Classification of discrete stress levels in users using eye tracker and K- Nearest Neighbour algorithm

Abstract: The advancement of the Head Mounted Display (HMD) used for Virtual Reality (VR) has come a long way and now the option of eye tracking is available in some HMD. The eyes show physiological responses when healthy individuals are stressed, justifying eye tracking as a tool to estimate at minimum, the very presence of stress. Stress can present itself in many shapes and may be caused by different factors such as work, social situations, cognitive load and many others. The stress test Group Stroop Color Word Test (GSCWT) can induce four different levels of stress in users; no stress, low stress, medium stress and high stress. In this thesis GSCWT was implemented in a virtual reality and users had their pupil dilation and blinking rate recorded. The data was then used to train and test a K-Nearest Neighbour algorithm (KNN). The KNN- algorithm could not accurately predict between the four different stress classes but it could predict the presence or absence of stress. VR has been used successfully as a tool for practicing different social skills and other everyday life skills for individuals with Autism Spectrum Disorder (ASD). By correctly identifying the stress level in the user in VR, tools for practicing social skills for ASD individuals could be more personalized and improved.