Robust biomimetic models of human legs to solve high-dimensional real-time control problems

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Project Summary Anatomically validated musculoskeletal models of human limbs computed faster than real-time are tools that will help advance the control of neuroprosthetics, rehabilitation, and the study of motor control principles. However, the current state-of-the-art models cannot be both accurate and fast. We propose to develop a new generation of validated real-time human leg models with musculoskeletal dynamics that are robust over the full range of multidimensional motion. The first aim is to validate a lower-limb model in the full range of static postures. The second aim is to validate a lower-limb model during dynamic locomotor tasks. Building on our previous work, we will use OpenSim model repository as a starting point for the iterative process of validating the muscle anatomy and function using published anatomical data. We expect to recreate the full range of leg postures with the validated model. We will then collect data during locomotor tasks performed by healthy humans on the split-belt treadmill with simultaneous re- cordings of ground reaction forces, full-body motion capture, and surface electromyography from rep- resentative leg muscles. The model will be validated over a rich experimental dataset for locomotor pat- terns required in asymmetric stepping on a self-paced treadmill. We expect to validate the dynamic model by estimating in real-time the observed full body kinematics from muscle activity and ground reaction forces. The inverse solutions will allow us to estimate the ongoing spatiotemporal patterns of muscle activity. At the conclusion of this study we will develop the detailed lower-limb model with high-di- mensional robust muscle path simulations to predict limb motion in real-time. The outcomes of this proposal will inform future work on the use of the real-time musculoskeletal models for the develop- ment of augmentation devices and the clinical assessment of locomotor deficits.