Multi-level Stress Assessment from ECG in a Virtual Reality Environment using Multimodal Fusion
This work addresses the need for real-time, multi-level stress assessment in VR applications to enable more engaging biofeedback, though it is incremental as it builds on existing methods with specific improvements.
The paper tackled the problem of poor performance in stress assessment from ECG in VR by proposing a multimodal deep fusion model that uses spectrogram and 1D ECG data, achieving a 9% increase in accuracy over classical HRV-based ML models and a 2.5% increase over baseline deep learning models for multi-level stress prediction from 1-second windows.
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.