Development of classifiers to determine factors associated with older adult’s cognitive functions and game user experience in VR using head kinematics
Virtual reality (VR) is increasingly being used to promote exercise among older adults. The data captured through VR may be useful indicator of the game user’s experience as well as providing insight into functional ability of older adults. This paper presents classifiers to predict game user experience variables using VR data from community-dwelling older adults. Head-kinematic data of the VR headset was collected from 13 participants over a six-week period with three 20-minutes exergame sessions per week (e.g., 360 minutes per participant). Cognitive function was assessed using the Montreal Cognitive Assessment (MoCa) and multisensory response-time (RT). Game user experience was captured through perceived-levels of cybersickness, enjoyment, and exertion after each session. Data was used as references for discrete binary and ternary classification patterns. Combinations of kinematic features were used to train different classifiers: K-nearest-neighbors (KNN), linear discriminant analysis (LDA), support vector machines (SVM), and decision trees. Maximum classification accuracy of 70% was found for MoCa, 68% for perceived exertion, 60% for cybersickness, 59% for multisensory RT, and 53% for perceived enjoyment. Results suggest unobtrusive recording of head kinematics from VR headsets combined with machine learning classifiers could be used to predict cognition, exertion, and game user experience among older adults.
Email Address of Submitting Authorfgali@uwaterloo.ca
Submitting Author's InstitutionUniversity of Waterloo
Submitting Author's Country