This paper deals with the development of a passive modular hip exoskeleton system aimed at preventing musculoskeletal low back pain, which commonly occurs in heavy weight transport workers, by improving back muscle strength. The passive exoskeleton system has the advantage of being lightweight, making it suitable for modular exoskeleton systems. The cam and spring actuator designed in this study was applied to the passive modular exoskeleton system to build human hip and lumbar muscle strength. In order to evaluate the effectiveness of the passive modular exoskeleton system, a test was performed in which a subject lifted a 15 kg weight three times in a stoop posture, using heart rate measurement and Borg scale recording. According to the results, all subjects showed 26.83% lower maximum heart rate and 34.73% lower average heart rate than those who did not wear the system, and Borg scale evaluation result was lower. All subjects wore this system and did not experience back pain during the experiment. Through this study, we validated the effectiveness of the passive modular exoskeleton system and proved that this system can build the strength of industrial workers and be a solution to prevent musculoskeletal lumbar disease.

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.

Automation of electronic connectors has been in demand, based on automation of assembly of electronic products. In this study, we propose a new classification of electronic connector, for grasping and assembling. We analyze characteristics of electrical connectors often used at actual industrial sites, from the perspective of the robot, not a person. As a result, it is appropriate to classify the grasp, according to the shape of the electric connector. For the assembly, we suggested that classification should be based on directions are different, because of interference of the electric wire and peripheral parts. We hope that this research will become a new basis, for electrical connector assembly.