Data modeling mobile augmented reality: integrated mind and body rehabilitation

Abstract

The rapid growth of elderly populations throughout the world necessitates inclusion of this sector in all active functions of communities. However, lack of physical and mental fitness threatens their effectiveness is making them to drain the community resources instead of positive and productive contributions. Our studies show the need for a massive large-scale boost in two main dimensions of physical and mental health enhancement. In order to solve this problem, in this paper we propose a new low-cost and innovative adoption of augmented reality (AR) functions through an agile deployment of mobile-based augmented reality (mAR) embedded in massively available intelligent smartphones. In our proposed method a set of downloadable AR-enabled embedded learning and exercising programs, designed upon users' historical and habitual improvement data would enable a collective sequence of required activities and individually optimized. At the system design level upon the individually recorded data in various databases select and configure the most suitable set of downloadable programs--a combination of mental and physical activities. From our experiment we provide some of our statistical results for two distinct application areas of mAR: `exercising-rehab' and `lifelong learning'. Three sets of results show the age related results for three user critical features of `ease of use', `usefulness' and `user attitude'. Further analysis of data through modeling helps us to provide a systematic design procedure based on user's age in conjunction with other variables.