MURMR: A Multimodal Sensing Framework for Automated Group Behavior Analysis in Mixed Reality

TL;DR

This paper introduces MURMR, a passive multimodal sensing framework for automated, annotation-free analysis of group behavior in mixed reality environments, capturing structural and temporal collaboration dynamics.

Contribution

MURMR is the first framework to analyze collaboration in mixed reality using passive headset data with automated sociograms and unsupervised behavioral phase detection.

Findings

Intra-session analysis reveals significant variability lost in session-level aggregation.

The temporal module identifies five behavioral phases with 83% accuracy compared to video.

Behavioral transitions occur within stable structural states, indicating complementary dynamics.

Abstract

When teams coordinate in immersive environments, collaboration breakdowns can go undetected without automated analysis, directly affecting task performance. Yet existing methods rely on external observation and manual annotation, offering no annotation-free method for analyzing temporal collaboration dynamics from headset-native data. We introduce \sysname, a passive sensing pipeline that captures and analyzes multimodal interaction data from commodity MR headsets without external instrumentation. Two complementary modules address different levels of analysis: a structural module that generates automated multimodal sociograms and network metrics at both session and intra-session granularities, and a temporal module that applies unsupervised deep clustering to identify moment-to-moment dyadic behavioral phases without predefined taxonomies. An exploratory deployment with 48 participants in a co-located object-sorting task reveals that intra-session structural analysis captures significant within-session variability lost in session-level aggregation, with gaze, audio, and position contributing non-redundantly. The temporal module identifies five behavioral phases with 83\% correspondence to video observations. Cross-tabulation shows that behavioral transitions consistently occur within structurally stable states, demonstrating that the two modules capture complementary dynamics. These results establish that passive headset sensing provides meaningful signal for automated, multi-level collaboration analysis in immersive environments.