ECG is an attractive option to assess stress in serious Virtual Reality (VR) applications due to its non-invasive nature. However, the existing Machine Learning (ML) models perform poorly. Moreover, existing studies only perform a binary stress assessment, while to develop a more engaging biofeedback-based application, multi-level assessment is necessary. Existing studies annotate and classify a single experience (e.g. watching a VR video) to a single stress level, which again prevents design of dynamic experiences where real-time in-game stress assessment can be utilized. In this paper, we report our findings on a new study on VR stress assessment, where three stress levels are assessed. ECG data was collected from 9 users experiencing a VR roller coaster. The VR experience was then manually labeled in 10-seconds segments to three stress levels by three raters. We then propose a novel multimodal deep fusion model utilizing spectrogram and 1D ECG that can provide a stress prediction from just a 1-second window. Experimental results demonstrate that the proposed model outperforms the classical HRV-based ML models (9% increase in accuracy) and baseline deep learning models (2.5% increase in accuracy). We also report results on the benchmark WESAD dataset to show the supremacy of the model.
ECG-Based Driver Stress Levels Detection System Using Hyperparameter Optimization
School of Electrical Engineering and Robotics, Queensland University of Technology（昆士兰科技大学电气工程与机器人学院）；Centre for Accident Research & Road Safety Queensland, Queensland University of Technology（昆士兰科技大学昆士兰事故研究与道路安全中心）；School of Information Technology, Deakin University（迪肯大学信息技术学院）
Prenatal Stress Detection from Electrocardiography Using Self-Supervised Deep Learning: Development and External Validation
Institute on Human Development and Disability, University of Washington, Seattle, WA, USA（人类发展与残疾研究所，华盛顿大学，西雅图，华盛顿州，美国）；JoyBeat Medical, Seattle, WA, USA（JoyBeat医疗公司，西雅图，华盛顿州，美国）；Department of Obstetrics and Gynecology, TUM University Hospital, Technical University of Munich, Munich, Germany（妇产科与妇科部，慕尼黑技术大学大学医院，慕尼黑，德国）；Technical University of Munich（慕尼黑技术大学；高级研究所，加兴，德国）；Institute for Advanced Study, Garching, Germany
Electromechanical Dynamics of the Heart: A Study of Cardiac Hysteresis During Physical Stress Test
School of Medicine, Emory University（埃默里大学医学院）；Department of Biomedical Engineering, Georgia Institute of Technology and Emory University（佐治亚理工学院与埃默里大学生物医学工程系）
Introducing WARM-VR: Benchmark Dataset for Multimodal Wearable Affect Recognition in Virtual Reality
School of Electrical Engineering and Computer Science, University of Ottawa（Ottawa 大学电气工程与计算机科学学院）；Computer Science, University of Ottawa（Ottawa 大学计算机科学系）；Computer Vision Department, Mohamed bin Zayed University of Artificial Intelligence（摩根·bin·扎耶德人工智能大学计算机视觉部门）
