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Treadmills are widely used in rehabilitation and gait analysis. However, previous studies have reported differences in terms of kinematics and kinetics between treadmill and overground walking due to physical and psychological factors. The aim of this study was to analyze gait differences due to only the physical factors of treadmill walking. The study has been published in Frontiers in Bioengineering and Biotechnology. - by Minki Jung
A human model and its gait controller were created. A patient model was created by limiting the hip joint torque of the human model. A gait assistive device with four degrees of freedom was attached to the patient model. The controller of the device was trained using a reinforcement learning method to help the patient model walk normally. - by Jonghyun Park
The skeletal model has 31 DOFs including the six DOFs in the root body. Ninety-two Hill-type muscles were attached to the lower limbs following a previous study (Rajagopal et al., 2016). The gait controller could provide excitation signals for the muscles to make it walk after a reinforcement learning to follow a reference motion (Peng et al., 2018). - by Youngjun Koo
Human movement is the result of a complex process involving neuronal muscle control, musculoskeletal dynamics, and environmental interactions. By combining forward dynamics simulations with realistic musculoskeletal models of the human joints—including the knee and foot—we investigate the neuronal control of human gait and estimate key biomechanical forces such as muscle forces, ligament tensions, and articular contact forces during daily activities. This integrated approach not only deepens our understanding of joint injury mechanisms for applications in orthopedics, rehabilitation, and sports, but also contributes directly to the design and control of bipedal and assistive exoskeleton robots.
인간의 운동은 신경계의 근육 제어, 근골격계 다이내믹스, 그리고 환경과의 상호작용이 결합된 복합적인 과정의 결과입니다. 본 연구실은 무릎 및 발관절을 포함한 인체 관절의 정밀한 근골격 모델과 순방향 다이내믹스 시뮬레이션을 결합하여, 인간 보행의 신경 제어 메커니즘을 규명하고 일상생활 중 발생하는 근육력, 인대 장력, 관절 접촉력 등 주요 생체역학적 하중을 추정합니다. 이러한 통합적 접근 방식은 정형외과, 재활, 스포츠 분야에 적용되어 관절 손상 메커니즘에 대한 이해를 심화할 뿐만 아니라, 이족보행 로봇 및 보조 외골격 로봇의 설계와 제어에도 직접적으로 기여하고 있습니다.
Musculoskeletal Knee and Ankle Modeling for Dynamics Simulation
Dynamic Balance of Forces in Joints
Forward Dynamics Simulation to Predict, Prevent, and Treat Joint Injuries
Inverse Dynamics Analysis of Human Movement
Supraspinal and Reflex Circuit Modeling for Human Movement
Interaction Between Human Body and Wearable Devices
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