[2302.01985] VR-LENS: Super Learning-based Cybersickness Detection and Explainable AI-Guided Deployment in Virtual Reality

A plethora of recent research has proposed several automated methods based on
machine learning (ML) and deep learning (DL) to detect cybersickness in Virtual
reality (VR). However, these detection methods are perceived as computationally
intensive and black-box methods. Thus, those techniques are neither trustworthy
nor practical for deploying on standalone VR head-mounted displays (HMDs). This
work presents an explainable artificial intelligence (XAI)-based framework
VR-LENS for developing cybersickness detection ML models, explaining them,
reducing their size, and deploying them in a Qualcomm Snapdragon 750G
processor-based Samsung A52 device. Specifically, we first develop a novel
super learning-based ensemble ML model for cybersickness detection. Next, we
employ a post-hoc explanation method, such as SHapley Additive exPlanations
(SHAP), Morris Sensitivity Analysis (MSA), Local Interpretable Model-Agnostic
Explanations (LIME), and Partial Dependence Plot (PDP) to explain the expected
results and identify the most dominant features. The super learner
cybersickness model is then retrained using the identified dominant features.
Our proposed method identified eye tracking, player position, and galvanic
skin/heart rate response as the most dominant features for the integrated
sensor, gameplay, and bio-physiological datasets. We also show that the
proposed XAI-guided feature reduction significantly reduces the model training
and inference time by 1.91X and 2.15X while maintaining baseline accuracy. For
instance, using the integrated sensor dataset, our reduced super learner model
outperforms the state-of-the-art works by classifying cybersickness into 4
classes (none, low, medium, and high) with an accuracy of 96% and regressing
(FMS 1-10) with a Root Mean Square Error (RMSE) of 0.03.