A Proposed Serious Game Architecture to Self-Management HealthCare for Older Adults

As people age, older adults' health begins to slow down. Moreover, the elderly population number will grow in upcoming years, according to statistics. This fact can lead to clinics and the hospitals becoming overloaded, and the demand for supervision becomes a challenge for the healthcare area. Because the majority of health issues are in the kinesiology domain, using new technologies like Kinect Sensor, this paper proposes a home system that implies the serious games for older adults, machine learning models for exercises recognition and remote activity supervision. The aim is to minimize the physical effort by offering a believable and motivating virtual world where the patient simulates kinesiology exercises, responds to quizzes and sends feedback. In the same time, the system recovers the exercise data and interprets it in order to model personalized care solutions, to create user profiles, to calibrate the difficulty level of the game using a language of powerful questions, to analyze the exercises progress and the performance feedback, to detect symptoms or falls and to learn the users' behavior. The approach described in this paper is based on analyses of the existing similar systems and on the statistics regarding the acceptability of the lifestyle in selfmanagement physical level for elders.