TruVR: Trustworthy Cybersickness Detection using Explainable Machine Learning

CyPVICS: A framework to prevent or minimise cybersickness in immersive virtual clinical simulation

“… Deep Learning based Framework [ 38 ] The Deep Learning based framework aims to detect CS by means of deep learning algorithms, for this reason it was added as part of possible methods to detect the onset of CS. TruVR: a framework to develop a trustworthy CS detection technique [ 39 ] TruVR uses machine learning to determine the onset of CS and was added as an early CS detection method …”

“…TruVR uses explainable machine learning (xML) to detect and minimise CS. TruVR predicted CS with an accuracy of more than 94 % in two separate datasets [ 39 ]. The TruVR Framework was therefore added as a potential method to accurately detect the onset of CS in VR.…”

Deep Learning-based Simulator Sickness Estimation from 3D Motion

“…Recently, more and more machine learning-based methods have emerged due to the advancement of artificial intelligence. Some methods focused on visually-induced simulator sickness predictions [25-27, 34, 43] while others investigate physiological signals, including postural sway, gait motion, heart rate, breathing rate, galvanic skin response, and electroencephalogram (EEG) data [10,16,18,30,32,35,51], or the combination of visual content information and physiological signals [28,31,33]. For visual-based machine learning methods, gameplay video will always be analyzed first to extract the raw features of depth and optical flow, and then input them into the deployed machine learning method to regress simulator sickness level or classify simulator sickness arousal.…”