Work

Towards Intelligent VR Training: A Physiological Adaptation Framework for Cognitive Load and Stress Detection

Year: 2025

Type: conference-paper

Abstract: Adaptive Virtual Reality (VR) systems have the potential to enhance training and learning experiences by dynamically responding to users' cognitive states.This research investigates how eye tracking and heart rate variability (HRV) can be used to detect cognitive load and stress in VR environments, enabling real-time adaptation.The study follows a three-phase approach: (1) conducting a user study with the Stroop task to label cognitive load data and train machine learning models to detect high cognitive load, (2) fine-tuning these models with new users and integrating them into an adaptive VR system that dynamically adjusts training difficulty based on physiological signals, and (3) developing a privacy-aware approach to detect high cognitive load and compare this with the adaptive VR in Phase two.This research contributes to affective computing and adaptive VR using physiological sensing, with applications in education, training, and healthcare.Future work will explore scalability, real-time inference optimization, and ethical considerations in physiological adaptive VR.

Source: ACM Conference on User Modeling, Adaptation and Personalization (UMAP)

DOI: 10.1145/3699682.3727575

Author: Mahsa Nasri

Institution: Northeastern University

Language: English

Topic: Virtual Reality Applications and Impacts

Subfield: Human-Computer Interaction

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: National Science Foundation

Award: Accelerating Skill Acquisition in Complex Psychomotor Tasks via an Intelligent Extended Reality Tutoring System