• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.419-420
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• 2013

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.1
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• pp.89-99
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• 2017

This paper describes the development of a thigh wearable robot for power assistance during stair climbing. In the wearable robot developed in this study, high-power BLDC motors and high-capacity harmonic reduction gears are used to effectively assist the thigh muscle during stair climbing. In particular, normal ground and stair are distinguished accurately by using wireless smart shoes, and the stair climbing assistance is performed by activating the actuators at an appropriate time. Impedance of the hip joint was effectively reduced by performing friction compensation of the gears, and a wearing adjustment mechanism was designed to fit the robot to the thigh by conveniently modifying the width and tilting angle of the robot using set collars. Consequently, the performance of the developed thigh wearable robot was verified through stair climbing experiments with EMG measurement.

• The Journal of Korea Robotics Society
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• v.12 no.2
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• pp.107-115
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• 2017

In this paper, four-bar linkage mechanism for the knee joint is developed which is used in prosthetics. But unlike the prosthetics, the feature of this mechanism is that the instantaneous center of rotation of the four-bar linkages can be moved behind the ground reaction force vector so that it can be passively supported without any external power. In addition, this mechanism is developed similar to the structure of the human knee joint for eliminating the sense of heterogeneity of the wearer. In order to design the mechanism with these two objectives, optimization design process is done using the PIAnO tool and detailed design is carried out through optimized variable values. The developed mechanism is attached to the robot which can assist the hip and ankle joints. In order to verify the operation of the developed knee mechanism, an insole type sensor was attached to the shoes to compare data values before and after wearing the robot. Result data showed that wearer wearing the exoskeleton robot with the knee mechanism was the same value regardless of whether the heavy tool is loaded or not.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.182-188
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• 2023

Due to the acceleration of an aging society, the need for lower limb exoskeletons to assist gait is increasing. And for use in daily life, it is essential to have technology that can accurately estimate gait phase even in the walking environment and walking speed of the wearer that changes frequently. In this paper, we implement an LSTM-based gait phase estimation learning model by collecting gait data according to changes in gait speed in outdoor level ground and stair environments. In addition, the results of the gait phase estimation error for each walking environment were compared after learning for both max hip extension (MHE) and max hip flexion (MHF), which are ground truth criteria in gait phase divided in previous studies. As a result, the average error rate of all walking environments using MHF reference data and MHE reference data was 2.97% and 4.36%, respectively, and the result of using MHF reference data was 1.39% lower than the result of using MHE reference data.