Heart Rate Estimation from Face Videos for Student Assessment: Experiments on edBB

TL;DR

This paper explores estimating students' heart rate from face videos using remote photoplethysmography within an e-learning platform to monitor physiological states and behavioral cues.

Contribution

It introduces a novel face-based heart rate estimation method applied to e-learning, validated with real student data, enhancing remote student monitoring capabilities.

Findings

High correlation with smartwatch heart rate data

Effective in real-time student assessment

Potential for detecting stress or attention levels

Abstract

In this study we estimate the heart rate from face videos for student assessment. This information could be very valuable to track their status along time and also to estimate other data such as their attention level or the presence of stress that may be caused by cheating attempts. The recent edBBplat, a platform for student behavior modelling in remote education, is considered in this study1. This platform permits to capture several signals from a set of sensors that capture biometric and behavioral data: RGB and near infrared cameras, microphone, EEG band, mouse, smartwatch, and keyboard, among others. In the experimental framework of this study, we focus on the RGB and near-infrared video sequences for performing heart rate estimation applying remote photoplethysmography techniques. The experiments include behavioral and physiological data from 25 different students completing a collection of tasks related to e-learning. Our proposed face heart rate estimation approach is compared with the heart rate provided by the smartwatch, achieving very promising results for its future deployment in e-learning applications.