An empirical study on evaluating basic characteristics and adaptability to users of a preventive care system with learning communication robots

Abstract

In many countries, and particularly in Japan, rising medical costs and shortfalls in the number of healthcare personnel are becoming serious national problems. This paper proposes a Preventive Care system with learning Communication robots (PrevCareCom), aiming to provide a preventive care approach wherein the elderly can engage in exercise over the long term without getting bored while communicating with robots through match-up games. In the PrevCareCom, the preventive care exercises are based on a simple, traditional Japanese match-up game, and several kinds of robots are used as opponents. The proposed system encourages a sense of familiarity with the robots (agents) for elderly users based on the concept of human–agent interaction. Reinforcement learning methods are also used to adapt system parameters such as exercise intensity and robot behavioral policy to the athletic performance of each user. Several experiments were carried out with the cooperation of local governments and health and welfare facilities to investigate characteristics of the PrevCareCom and to evaluate how well it adapts to its users in terms of familiarity, quality and quantity of exercise. The results of experiments showed that the users’ interest in the proposed system and sense of familiarity with the robots were encouraged by playing the game and interacting with the robots. Questionnaire results also showed that the PrevCareCom could provide appropriate exercise loads for the users’ care prevention.