This paper proposes a system for improving daily lives of elderly people to ensure health, which we termed as"health promotion support system". In order to realize an enriched life style among elderly people, daily health care is important. Therefore, we propose a system where robot partners will assist in exercising activity among elderly people. This system is intended to provide encouragement, exercise guidance and exercise evaluation to motivate voluntary and active participation from the elderly through the help of robot partner. The robot partner presents appropriate comment based on the score using calculated information from quantitative evaluation of the measured data. Elderly people can get a good evaluation by raising their intrinsic motivation. In addition, elderly people's self-efficacy can be improved using encouraging speech from robot partner. Finally, we consider the element of communication to support voluntary participation based on survey results from elderly people and caregiver.

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In this research, we suggest a methodology for getting joint angles by Kinect sensor for rehabilitation evaluation support. We measured the motion of the arm of a patient with hemiplegia before and after the rehabilitation, and showed the range of the motion by using genetic algorithm. The range after the rehabilitation was bigger than before the rehabilitation. Based on this result, our methodology is able to evaluate the change of the motion before and after the rehabilitation for patients with hemiplegia.

This paper proposes a method of arm angle estimation by using a range image sensor that can detect each angle position in the non-contact measurement. We can solve the inverse kinematics by using relative position data, but the estimation quality is not good owing to the measurement noise of the sensor. Therefore, we apply genetic algorithm to solve the optimization problem. Furthermore, if we can model the human motion pattern from the measured data, it can be prevented falling into the local solution. In the method, we apply a feed-forward neural network (NN) for learning the motion patterns. In this paper, we show some experimental results of the angle estimation and discuss the effectiveness through the experimental results.

This paper proposes a visualization system used for motion analysis in computational system rehabilitation. First, we discuss the importance of system integration based on information technology, intelligence technology, network technology, and robot technology for rehabilitation. Next, we explain the concept of computational system rehabilitation. Next, we propose a visualization system used for motion analysis based on measurement results using a 3D range sensor in a rehabilitation. Finally, we show several examples of the proposed method to rehabilitation support, and discuss the future direction of computational system rehabilitation.