Estimating Participant Engagement in Online Meetings Using Unsupervised Remote Photoplethysmography and Behavioral Features

Core Concepts

The core message of this work is to demonstrate the feasibility of using unsupervised remote photoplethysmography (rPPG) signals and behavioral features to accurately estimate participant engagement levels in online group meetings.

Abstract

The study introduces a novel Engagement Dataset of online group meetings among social workers, which provides granular engagement labels and includes contact photoplethysmography (cPPG) data in addition to facial videos. This dataset enables in-depth analysis of engagement dynamics in virtual meetings.
The proposed method first reconstructs accurate rPPG signals from the facial videos in an unsupervised manner, allowing the calculation of heart rate variability (HRV) features. The study investigates the impact of the HRV observation window size on engagement estimation performance, finding that longer windows of 2-4 minutes significantly enhance the results compared to shorter 10-second windows.
Furthermore, the effectiveness of behavioral features, such as facial expressions and head/eye movements, is evaluated and fused with the physiological HRV features. This multimodal approach further boosts the engagement estimation accuracy, reaching up to 96% when both HRV and behavioral features are utilized.
The results demonstrate that the unsupervised rPPG technique can effectively replace contact sensors, eliminating the need for specialized equipment or ground truth signals. The incorporation of behavioral cues provides additional insights into participant engagement, making the proposed method a practical and comprehensive solution for engagement analysis in online meetings.

Stats

The heart rate (HR) from the reconstructed rPPG signals was compared to the ground truth cPPG, achieving a mean absolute error of 5.15 bpm and a root mean square error of 7.81 bpm.
The Engagement Dataset contains 24 recorded group online video meetings, each lasting approximately 1.5 hours, with 7-9 participants and one consultant per session.

Quotes

"The rapid transition to online communication underlines the importance of engagement analysis in virtual meetings."
"Engagement analysis in virtual meetings often relies on facial and body language recognition, although they can't gauge direct physiological responses like heart rate variability (HRV), which is difficult for individuals to fake."
"This article pioneers the application of unsupervised rPPG measurement technology in estimating engagement during online meetings."

Key Insights Distilled From

Analyzing Participants' Engagement during Online Meetings Using Unsupervised Remote Photoplethysmography with Behavioral Features

by Alexander Ve... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04394.pdf

Deeper Inquiries

How can the proposed method be extended to analyze engagement dynamics at the group level, such as synchrony or interaction patterns among participants?

To extend the proposed method for analyzing engagement dynamics at the group level, several enhancements can be implemented. Firstly, incorporating multi-modal data sources such as audio signals to capture vocal cues and speech patterns can provide valuable insights into group dynamics. Analyzing speech turn-taking, interruptions, and overall conversational flow can offer a deeper understanding of engagement levels within the group. Additionally, integrating motion tracking data to assess body language and gestures among participants can further enrich the analysis of group interactions. By combining facial expressions, vocal cues, and body language, a more comprehensive picture of engagement dynamics within the group can be obtained. Furthermore, leveraging machine learning algorithms for clustering and pattern recognition can help identify synchrony in engagement levels among participants, highlighting moments of alignment or divergence in group interactions.

How can the insights from this study on online meeting engagement be applied to improve the design and facilitation of virtual collaboration and training sessions?

The insights from this study on online meeting engagement can be instrumental in enhancing the design and facilitation of virtual collaboration and training sessions. Firstly, by utilizing the proposed method for real-time engagement monitoring, facilitators can receive immediate feedback on participants' engagement levels during virtual sessions. This feedback can guide facilitators in adjusting their delivery, pacing, and content to maintain optimal engagement levels. Moreover, the analysis of group dynamics and interaction patterns can help facilitators identify key moments of engagement or disengagement within the group, allowing for targeted interventions to re-engage participants as needed. Additionally, the integration of physiological signals beyond HRV, such as electrodermal activity or facial temperature, can provide additional insights into participants' emotional states and stress levels, enabling facilitators to create a supportive and engaging virtual environment. By leveraging these insights, facilitators can tailor their approach to foster collaboration, participation, and learning outcomes in virtual collaboration and training sessions.

How can the proposed method be extended to analyze engagement dynamics at the group level, such as synchrony or interaction patterns among participants?

To extend the proposed method for analyzing engagement dynamics at the group level, several enhancements can be implemented. Firstly, incorporating multi-modal data sources such as audio signals to capture vocal cues and speech patterns can provide valuable insights into group dynamics. Analyzing speech turn-taking, interruptions, and overall conversational flow can offer a deeper understanding of engagement levels within the group. Additionally, integrating motion tracking data to assess body language and gestures among participants can further enrich the analysis of group interactions. By combining facial expressions, vocal cues, and body language, a more comprehensive picture of engagement dynamics within the group can be obtained. Furthermore, leveraging machine learning algorithms for clustering and pattern recognition can help identify synchrony in engagement levels among participants, highlighting moments of alignment or divergence in group interactions.