The purpose of this study was to suggest the method for automated locomotion modes (Level Walking, Stair Ascent, Stair Descent) detection based on the Radial Basis Function Support Vector Machine (RBF-SVM) for the hip gait assist robot. The universal hip gait assist robot had a limit in detection of the walking intention of users because of the limited sensors’ quantity. Through the offline training, using MATLAB, we trained the collected gait data of users wearing the hip gait assist robot and obtained the parameter of the RBF-SVM model. In the online test, using LabVIEW, we developed the algorithm for the locomotion modes decision of individuals using the optimized parameter of the RBF-SVM. Finally, we executed the gait test for three terrains through the walking environment’s test platform. As a result, the locomotion modes decision rate for three terrains was 98.5%, 99%, and 98% respectively. And the decision delay time of algorithm was 0.03 s, 0.03 s, and 0.06 s respectively.

In the case of paralysis caused by brain diseases and accidents, proper rehabilitation and supplementary systems for daily life are essential. In this study, we designed a system that can enable daily life by supporting the hand of a patient whose function has been lost to paralysis. The hand exoskeleton robot proposed in this study can be transported for the purpose of ADL (activities of daily living). It was designed to focus on the most important assistant for the thumb and index finger. The hand exoskeleton robot proposed in this study enables grasp and pinch motor skills during the human hand operation through the specific mechanism for the segment movement of the human finger simultaneously. Finally, the finger movement trace of the wearer through the vision system is measured, respectively.

Patients with complete paralysis that only walk with the assistance of exoskeleton robots because they lost their ability to walk. However, robots do not allow the exoskeleton robot to grasp the current state before walking and change the walking pattern. A "Stability Circle Region" was proposed to determine the current state of the exoskeleton robot. The Stability Circle is an area that can determine the possibility of a fall situation before the next walk using the link parameters of the robot and the current center of gravity of the patients. This study verified the validity of "stability circle" by simulating the change in the center of mass. Simulation results can be used to determine the stability of walking depending on whether the position of the center of mass before the walking is included in the circle area.