Analyzing Participants' Engagement during Online Meetings Using
  Unsupervised Remote Photoplethysmography with Behavioral Features
Alexander Vedernikov Zhaodong Sun Virpi-Liisa Kykyri Mikko Pohjola Miriam Nokia Xiaobai Li
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.