Salam Rahmatalla

A New Discomfort Function for Optimization-Based Posture Prediction

Using multi-objective optimization, we develop a new human performance measure for direct optimizationbased posture prediction that incorporates three key factors associated with musculoskeletal discomfort: 1) the tendency to move different segments of the body sequentially, 2) the tendency to gravitate to a comfortable neutral position, and 3) the discomfort associated with moving while joints are near their respective limits. This performance measure operates in real-time and provides realistic postures. The results are viewed using Santos TM , an advanced virtual human, and they are validated using motion-capture. This research lays groundwork for studying how and why humans move as they do.

CONTINUUM TOPOLOGY OPTIMIZATION OF BUCKLING-SENSITIVE STRUCTURES

Twoformulationsforcontinuum topologyoptimizationofstructurestakingbucklingconsiderationsinto account are developed, implemented, and compared. In thee rst, the structure undergoing a specie ed loading is modeled as a hyperelastic continuum at e nite deformations and is optimized to maximize the minimum critical buckling load. In the second, the structure under a similar loading is modeled as linear elastic, and the critical buckling load is computed with linearized buckling analysis. Specie c issues addressed include usage of suitable “ mixing rules,” a node-based design variable formulation, techniques for eliminating regions devoid of structural material from the analysis problem, and consistent design sensitivity analysis. The performance of the formulations is demonstrated on the design of different structures. When problems are solved with moderate loads and generous material usage constraints, designs using compression and tension members are realized. Alternatively, when fairly large loads together with very stringent material usage constraints are imposed, structures utilizing primarily tension members result. Issues that arise when designing very light structures with stringent material usage constraints are discussed along with the importance of considering potential geometrical instabilities in the concept design of structural systems.

Validation of predicted posture for the virtual human Santos

Digital human modeling and simulation plays an important role in product design, prototyping, and manufacturing: it reduces the number of design iterations and increases the safety and design quality of products. Posture prediction is one of the key capabilities. It is especially useful in the design of vehicle interiors for checking the reachability of buttons and determining comfort levels. This paper presents the validation of predicted posture for the virtual human Santos. The predicted posture is a physics-based model and is formulated as a multi-objective optimization (MOO) problem. The hypothesis is that human performance measures (cost functions) govern how humans move. We chose 12 subjects from four different percentiles, all Americans (female 5%, female 50%, male 50%, and male 95%). Four realistic in-vehicle tasks requiring both simple and complex functionality of the human simulations were chosen: reaching a point at the top of the A-pillar, the radio tuner button, the glove box handle, and a point on the drivers B-pillar seatbelt adjuster. The subjects were asked to reach the four target points, and the joint centers for wrist, elbow, and shoulder and the joint angle of elbow were recorded using a motion capture system. We used these data to validate our model. The validation criteria comprise R-square and confidence intervals. The results show that the predicted postures match well with the experiment results, and are realistic postures.

Dynamic Motion Planning of 3D Human Locomotion Using Gradient-Based Optimization

Since humans can walk with an infinite variety of postures and limb movements, there is no unique solution to the modeling problem to predict human gait motions. Accordingly, we test herein the hypothesis that the redundancy of human walking mechanisms makes solving for human joint profiles and force time histories an indeterminate problem best solved by inverse dynamics and optimization methods. A new optimization-based human-modeling framework is thus described for predicting three-dimensional human gait motions on level and inclined planes. The basic unknowns in the framework are the joint motion time histories of a 25-degree-of-freedom human model and its six global degrees of freedom. The joint motion histories are calculated by minimizing an objective function such as deviation of the trunk from upright posture that relates to the human models performance. A variety of important constraints are imposed on the optimization problem, including (1) satisfaction of dynamic equilibrium equations by requiring the models zero moment point (ZMP) to lie within the instantaneous geometrical base of support, (2) foot collision avoidance, (3) limits on ground-foot friction, and (4) vanishing yawing moment. Analytical forms of objective and constraint functions are presented and discussed for the proposed human-modeling framework in which the resulting optimization problems are solved using gradient-based mathematical programming techniques. When the framework is applied to the modeling of bipedal locomotion on level and inclined planes, acyclic human walking motions that are smooth and realistic as opposed to less natural robotic motions are obtained. The aspects of the modeling framework requiring further investigation and refinement, as well as potential applications of the framework in biomechanics, are discussed.

Multi-objective optimization-based method for kinematic posture prediction: Development and validation

Posture prediction plays an important role in product design and manufacturing. There is a need to develop a more efficient method for predicting realistic human posture. This paper presents a method based on multi-objective optimization (MOO) for kinematic posture prediction and experimental validation. The predicted posture is formulated as a multi-objective optimization problem. The hypothesis is that human performance measures (cost functions) govern how humans move. Twelve subjects, divided into four groups according to different percentiles, participated in the experiment. Four realistic in-vehicle tasks requiring both simple and complex functionality of the human simulations were chosen. The subjects were asked to reach the four target points, and the joint centers for the wrist, elbow, and shoulder and the joint angle of the elbow were recorded using a motion capture system. We used these data to validate our model. The validation criteria comprise R-square and confidence intervals. Various physics factors were included in human performance measures. The weighted sum of different human performance measures was used as the objective function for posture prediction. A two-domain approach was also investigated to validate the simulated postures. The coefficients of determinant for both within-percentiles and cross-percentiles are larger than 0.70. The MOO-based approach can predict realistic upper body postures in real time and can easily incorporate different scenarios in the formulation. This validated method can be deployed in the digital human package as a design tool.

Predictive discomfort of non-neutral head–neck postures in fore–aft whole-body vibration

It seems obvious that human head–neck posture in whole-body vibration (WBV) contributes to discomfort and injury risk. While current mechanical measures such as transmissibility have shown good correlation with the subjective-reported discomfort, they showed difficulties in predicting discomfort for non-neutral postures. A new biomechanically based methodology is introduced in this work to predict discomfort due to non-neutral head–neck postures. Altogether, 10 seated subjects with four head–neck postures—neutral, head-up, head-down and head-to-side—were subjected to WBV in the fore–aft direction using discrete sinusoidal frequencies of 2, 3, 4, 5, 6, 7 and 8 Hz and their subjective responses were recorded using the Borg CR-10 scale. All vibrations were run at constant acceleration of 0.8 m/s2 and 1.15 m/s2. The results have shown that the subjective-reported discomfort increases with head-down and decreases with head-up and head-to-side postures. The proposed predictive discomfort has closely followed the reported discomfort measures for all postures and rides under investigation. Statement of Relevance: Many occupational studies have shown strong relevance between non-neutral postures, discomfort and injury risk in WBV. With advances in computer human modelling, the proposed predictive discomfort may provide efficient ways for developing reliable biodynamic models. It may also be used to assess discomfort and modify designs inside moving vehicles.

Vision Performance Measures for Optimization-Based Posture Prediction

Although much work has been completed with modeling head-neck movements as well with studying the intricacies of vision and eye movements, relatively little research has been conducted involving how vision affects human upper-body posture. By leveraging direct human optimized posture prediction (D-HOPP), we are able to predict postures that incorporate one’s tendency to actually look towards a workspace or see a target. DHOPP is an optimization-based approach that functions in real time with Santos, a new kind of virtual human with a high number of degrees-of-freedom and a highly realistic appearance. With this approach, human performance measures provide objective functions in an optimization problem that is solved just once for a given posture or task. We have developed two new performance measures: visual acuity and visual displacement. Although the visual-acuity performance measure is based on well-accepted published concepts, we find that it has little effect on the predicted posture when a target point is outside one’s field of view. Consequently, we have developed visual displacement, which corrects this problem. In general, we find that vision alone does not govern posture. However, using multi-objective optimization, we combine visual acuity and visual displacement with other performance measures, to yield realistic and validated predicted human postures that incorporate vision.

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.

Comprehensive measurement in whole-body vibration

Accurate measurements of human response to whole-body vibration are essential to any conclusions about the health risks, discomfort, and assessment of suspension systems in vibration environments. While accelerometers are traditionally considered the main measurement tools in whole-body vibration studies, their measurements become questionable when they are attached to inclined surfaces or when the motion has coupled components in multiple directions. Current measurement correction methodologies are subjective and limited to simple cases. A comprehensive correction methodology using inertial sensors was used in this work to quantify human response under single fore-aft, single-vertical, and multiple-axis whole-body vibration of twelve seated subjects with supported-backrest and unsupported-backrest upright posture. Vibration files of white noise random signals with frequency content of 0.5–12 Hz and vibration magnitude of 1.8 m/s2 RMS were used in the testing. The results have shown considerable differences in the transmissibility measurements without proper correction. The work presented has the potential to standardize experimentation in whole-body vibration and make measurements more accurate and defined across labs.

A physics-based digital human model

This paper presents a comprehensive human modelling and simulation environment. This environment, called Santos™, is a new generation of digital human simulation systems that allows a user to interact with a digital character with full and accurate biomechanics and a complete muscular system, subject to the laws of physics. Major results in the areas of dynamic motion prediction, advanced posture prediction and comfort level assessment, physiology model, modelling of clothing and muscle wrapping and force assessment will be presented. This paper will feature the various modules that comprise the Santos environment.