Musculoskeletal BioDynamics Lab

근골격 운동역학 연구실

KAIST, Daejeon, South Korea

  Walking is the outcome of a complex process encompassing neuronal muscle control, musculoskeletal dynamics, and environmental interaction. Our research focuses on exploring the neuronal control of human walking, employing deep reinforcement learning for applications in orthopedics, rehabilitation, and sports science. The study of human gait is intimately connected to the development of bipedal and exoskeleton robots designed to assist with walking. 

  The human joint is a mechanically redundant system, composed of an array of ligaments and muscles. Our team has developed musculoskeletal dynamic models for the knee and foot joints. Utilizing these models, we have conducted simulations to estimate the forces exerted by muscles, the tension in ligaments, and the forces on articular contacts during everyday activities. This approach aids in understanding the mechanisms underlying joint injuries. 

  Abnormal movements in human joints can trigger a degenerative process by altering mechanical stresses in soft tissues and on bone surfaces. To accurately measure three-dimensional joint motion in patients, we have developed a dual X-ray fluoroscopic imaging system. This system is utilized to gain insights into abnormal joint movements and to assess changes resulting from surgical interventions. 

본 연구실에서는 인체 근골격 운동의 원리와 정형외과적 근골격 문제의 원인을 이해하기 위한 연구를 진행합니다. 다물체 동역학에 기반한 인체 전신 근골격 동역학 모델과, 이에 포함할 수 있는 해부학적 관절(무릎, 족부) 모델을 개발합니다. 역동역학 계산 기술을 이용하여 인체 운동 시 근육힘 및 관절 접촉힘을 예측하고 관절 부상의 원리 및 치료 방법을 연구합니다. AI 강화학습 기반 인체 운동 생성 기술을 연구하고, 이를 이용하여 인체 내외부 환경 변화에 따른 인체 근골격계의 운동과 운동역학 변화와, 외골격 장치를 이용한 인체 운동 보조 방법을 연구합니다. 관절을 이루는 골격들이 상호 운동하는 형상을 정밀하게 측정하는 엑스선 영상 기술을 개발하고, 이를 이용한 최적의 인공관절 및 관절 수술 기구물의 형상을 연구합니다.

응용분야 예

In this laboratory, we conduct research to understand the principles of human musculoskeletal movement and the causes of orthopedic musculoskeletal problems. We develop a whole-body human musculoskeletal dynamic model based on multi-body dynamics and anatomical joint models (knee and foot) that can be included in it. Using inverse dynamics calculation techniques, we predict muscle forces and joint contact forces during human movement and study the principles of joint injuries and its treatments. We research human motion generation technology based on AI reinforcement learning and use this technology to study the changes in musculoskeletal system movement and movement dynamics due to changes in internal and external human environmental conditions, as well as methods to assist human movement using exoskeleton devices. We develop X-ray imaging technology that accurately measures the shapes in which the skeletons that make up the joint move, and study the optimal shape of artificial joints and joint surgery tools using this technology.

Example Applications:


MSKBioDyn Lab introduction taken September, 2022

See KAIST ME Lab introduction series