Synchrony Vision: An RGB-D Sensor-Based System for Real-Time Monitoring and Event-Level Analysis of Interpersonal Motion Synchrony

Interpersonal synchrony is a time-dependent coordination pattern in which interacting partners’ body movements become temporally aligned. This study frames interpersonal synchrony as a human motion analysis problem and presents Synchrony Vision, an RGB-D sensor-based system for real-time monitoring and event-level analysis of interpersonal motion synchrony in free dialog. The system transforms Kinect-derived skeletal positions into joint acceleration signals, applies sensor-specific conditioning, detects movement peaks, and estimates event-level phase differences between two participants within a ±1.0 s window. The operator-facing interface supports live RGB-D monitoring, acceleration visualization, joint selection, millisecond-scale phase-difference histograms, four synchrony metrics (Frequency, Lead–lag, Width, and Strength), and exportable acceleration, timestamp, peak-pairing, and summary artifacts. We evaluated the deployed pipeline on 25 Kinect-tracked dyads engaged in unconstrained conversation. Across 200.6 min comprising 245,835 frames, the system detected 2681 synchrony events. The observed event rate exceeded circular-surrogate baselines, and dyad rankings remained stable across reasonable parameter settings. Motion Energy Analysis-style cross-correlation on the same acceleration signals also confirmed above-chance synchrony but produced different dyad rankings. These findings show that RGB-D skeletal sensing can extend human motion analysis from individual movement capture to transparent, event-level quantification of interpersonal coordination.