Predicting Engagement of Older People’s Virtual Teams from Video Call Analysis

This study examines seniors’ creative engagement in group activities using synchronous communication tools and explores automatic assessment methods through behavioral and psychophysiological measurements. Working with a small senior group on collaborative creative tasks, we implemented a comprehensive data collection approach using audio-visual and physiological measurements. Machine learning models were used to evaluate group creative engagement levels using various data subsets. Results show that engagement assessment can be effective with different feature combinations, allowing flexibility across contexts and constraints. The multimodal approach, combining facial, audio, and body analysis, achieved optimal performance and is recommended when conditions permit. Our research provides insights into seniors’ online creative participation and presents an automated system for detecting creative engagement in virtual teams, supporting active participation strategies.