Hybrid robotic gait training system with an adaptive training program for the patient with a gait disorder

Abstract

[한글]

[영문]Gait, a method of locomotion, is a process that an animal moves itself from one position to another. The human gait serves an individual’s basic need to move from place to place. As such, the gait is the most common in all human movements. It also can provide independence and in the many activities of daily life. The importance of gait as an ability of humans has been emphasized for a long time by many researchers. The rapid-onset gait disorder represents the combined effects of more than one coexisting condition. Therefore, many kinds of gait training methods have been applied to recover or improve the walking abilities of patients with gait disorders. However, various conventional gait training methods have some limitations due to the inappropriateness and the ineffectiveness in the rehabilitation training. In this dissertation, a new hybrid robotic gait training system (HRGT) with an adaptive training program for the patients with a gait disorder was developed. Predefined joint motions of hip and knee were applied using robotic driving parts including AC servo motors and linear actuators. Functional-electrical-stimulation (FES) could be also applied to the system to control ankle dorsi/ plantarflexors based on the individual’s gait cycle during training. A graphic-user-interface (GUI) for the control algorithm and training program was also designed to provide patient information, personalized adaptive gait training pattern, and FES timing. Experimental validation was also conducted to assess the compatibility of the newly developed system for gait training. The kinematic validation confirms the correctness of the provided trajectory in static and dynamic conditions during gait training. The kinetic validation calculates joint moments provided by the developed gait training system. FES validation confirms the accuracy of the provided FES trigger signal during gait training. It is very difficult to measure various biomechanical parameters and the effects experimentally during gait training. Therefore, the gait training simulation was also performed to enable to estimate effects of gait training using CAD models of the RGT with the human musculoskeletal model and the multi-body dynamics. We expect that the developed HRGT system with FES could be applied very practically to recover walking abilities of patients with a gait disorder and the gait training simulation also could provide useful information and proper guidelines for the rehabilitation training of patients with a gait disorder.