Research on Human Motion Synthesis
Concurrent tools in biomechanics and robotics communities enabled our effort to explore natural human motion having benefits in rehabilitation and facilitating development of human-inspired robots. Understanding human motion is a complex procedure that requires accurate reconstruction of movement sequences, modeling of musculoskeletal kinematics, dynamics, and actuation, and suitable criteria for the characterization of performance. These issues have much in common with the problems of articulated body systems studied in robotics research. Building on methodologies and techniques developed in robotics, a host of new effective tools have been established for the synthesis of human motion. These developments are providing new avenues for exploring human motion -- with exciting prospects for novel clinical therapies, athletic training, and performance improvement.Control and Reconstruction of Musculoskeletal Systems
The reconstruction of human dynamic motion is performed by successive projections into the null spaces of all tasks that are above it in the hierarchy formed in marker space. The control framework provides an efficient way to map motion patterns to accurate musculoskeletal models without the need for inverse kinematics computations. It provides real-time motion dynamic and incorporates performance metrics of minimization of muscular effort. These metrics enable predictions of novel motions which can be entrained to an individual.
Video: Three-dimensional Muscle Actuated Dynamic Simulation of a Professional Football Throwing
We created the dynamic simulation of an American football player throwing motion which provides insight into how the muscle forces contribute to operational-space accelerations of the throwing hand.Real-time Simulation and Motion Analysis
Accurate modeling and simulationof human motion have a significant impact on a host of domains: from the rehabilitation of patients with physical impairments to the training of athletes or the design of machines for physical therapy and sport. In the case of rehabilitation, a patient may benefit from knowing what movement pattern might influence loads on a specific joint or tissue. For example, a patient who has undergone arthroscopic knee meniscectomy is at high risk of developing knee joint osteoarthritis, particularly if they walk with large knee adduction (varus) moments. In this scenario, the patient can benefit from knowing what movement pattern could be used to reduce loading on the medial compartment of the knee during walking, thus alleviating the stresses on the articular surface of the knee and reducing the risk of developing osteoarthritis. An additional term describing the loads at the knee can easily be added to the current optimization criteria and the generalized robotics technique can be used to predict a novel gait pattern for the patient that minimizes energy expenditure during walking as well as reducing the loads on the knee.