• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2013.05a
• /
• pp.985-986
• /
• 2013

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2013.05a
• /
• pp.867-868
• /
• 2013

• Journal of the Korean Society of Clothing and Textiles
• /
• v.44 no.6
• /
• pp.1107-1119
• /
• 2020

This study analyzed a subject's body reaction and subjective sensation when wearing a gait-assistive rehabilitation robot. The research method measured skin and clothing surface temperatures for 'seating-standing' and 'walking in place' exercises after wearing a gait-assistive rehabilitation robot. In addition, subjective sensation and satisfaction were evaluated on a 7-point Likert scale. The study results showed that the average skin temperature during exercise while wearing the gait-assistive rehabilitation robot was within a comfortable range. However, during the 'seating-standing' exercise, the skin temperature was slightly lowered. Additionally, the clothing surface temperature tended to be lower than the pre-exercise temperature after all exercises. The subjective sensation evaluation results showed that the wear comfort of the waist part was low during mobility/activity. In addition, an overall improvement in the wear comfort of the robot is necessary. The short-time movement of wearing and walking in the gait-assistive rehabilitation robot did not interfere with the thermal comfort of the body. However, the robot needs to be ergonomically improved in consideration of the long wearing time along with improved material that to satisfy overall wearing comfort.

• Journal of the Korean Society for Precision Engineering
• /
• v.32 no.10
• /
• pp.879-883
• /
• 2015

Conventional gait rehabilitation requires at least three therapists in a traditional rehabilitation training program. Several robots have been developed to reduce human burden and increase rehabilitation efficacy. In this study, we present a lower-limb wearable robot (WA-H) for gait rehabilitation of hemiplegia patients, and propose a protocol of 12 weeks gait rehabilitation training program using WA-H. To identify the efficacy of the robot and protocols, we conducted a clinical study with two actual hemiplegia patients and observed a chronological change of ambulation ability through four assessments. We discovered the progression of results by 6 minute walking test, TUGT (Timed Up and Go Test), SPPB (Short Physical Performance Battery), BBS (Berg Balance Test), and Fugl-Meyer score. The torques generated in the normal side and paralyzed side of the patient became similar, indicating rehabilitation. The result also showed the walking of the paralysis patient improved and imbalance motion had considerable improved performance.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.6
• /
• pp.518-523
• /
• 2015

This paper proposes a gait phase classifier using a Recurrent Neural Network (RNN). Walking is a type of dynamic system, and as such it seems that the classifier made by using a general feed forward neural network structure is not appropriate. It is known that an RNN is suitable to model a dynamic system. Because the proposed RNN is simple, we use a back propagation algorithm to train the weights of the network. The input data of the RNN is the lower body's joint angles and angular velocities which are acquired by using the lower limb exoskeleton robot, ROBIN-H1. The classifier categorizes a gait cycle as two phases, swing and stance. In the experiment for performance verification, we compared the proposed method and general feed forward neural network based method and showed that the proposed method is superior.

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.11
• /
• pp.936-942
• /
• 2005

Recently the exoskeletal power assistive equipment which is a kind of wearable robot has been widely developed to help the human body motion. For the elderly people and patients, however, some limits exist due to the weight and volume of the equipments. As a feasible solution, a tendon-driven exoskeletal power assistive device fur the lower body, and caster walker are proposed in this research. Since the caster walker carries the heavy items, the weight and volume of the wearable exoskeleton are minimized. The key control is used to generate the joint torque required to assist motions such as sitting, standing and walking. Experiments were performed for several motions and the EMG sensors were used to measure the magnitude of assistance. When the motion of sitting down and standing up was compared with and without wearing the proposed device, the $25\%$ assistance was acquired.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2014.07a
• /
• pp.17-18
• /
• 2014

현대사회와 미래사회는 가속화되어지는 IT기술에 의해서 융합되어진 작업의 효율 및 성능의 발전이 이슈화되고 있다. 따라서 1990년대부터는 군사 및 재활분야와 함께 제조업 및 유통업 등 전반적인 산업 모두에서 근력보조기구에 대한 연구가 활발히 진행되고 있다. 과거에는 일반인이 무거운 짐을 운반하는 것을 완전한 로봇이 대체하거나 몸이 불편한 사회적 약자가 휠체어 및 지팡이 또는 전동 휠체어와 같은 보조 개념이 아닌 완전한 대체의 개념을 가지고 있었다. 그러나 웨어러블이 대두됨에 따라 기계와 인체가 합쳐지는 상호작용 근력보조기구가 탄생했다. 근력보조기구는 힘/토크 센서를 통한 인간과 로봇간의 상호작용에 의해 인간의 다양한 상지 및 하지 동작을 구현할 수 있는 근력 강화용 웨어러블 로봇 등이 있다.

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