Interaction Behavior of Older Adults with Immersive Virtual Reality Application for Cognitive Training

Abstract

Nowadays, older adults face health problems. The main problem is decreasing our brain skills. Memory, visuospatial skill, and cognitive functions are considered to be essence of the brain skills. These capabilities can performance by applying cognitive training or brain training. Currently, virtual reality (VR) technology is applied to cognitive training application. The results of training can be analyzed after several weeks, but it is a lack of interaction behavior analysis of older adults with VR application. The behavior analysis helps to design efficient program training, in other words, we can utilize better VR application with older adults who do not familiar with current technology. Moreover, we can understand behavior of older adult via VR application for their brain ability. VR application can be designed and kept log files for interaction behavior analysis. The algorithm for preliminary analysis is machine learning. Machine learning can predict score that measures brain's ability from the dataset. We adopted 2 models in this research. The first model is to predict the scores of visual short-term memory, called VSTM-model. The second model is to predict the scores of visuospatial skill, called VS-model. We performed baseline regression and support vector regression algorithm for score prediction of behavior. Root mean squared error is selected to measure performance of the algorithm; root mean squared error of baseline regression equal to 0.3012 in VSTM-model and 1.2427 in VS-model, and root mean squared error of support vector regression equal to 0.2876 in VSTM-model and 1.0536 in VS-model.