-stage training strategy enhance the precision of HRV measurements.
The developed noncontact method offers a promising advancement for cardiovascular health monitoring.