基于人脸视频的心率估计及其在学生评价中的应用--基于edBB的实验
Heart Rate Estimation from Face Videos for Student Assessment: Experiments on edBB
摘要：
在这项研究中，我们估计心率从面部视频的学生评估。这些信息对于跟踪他们的状态以及估计其他数据（例如他们的注意力水平或可能由作弊尝试引起的压力）非常有价值。最近的edBBplat，在远程教育中的学生行为建模的平台，被认为是在这项研究1。该平台允许从一组捕获生物特征和行为数据的传感器捕获多个信号：RGB和近红外摄像头、麦克风、EEG带、鼠标、智能手表和键盘等。在这项研究的实验框架中，我们专注于RGB和近红外视频序列进行心率估计应用远程光电容积描记技术。实验包括行为和生理数据，从25个不同的学生完成一系列与电子学习相关的任务。我们提出的人脸心率估计方法与智能手表提供的心率进行了比较，其未来在电子学习应用中的部署取得了非常有前途的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
