Pathological sit-to-stand predictive models for control of a rehabilitation robotic device

The aim of the work presented in this paper is to realize the model for the control of a robotic interface for equilibrium assistance during sit-to-stand transfer. Interactive robotic devices designed as human-centered robotics, can give more comfortable and more efficient solutions than traditional technical devices. One supposes the need of a virtual model of the pathology. This model, called observer, aims at being used in the smoothing control part of this assisting device. A useful property of this observer should be a postural prediction ability. This article presents a study of different neural solutions a Neural Predictive Observer (NPO) and a Reduced Neural Predictive Observer (RNPO). Records used for the learning have been done from healthy people that stand up normally and quickly. Some tests will also be done in patients with cerebellar syndrome disease. The presented experimental results show the good accuracy of these approaches whatever the speed of the movement.