Rajankumar Bhatt

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

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.

Simulating Equipment Interaction With Predictive Dynamics

Recent advances in predictive dynamics allow the user to not only predict physics based human motion simulations but also determine the actuation torques required to achieve those motions. The predictive dynamics approach uses optimization to predict motion while using many task based, physics based, and environment based constraints including the equations of motion. Many tasks have been simulated using this new method of predicting and simulating digital human motion, e.g. walking, running, stair climbing, and box lifting. In this research, we develop a method to predict the motion as well as effect of external equipment hanging on the digital human. The proposed method is tested on a simple case of a two degree of freedom serial chain mechanism with a simple passive system to behave as external equipment. In particular, the passive mass is assumed to be attached to the two links system with a spring and damper. The results of the proposed method are compared with the results obtained by integrating the equations of motion of the full three degree of freedom system.Copyright

Validation of Santos Biomechanics

A validation methodology for an optimization-based predictive dynamics framework for digital human motion simulation is presented in this work. The proposed validation methodology has been implemented in time and frequency domains on a predicted walking task. The methodology uses selected critical key frames in the comparison process in the time domain, as against the full profile of all joint angles. In the frequency domain, the methodology considers using fast Fourier transform and power spectrum density. In addition to human kinematics, the methodology compares inertia and ground reaction forces, a key kinetic parameter for motion validation. The results have shown considerable correlation and insight information between the predicted and the measured data.Copyright

Throwing motion generation of a biped human model

Throwing motion is a complicated whole-body movement. The kinematics and dynamics of throwing have been analyzed and simulated in the literature mostly from the experimental measurements such as motion capture. In this presentation, the overarm throwing motion of a 55 degree-of-freedom biped human model is generated from multibody dynamics and optimization. The whole-body dynamic balance is maintained during the motion through an efficient Zero-Moment Point formulation. The proposed method is completely predictive and no input reference motion is required. As outputs, the generated motion and the required actuator torques are calculated. The results show dynamically feasible human motion of throwing in a physics-based simulation environment.