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arXiv · 2107.04566

Multi-level Stress Assessment from ECG in a Virtual Reality Environment using Multimodal Fusion

Zeeshan Ahmad·Suha Rabbani·Muhammad Rehman Zafar·Syem Ishaque·Sridhar Krishnan·Naimul Khan
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Abstract

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.

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Zeeshan Ahmad, Suha Rabbani, Muhammad Rehman Zafar, Syem Ishaque, Sridhar Krishnan, Naimul Khan. 2021-07-09. Multi-level Stress Assessment from ECG in a Virtual Reality Environment using Multimodal Fusion. https://arxiv.org/abs/2107.04566

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