Virtual Reality Centric Stress Detection Using Dynamic Baseline Calibration

With the increasing adoption of virtual reality (VR) in research and training applications, reliable stress detection in naturalistic settings remains challenging, particularly when hardware complexity must be minimized. This study presents an enhanced framework for real-time stress recognition in VR environments that integrates behavioral interactions with selectively derived physiological signals. Building upon previous architectures, the proposed framework incorporates pre-task baseline measurements to account for subject-specific and session-initial variability. While the comprehensive analysis employs a three-class affective framework, the practical implementation focuses on binary stress detection for real-world VR applications. Stress detection is achieved through VR-based behavioral signals, complemented by minimal input from a Galvanic Skin Response (GSR) sensor. The experimental evaluation demonstrates that baseline calibration improves separation across stress conditions. Quantitatively, the proposed Weighted Baseline Detector (WBD) achieved a classification accuracy of 94.17% and an Area Under the Curve (AUC) of 0.9993, outperforming the fixed global baseline approach (85.0% accuracy, AUC 0.9067), which demonstrates the effectiveness of the proposed calibration method. Rigorous cross-validation confirms that the approach achieves stable performance with statistical significance across stress conditions. These findings highlight the potential of combining behavioral analysis with physiological support to develop practical, low-hardware VR platforms for live stress recognition.