arXiv subjects

Diana Romero

Publications and source records attributed to Diana Romero.

8 recordsLinked to original sources

arXiv2026

Follow My Eyes: Backdoor Attacks on Goal-Directed Scanpath Prediction

Scanpath prediction models forecast the sequence of fixations a person makes while searching a scene, and increasingly serve as the upstream perception layer for foveated rendering, intent inference, and gaze-driven assistive interfaces. Since eye-tracking data is expensive to collect, these models are routinely fine-tuned from public datasets or third-party pretrained weights, exposing them to training-time poisoning. We present the first backdoor attacks on multimodal scanpath prediction. This task differs from classification: its output is a continuous, variable-length sequence of fixations, opening new avenues of attack. A fixed-trajectory backdoor is easy to implant, but it clusters poisoned samples away from clean data, making it detectable. We instead design two backdoor attacks that condition the malicious supervision on each scene to keep triggered outputs diverse and plausible: a spatial misdirection attack that redirects the predicted search toward an attacker-chosen object instead of the queried one, and a duration inflation attack that lengthens the predicted search by inserting extra fixations while preserving correct localization. Our attacks succeed across visual, textual, and multimodal triggers, with duration inflation reaching up to 93.5% attack success from as few as 540 poisoned samples (2.5% of training data), and spatial misdirection redirecting the search in up to 61% of triggered inputs. We evaluate our attacks against five existing backdoor defenses, spanning fine-tuning, fine-pruning, neural attention distillation, contrastive learning, and trigger inversion, and show none removes the backdoor without degrading model output below the usable threshold. Our attacks generalize across models and datasets, showing scanpath prediction models are vulnerable to backdoor attacks through data poisoning, and that designing an effective defense remains an open problem.

cs.CR↗

arXiv2026

TeamLLM: Exploring the Capabilities of LLMs for Multimodal Group Interaction Prediction

Predicting group behavior, how individuals coordinate, communicate, and interact during collaborative tasks, is essential for designing systems that can support team performance through real-time prediction and realistic simulation of collaborative scenarios. Large Language Models (LLMs) have shown promise for processing sensor data for human-activity recognition (HAR), yet their capabilities for team dynamics or group-level multimodal sensing remain unexplored. This paper investigates whether LLMs can predict group coordination patterns from multimodal sensor data in collaborative Mixed Reality (MR) environments. We encode hierarchical context -- individual behavioral profiles, group structural properties, and temporal activity context -- as natural language and evaluate three LLM adaptation paradigms (zero-shot, few-shot, and supervised fine-tuning) against statistical baselines. Our evaluation on 16 groups (64 participants, $\sim$25 hours of sensor data) reveals that LLMs achieve 3.2$\times$ improvement over LSTM baselines for linguistically-grounded behaviors, with fine-tuning reaching 96\% accuracy for conversation prediction while maintaining sub-35ms latency. Beyond performance gains, we characterize the boundaries of text-based LLMs for multimodal sensing conversation prediction succeeds because turn-taking maps to linguistic patterns, while shared or joint attention may require spatial and visual reasoning that text only LLMs cannot capture. We further identify simulation mode brittleness (83\% degradation from cascading context errors) and minimal few-shot sensitivity to example selection strategy. These findings establish guidelines when LLMs are appropriate for CPS/IoT sensing for team dynamics and inform the design of future multimodal foundation models.

cs.HC↗

arXiv2026

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

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.

cs.HC↗

arXiv2026

What Sensors See, What People Feel: An Exploratory Study of Subjective Collaboration Perception in Mixed Reality

Mixed Reality (MR) enables rich, embodied collaboration; however, it is uncertain whether sensor- and system-logged behavioral signals capture how users experience that collaboration. This disconnect stems from a fundamental gap. Behavioral signals are observable and continuous, while collaboration is interpreted subjectively and shaped by internal states like presence, cognitive availability, and social awareness. Our core insight is that sensor signals serve as observable manifestations of subjective experiences in MR collaboration, and they can be captured through sensor data such as shared gaze, speech, spatial movement, and other system-logged performance metrics. We propose the Sensor-to-Subjective (S2S) Mapping Framework, a conceptual model that links observable interaction patterns to users' subjective perceptions of collaboration and internal cognitive states through sensor-based indicators and task performance metrics. To evaluate this model, we conducted an exploratory study with 48 participants across 12 MR groups engaged in a collaborative image-sorting task. Our findings show a correlation between sensed behavior and perceived collaboration, particularly through shared attention and proximity.

cs.HC↗

arXiv2025

GaitGuard: Protecting Video-Based Gait Privacy in Mixed Reality

Mixed Reality (MR) systems capture continuous video streams that expose bystanders' and collaborators' gait patterns -- a biometric revealing sensitive attributes including age, gender, and health conditions. We show that video-based gait profiling achieves 78\% accuracy (15.6$\times$ random chance) on unprotected MR feeds, motivating \textbf{GaitGuard}, a real-time defense operating on a companion mobile device. GaitGuard introduces \textbf{GaitExtract}, an automated gait feature extraction pipeline adapted from clinical analysis for egocentric MR perspectives. Through systematic evaluation of 233 mitigation configurations, we characterize privacy-utility-performance trade-offs. A key insight is that gait features derive primarily from transient events (heel strikes, toe-offs). We exploit this temporal sparsity through adaptive mitigation that selectively processes only gait-critical frames, achieving a 68\% reduction in profiling accuracy while preserving visual quality (SSIM: 0.97) at 29~FPS. \textbf{GaitGuard} scales to 10 simultaneous users with under 10ms latency. A qualitative study of 20-participants confirms that the users preferred a solution such as \textbf{GaitGuard} which provides privacy guarantees.

cs.HC↗

arXiv2025

M-CALLM: Multi-level Context Aware LLM Framework for Group Interaction Prediction

This paper explores how large language models can leverage multi-level contextual information to predict group coordination patterns in collaborative mixed reality environments. We demonstrate that encoding individual behavioral profiles, group structural properties, and temporal dynamics as natural language enables LLMs to break through the performance ceiling of statistical models. We build M-CALLM, a framework that transforms multimodal sensor streams into hierarchical context for LLM-based prediction, and evaluate three paradigms (zero-shot prompting, few-shot learning, and supervised fine-tuning) against statistical baselines across intervention mode (real-time prediction) and simulation mode (autoregressive forecasting) Head-to-head comparison on 16 groups (64 participants, ~25 hours) demonstrates that context-aware LLMs achieve 96% accuracy for conversation prediction, a 3.2x improvement over LSTM baselines, while maintaining sub-35ms latency. However, simulation mode reveals brittleness with 83% degradation due to cascading errors. Deep-dive into modality-specific performance shows conversation depends on temporal patterns, proximity benefits from group structure (+6%), while shared attention fails completely (0% recall), exposing architectural limitations. We hope this work spawns new ideas for building intelligent collaborative sensing systems that balance semantic reasoning capabilities with fundamental constraints.

cs.HC↗

arXiv2025

MoCoMR: A Collaborative MR Simulator with Individual Behavior Modeling

Studying collaborative behavior in Mixed Reality (MR) often requires extensive, challenging data collection. This paper introduces MoCoMR, a novel simulator designed to address this by generating synthetic yet realistic collaborative MR data. MoCoMR captures individual behavioral modalities such as speaking, gaze, and locomotion during a collaborative image-sorting task with 48 participants to identify distinct behavioral patterns. MoCoMR simulates individual actions and interactions within a virtual space, enabling researchers to investigate the impact of individual behaviors on group dynamics and task performance. This simulator facilitates the development of more effective and human-centered MR applications by providing insights into user behavior and interaction patterns. The simulator's API allows for flexible configuration and data analysis, enabling researchers to explore various scenarios and generate valuable insights for optimizing collaborative MR experiences.

cs.HC↗

arXiv2025

GroupBeaMR: Analyzing Collaborative Group Behavior in Mixed Reality Through Passive Sensing and Sociometry

Understanding group behavior is crucial for enhancing collaboration and productivity in mixed reality (MR). This paper introduces a framework for group behavior analysis in MR, or GroupBeaMR for short for analyzing group behavior in MR. GroupBeaMR leverages MR headsets' sensors to analyze group behavior through conversation, shared attention, and proximity, identifying cohesive, fragmented, and competitive interaction patterns. Using social network analysis, GroupBeaMR provides quantitative assessments of group dynamics, offering insights into collaboration structures. A user study with 48 participants across 12 groups validates the framework's ability to distinguish interaction patterns in MR environments. Our analyses show that group behavior is independent of task performance, emphasizing the significance of social interaction patterns. Our group-type assignments indicate that sensor-based assessments in MR can provide meaningful insights into collaborative experiences, supporting the design of systems that adapt and optimize group behaviors.

cs.HC↗