Pupil Heart Rate Variability Monitoring Via Pupillary Fluctuations on MobileDevices
DOI:
https://doi.org/10.69996/fmep.2025027Keywords:
Heart Rate Variability (HRV), Pupillary Fluctuations, Mobile Health (mHealth), OneDimensional Convolutional Neural Network (1D-CNN), Recurrent Neural Network (RNN)Abstract
Heart Rate Variability (HRV) is a critical physiological marker used to assess autonomic nervous system activity and overall cardiovascular health. Traditional HRV monitoring relies on electrocardiograms (ECG) or wearable sensors, which can be intrusive, costly, and inconvenient for continuous use. This paper presents PUPILHEART, an innovative mobile-based system that leverages pupillary fluctuations—captured through smartphone cameras—as a non-contact alternative for HRV analysis. The proposed framework integrates a one-dimensional convolutional neural network (1D-CNN) with a recurrent neural network (RNN) to extract spatial and temporal features from real-time video data, enabling accurate HRV classification and prediction. The system is designed with intuitive user interfaces for external users and health analysts, ensuring ease of access and deployment. Extensive experimental analysis demonstrates high performance in terms of accuracy, sensitivity, and latency, validating the system’s effectiveness in real-world scenarios. PUPILHEART offers a scalable, privacy-conscious, and hardware-efficient solution for continuous cardiac monitoring, setting a new direction in mobile health diagnostics and preventive care.
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