Yujiang Xiang

Optimization-based prediction of asymmetric human gait

An optimization-based formulation and solution method are presented to predict asymmetric human gait for a large-scale skeletal model. Predictive dynamics approach is used in which both the joint angles and joint torques are treated as unknowns in the equations of motion. For the optimization formulation, the joint angle profiles are treated as the primary unknowns, and velocities and accelerations are calculated using them. In numerical implementation, the joint angle profiles are discretized using the B-spline interpolation. An algorithm is presented to inversely calculate the joint torques and the ground reaction forces. The sum of the joint-torques squared, called the dynamic effort, is minimized as the human performance measure. Constraints are imposed on the joint strengths (torques) and joint ranges of motion along with other physical constraints. The formulation is validated by simulating a symmetric gait and comparing the results with the experimental data. Then asymmetric gait motion is simulated, where the left and right step lengths are different. The kinematics and kinetics results from the simulation are presented and discussed. Predicted ground reaction forces are explained by using the inverted pendulum model. Predicted kinematics and kinetics have trends that are similar to those reported in the literature. Potential practical applications of the formulation and the solution approach are discussed.

A Robust Formulation for Prediction of Human Running

Abstract : A method to simulate digital human running using an optimization-based approach is presented. The digital human is considered as a mechanical system that includes link lengths, mass moments of inertia, joint torques, and external forces. The problem is formulated as an optimization problem to determine the joint angle profiles. The kinematics analysis of the model is carried out using the Denavit-Hartenberg method. The B-spline approximation is used for discretization of the joint angle profiles, and the recursive formulation is used for the dynamic equilibrium analysis. The equations of motion thus obtained are treated as equality constraints in the optimization process. With this formulation, a method for the integration of constrained equations of motion is not required. This is a unique feature of the present formulation and has advantages for the numerical solution process. The formulation also offers considerable flexibility for simulating different running conditions quite routinely. The zero moment point (ZMP) constraint during the foot support phase is imposed in the optimization problem. The proposed approach works quite well, and several realistic simulations of human running are generated.

3D HUMAN LIFTING MOTION PREDICTION WITH DIFFERENT PERFORMANCE MEASURES

This paper presents an optimization-based method for predicting a human dynamic lifting task. The three-dimensional digital human skeletal model has 55 degrees of freedom. Lifting motion is generated by minimizing an objective function (human performance measure) subjected to basic physical and kinematical constraints. Four objective functions are investigated in the formulation: the dynamic effort, the balance criterion, the maximum shear force at spine joint and the maximum pressure force at spine joint. The simulation results show that various human performance measures predict different lifting strategies: the balance and shear force performance measures predict back-lifting motion and the dynamic effort and pressure force performance measures generate squat-lifting motion. In addition, the effects of box locations on the lifting strategies are also studied. All kinematics and kinetic data are successfully predicted for the lifting motion by using the predictive dynamics algorithm and the optimal solution was obtained in about one minute.

Enhanced optimisation-based inverse kinematics methodology considering joint discomfort

This paper presents an optimisation-based inverse kinematics (IK) method to compute realistic joint angles from marker-based positional data of a highly redundant three-dimensional digital human model. The effects of skin movement artefact and spine-shoulder joint coupling are alleviated by augmenting the optimisation problem with a skeletal discomfort function which is applied to the spine-shoulder-neck region only. The discomfort function ensures natural movement and avoids extreme joint motion. A key feature of the proposed error-minimisation-based method is that the error of the joint centre positions are not only minimised in the objective function, but also considered in the constraints so that the motion capture motion can be tracked more accurately. The methodology is demonstrated by three numerical examples, including vehicle-braking, throwing, and sprinting. The results showed that the proposed method is approved to be numerically stable and efficient to convert the motion capture data into joint space for a large-scale digital human model. Finally, the IK results are verified with the Visual3d software from C-motion.

Determining the three-dimensional relation between the skeletal elements of the human shoulder complex

In this paper, we present an inverse kinematics method to determining human shoulder joint motion coupling relationship based on experimental data in the literature. This work focuses on transferring Euler-angle-based coupling equations into a relationship based on the Denavit-Hartenberg (DH) method. We use analytical inverse kinematics to achieve the transferring. For a specific posture, we can choose points on clavicle, scapula, and humerus and represent the end-effector positions based on Euler angles or DH method. For both Euler and DH systems, the end-effectors have the same Cartesian positions. Solving these equations related to end-effector positions yields DH joint angles for that posture. The new joint motion coupling relationship is obtained by polynomial and cosine fitting of the DH joint angles for all different postures.

A validation framework for predictive human models

A validation framework is introduced in this work to evaluate the motion of a predictive human model and provide feedback to the model developers for refinement in ergonomic applications. Two qualitative and two quantitative benchmark tests were designed and used to assess the strength and weakness of the model and to localise abnormalities in the predicted motion. Twelve subjects participated in a whole-body motion task, and another 12 subjects participated in the subjective evaluation of the predicted motion. The validation framework was able to highlight the weakness and limitations of a predicted human model with 55 degrees of freedom in a box-lifting task. The results have shown that the proposed framework was very effective in identifying the flaws in the model under investigation and in giving guides for improvement and acceptance.

Efficient zmp formulation and effective whole-body motion generation for a human-like mechanism

An approach of generating dynamic biped motions of a human-like mechanism is proposed. An alternative and efficient formulation of the Zero-Moment Point for dynamic balance and the approximated ground reaction forces/moments are derived from the resultant reaction loads, which includes the gravity, the externally applied loads, and the inertia. The optimization problem is formulated to address the redundancy of the human task, where the general biped and task-specific constraints are imposed depending on the task requirements. The proposed method is fully predictive and generates physically feasible human-like motions from scratch; it does not require any input reference from motion capture or animation. The resulting generated motions demonstrate how a human-like mechanism reacts effectively to different external load conditions in performing a given task by showing realistic features of cause and effect. In addition, the energy-optimality of the upright standing posture is numerically verified among infinite feasible static biped postures without self contact. The proposed formulation is beneficial to motion planning, control, and physics-based simulation of humanoids and human models.Copyright

Alternative Formulations for Optimization-Based Human Gait Planning

*† ‡ § Simulating human motion is a complex problem due to redundancy of the human musculoskeletal system. The concept of task-based dynamic motion prediction using singleor multi-objective optimization techniques provides a viable approach for predicting dynamic gait motions of digital humans, subjected to basic physical and kinematical constraints. The task-based motion prediction is in fact a numerical optimal control problem. Alternative formulations for simulation of human gait motion are possible and can be solved by modern nonlinear optimization methods. Different ways to discretize the equations of motion are presented, namely finite difference, and Hermite and B-spline interpolations. The advantages and disadvantages of different formulations are discussed. Since the human gait simulation utilizes gradient-based optimization techniques, analytical gradients of objective and constraint functions are provided. A skeletal model for the lower body having 18 degrees of freedom is used to demonstrate the formulations, and is solved by a large-scale sparse nonlinear programming solver.

Dynamic optimization of human stair-climbing motion

Abstract : The objective of this paper is to present our method of predicting and simulating visually realistic and dynamically consistent human stair-climbing motion. The digital human is modeled as a 55-degrees of freedom branched mechanical system with associated human anthropometry-based link lengths, mass moments of inertia, and centers of gravity. The joint angle profiles are determined using a B-spline-based parametric optimization technique subject to different physics-based, task-based, and environment-based constraints. The formulation offers the ability to study effects of the magnitude and location of external forces on the resulting joint angle profiles and joint torque profiles. Several virtual experiments are conducted using this optimization-based approach and results are presented.

Determining the Static Joint Torques of a Digital Human Model Considering Balance

This paper presents a methodology for determining the static joint torques of a digital human model considering balance for both standing and seating tasks. An alternative and efficient formulation of the Zero-Moment Point (ZMP) for static balance and the approximated (ground/seat) support reaction forces/moments are derived from the resultant reaction loads, which includes the gravity and externally applied loads. The proposed method can be used for both standing and seating tasks for assessing the stability/balance of the posture. The proposed formulation can be beneficial to physics-based simulation of humanoids and human models. Also, the calculated joint torques can be considered as an indicator to assess the risks of injuries when human models perform various tasks.Copyright