From Multimodal Signals to Adaptive XR Experiences for De-escalation Training

2026-04-13

AI Summary

This work proposes a multimodal real-time perception and adaptive feedback framework for de-escalation training of law enforcement personnel in extended reality (XR). By synchronously fusing multi-view RGB video, facial electromyography, electroencephalography (EEG), galvanic skin response, and vocal signals, the system constructs an interaction semantics layer grounded in social semiotics and interaction theory to map low-level physiological and behavioral cues onto conflict escalation or de-escalation states. Built upon the Lab Streaming Layer for high-precision synchronization, the architecture integrates gesture recognition, occlusion-robust facial emotion analysis, vocal prosody assessment, psychological state decoding, and arousal estimation. Experimental results demonstrate that multi-view perception effectively mitigates head-mounted display occlusion and yields promising performance across key metrics, offering an innovative and empirically grounded framework for AI-enhanced XR-based interpersonal skills training.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Humans and AI: Interaction Techniques and Devices

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

We present the early-stage design and implementation of a multimodal, real-time communication analysis system intended as a foundational interaction layer for adaptive VR training. The system integrates five parallel processing streams: (1) verbal and prosodic speech analysis, (2) skeletal gesture recognition from multi-view RGB cameras, (3) multimodal affective analysis combining lower-face video with upper-face facial EMG, (4) EEG-based mental state decoding, and (5) physiological arousal estimation from skin conductance, heart activity, and proxemic behavior. All signals are synchronized via Lab Streaming Layer to enable temporally aligned, continuous assessments of users' conscious and unconscious communication cues. Building on concepts from social semiotics and symbolic interactionism, we introduce an interpretation layer that links low-level signal representations to interactional constructs such as escalation and de-escalation. This layer is informed by domain knowledge from police instructors and lay participants, grounding system responses in realistic conflict scenarios. We demonstrate the feasibility and limitations of automated cue extraction in an XR-based de-escalation training project for law enforcement, reporting preliminary results for gesture recognition, emotion recognition under HMD occlusion, verbal assessment, mental state decoding, and physiological arousal. Our findings highlight the value of multi-view sensing and multimodal fusion for overcoming occlusion and viewpoint challenges, while underscoring that fusion and feedback must be treated as design problems rather than purely technical ones. The work contributes design resources and empirical insights for shaping human-AI-powered XR training in complex interpersonal settings.

Problem

• de-escalation training
• multimodal signals
• adaptive XR
• communication analysis
• law enforcement

Innovation

• multimodal fusion
• adaptive XR
• real-time communication analysis
• de-escalation training
• synchronized biosensing