Improved algorithms for movement pattern recognition and classification in physical rehabilitation

In this paper, a solution is presented to support both existing and future movement rehabilitation applications. The presented method combines the advantages of human computer interaction based movement therapy with the cognitive property of intelligent decision making systems. With this solution, therapy could be fully adapted to the needs of the patients and conditions while maintaining a sense of success in them, thereby motivating them. In our modern digital age, the development of HCI interfaces walk together with the growing of user needs for them. The available technologies have limitations, which can reduce the effectiveness of modern input devices, such as the Kinect sensor or any other similar sensors. In this article, multiple newly developed and modified methods are introduced with the aim to overcome these limitations. This methods can fully adapt the movement pattern recognition to the users' skills. The main are to apply this method, is movement rehabilitation, where the supervisor, therapist can personalize the rehabilitation exercises due to the Distance Vector Based Gesture Recognition (DVGR), Reference Distance Based Synchronous/Asynchronous Movement Recognition (RDSMR/RDAMR) and the Real-Time Adaptive Movement Pattern Classification (RAMPC) methods. Keywords: cognitive infocommunication, rehabilitation exercises, motivation, Kinect sensor, adaptive interface controller, real-time gesture recognition and classification