Computational Modeling: Human Dynamic Model

Empowering human-like walking with a bio-inspired gait controller for an under-actuated torque-driven human model

Human gait simulation plays a crucial role in providing insights into various aspects of locomotion, such as diagnosing injuries and impairments, assessing abnormal gait patterns, and developing assistive and rehabilitation technologies. To achieve more realistic gait simulation results, it is essential to use a comprehensive model that accurately replicates the kinematics and kinetics of human movement. Human skeletal models in OpenSim software provide anatomically accurate and anthropomorphic structures, enabling users to create personalized models that accurately replicate individual human behavior. However, these torque-driven models encounter challenges in stabilizing unactuated degree of freedom of pelvis tilt in forward dynamic simulations Adopting a bio-inspired strategy that ensures human balance with a minimized energy expenditure during walking, this paper addresses a gait controller for a torque-driven human skeletal model to achieve stable walking. The proposed controller employs a nonlinear model-based approach to calculate a balance-equivalent control torque and utilizes the hip-ankle strategy to distribute this torque across the lower-limb joints during the stance phase. To optimize the parameters of the trajectory tracking controller and the balance distribution coefficients, we developed a forward dynamic simulation interface established between MATLAB and OpenSim. The simulation results indicated that the torque-driven model achieves a natural gait, with joint torques closely aligning with the experimental data. The robustness of the bio-inspired gait controller was further evaluated by applying a range of external forces on the skeletal model. The robustness analysis demonstrated efficient balance recovery mechanism of the proposed bio-inspired gait controller in response to external disturbances.

Humans use minimum cost movements in a whole-body task

Humans have elegant bodies that allow gymnastics, piano playing, and tool use, but understanding how they do this in detail is difficult because their musculoskeletal systems are extraordinarily complicated. Nonetheless, common movements like walking and reaching can be stereotypical, and a very large number of studies have shown their energetic cost to be a major factor. In contrast, one might think that general movements are very individuated and intractable, but our previous study has shown that in an arbitrary set of whole-body movements used to trace large-scale closed curves, near-identical posture sequences were chosen across different subjects, both in the average trajectories of the body's limbs and in the variance within trajectories. The commonalities in that result motivate explanations for its generality. One explanation could be that humans also choose trajectories that are economical in cost. To test this hypothesis, we situate the tracing data within a forty eight degree of freedom human dynamic model that allows the computation of movement cost. Using the model to compare movement cost data from nominal tracings against various perturbed tracings shows that the latter are more energetically expensive, inferring that the original traces were chosen on the basis of minimum cost.