Tracking the Variability of Pupil Heart Rate on Mobile Devices by Means of Pulsatile Fluctuations

Abstract

Heart disease is on the rise and has devastating effects, yet it is preventable with early intervention. Therefore, it is crucial to examine heart health every day. Seismocardiography (SCG) and photo plethysmography (PPG) are the mainstays of current mobile cardiac monitoring devices. People can't keep tabs on their hearts whenever and wherever they want to since these techniques are cumbersome and need extra equipment. We propose a method to monitor the user's heart rate by analysing their pupillary reaction when they unlock their phone using face recognition. This method is based on our discovery of the association between pupil size and heart rate varaiability (HRV). With this goal in mind, we provide PupilHeart, a server-side and mobile terminal-based computer vision-based mobile HRV monitoring framework. When users unlock their phones using the front-facing camera on the mobile terminal, PupilHeart records data on the change in pupil size. After that, preliminary processing of the raw pupil size data is done on the server side. A one-dimensional convolutional neural network (1D-CNN) is used by PupilHeart to detect HRV-related time series characteristics. The PupilHeart app also models the pupil and HRV using a three- layered recurrent neural network (RNN). Every time a user unlocks their phone, PupilHeart uses this model to infer their HRV and determine their heart condition. In order to thoroughly assess the efficacy of PupilHeart, we recruit 60 individuals and do both laboratory and field investigations using the prototype. All things considered, PupilHeart does a good job at predicting the user's HRV.