Thomas Koch

Birgit Nierula, Karam Tomotaki-Dawoud, Daniel Johannes Meyer et al.

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.

Ilia Vovsha, Ansaf Salleb-Aouissi, Anita Raja et al.

We describe an application of machine learning to the problem of predicting preterm birth. We conduct a secondary analysis on a clinical trial dataset collected by the National In- stitute of Child Health and Human Development (NICHD) while focusing our attention on predicting different classes of preterm birth. We compare three approaches for deriving predictive models: a support vector machine (SVM) approach with linear and non-linear kernels, logistic regression with different model selection along with a model based on decision rules prescribed by physician experts for prediction of preterm birth. Our approach highlights the pre-processing methods applied to handle the inherent dynamics, noise and gaps in the data and describe techniques used to handle skewed class distributions. Empirical experiments demonstrate significant improvement in predicting preterm birth compared to past work.