Development of 3D Equipment Interaction With Predictive Dynamics in Human Motion Simulation

3D equipment interaction module in human motion simulation is developed in this paper. A predictive dynamics method is used to simulate human motion, and a helmet is modeled as the equipment that is attached to the human body. We then implement this method using the predictive dynamics task of walking. A mass-spring-damper system is attached at the top of the head as a helmet model. The equations of motion for the helmet are also derived in a recursive Lagrangian formulation within the same inertial reference frame as the human model’s. The total number of degrees of freedom for the human model is 55 — 6 degrees of freedom for global translation and rotation, and 49 degrees of freedom for the body. The helmet has 7 degrees of freedom, but 6 of them are dependent to the human model. The movement of the helmet is analyzed due to the human motion. Then, the reaction force between the human body and the equipment is calculated. Once the reaction force is obtained, it is applied to the human body as an external force in the predictive dynamics optimization process. Results include the motion of equipment, the force acting on body at the attachment point, the joint torque profiles, and the ground reaction force profiles at the foot contacting point.

Muscle Force Prediction of 2D Gait Using Predictive Dynamics Optimization

Cyclic human gait is simulated in this work by using a 2D musculoskeletal model with 12 degrees of freedom (DOF). Eight muscle groups are modeled on each leg. Predictive dynamics approach is used to predict the walking motion. In this process, the model predicts joints dynamics and muscle forces simultaneously using optimization schemes and task-based physical constraints. The results indicated that the model can realistically match human motion, ground reaction forces (GRF), and muscle force data during walking task. The proposed optimization algorithm is robust and the optimal solution is obtained in seconds. This can be used in human health domain such as leg prosthesis design.

The Hybrid Predictive Dynamics Method for Analysis, Simulation and Prediction of Human Motion

The ‘Hybrid Predictive Dynamics Method for Digital Human Modeling’ is analyzed in this work. The ‘Hybrid’ prefix mentioned in the literature recently [1], refers to the use of motion capture data for improving human motion simulations. This use of motion capture compensates for the inherent weaknesses of purely theoretical motion prediction due to deficiencies in computational power or available theoretical backgrounds. In this work, it is shown that while using the ‘Hybrid’ the more precisely and finely the human motion is modeled (if computational and theoretical limitations allow), the less will be the need for the ‘Hybrid’ method and the more will the human model be able to change the prediction if the inputs are varied (cause and effect). Several human motion scenarios are mentioned in this work. These motion tasks are: “Jogging around Markers”, “Rolling Over”, “Getting up from Prone”, “Vertical Jumping” and “Kneeling and Aiming”. The digital human model is a full-body, three dimensional model with 55 degrees of freedom. Six degrees of freedom specify the global position and orientation of the coordinate frame attached to the pelvic point of the digital human and 49 degrees of freedom represent the revolute joints which model the human joints and determine the kinematics of the entire digital human. Motion is generated by a multi-objective optimization approach. The optimization problem is subject to constraints which represent the limitations of the environment, the digital human model and the motion task. Design variables are the joint angle profiles. All the forces, inertial, gravitational as well as external, are known, except the ground reaction forces. The feasibility of the generation of that arbitrary motion by using the given ground contact areas is ensured by using the well-known Zero Moment Point (ZMP) constraint.

On the impact of hidden modeling assumptions on living systems- predictive dynamics

ONTARE. REVISTA DE INVESTIGACIÓN DE LA FACULTAD DE INGENIERÍAThis paper aims at showing the predictive modeling of living systems, particularly some commonly structured modeling assumptions which simplify the behavior of living systems. It also takes into account the stochastic modeling of basic gene expression mechanisms, such as transcription and translation, reaffirming the effect that simplifications have on the predictive behavior of living systems. These mechanisms rely on the basis of most gene expressions, signaling pathways and protein- protein interaction network models. This paper states that the usage of naïve modeling abstractions may result in predictive behaviors that are quite far from reality.