Engagement measurement finds application in healthcare, education, advertisement, and services. The use of physiological and behavioral features is viable, but the impracticality of traditional physiological measurement arises due to the need for contact sensors. We demonstrate the feasibility of unsupervised remote photoplethysmography (rPPG) as an alternative for contact sensors in deriving heart rate variability (HRV) features, then fusing these with behavioral features to measure engagement in online group meetings. Firstly, a unique Engagement Dataset of online interactions among social workers is collected with granular engagement labels, offering insight into virtual meeting dynamics. Secondly, a pre-trained rPPG model is customized to reconstruct accurate rPPG signals from video meetings in an unsupervised manner, enabling the calculation of HRV features. Thirdly, the feasibility of estimating engagement from HRV features using short observation windows, with a notable enhancement when using longer observation windows of two to four minutes, is demonstrated. Fourthly, the effectiveness of behavioral cues is evaluated and fused with physiological data, which further enhances engagement estimation performance. An accuracy of 94% is achieved when only HRV features are used, eliminating the need for contact sensors or ground truth signals. The incorporation of behavioral cues raises the accuracy to 96%. Facial video analysis offers precise engagement measurement, beneficial for future applications.

使用无监督的远程光电容积脉搏图 (rPPG) 技术，结合行为特征，有效地测量在线团体会议中的参与度，通过使用仅心率变异性 (HRV) 特征可实现 94% 的准确性，结合行为线索后可提高至 96% 的准确性。

使用行为特征的无监督远程光电容积测量分析在线会议参与者的参与度