Timothy Marler

Towards a new generation of virtual humans

This paper presents work from an ongoing project towards developing a new generation of virtual human models that are highly realistic in appearance, movement, and feedback. Santos™, an anatomically correct human model with more than 100 degrees of freedom, is an avatar that exhibits extensive modelling and simulation capabilities, resides in a virtual environment, and conducts human-factors analysis. The paper presents an optimisation-based approach to posture and motion prediction that allows the avatar to operate with autonomy rather than depending on stored animations and data or being restricted by inverse kinematics. It also presents approaches to determining reach envelopes and workspace zone differentiation, and discusses methods for evaluating the physiological status of the virtual human as it completes tasks. Muscle modelling including muscle wrapping, muscle force and stress determination is also discussed. Finally, the process of building a 25-DOF hand model is described. The result is an exciting step towards a virtual human that is more extensive and complete than any other.

A new digital human environment and assessment of vehicle interior design

Vehicle interior design directly relates to driver performance measures such as comfort, efficiency, risk of injury, and vehicle safety. A digital human is a convenient tool for satisfying the need to reduce the design cycle in order to save time and money. This paper presents a digital human environment, Santos(TM), developed at The University of Iowa, and its assessment as applied to the interior design of a Caterpillar vehicle. The digital human environment involves male models and accommodates a large percentage of the operator population (from the 5th percentile to the 95th percentile). It has a user-friendly interface and includes various tools such as posture prediction, reachability check, zone differentiation, and biomechanics assessment for the upper body and hand. The key difference from a traditional digital human environment is that Santoss environment is optimization-based. This can answer design questions regarding whether the operator can reach relevant controls, what the comfort level is if one can reach the control, and what strength is required of the operator to pull a shift, etc. The illustrative example of a Caterpillar cab is demonstrated using this digital human environment.

Development of the virtual-human Santos®

This paper presents the background and history of the virtual human Santos™ developed by the Virtual Soldier Research (VSR) Program at The University of Iowa. The early virtual human environment was called Mira™. This 15-degree-of-freedom (DOF) upper-body model with posture and motion prediction was funded by John Deere Inc. and US Army TACOM Automotive Research Center. In 2003 US Army TACOM began funding VSR to develop a new generation of virtual humans called Santos (109 DOFs), which was to be another generation of Mira. Later on, Caterpillar Inc., Honda R&D North Americas, Natick Soldier System Center, and USCAR (GM, Ford, and Chrysler) joined the VSR partnership. The objective is to develop a new generation of digital humans comprising realistic human models including anatomy, biomechanics, physiology, and intelligence in real time, and to test digital mockups of products and systems before they are built, thus reducing the significant costs and time associated with making prototypes. The philosophy is based on a novel optimization-based approach for empowering these digital humans to perform, un-aided, in a physics-based world. The research thrusts include the following areas: (1) predictive dynamics, (2) modeling of cloth, (3) hand model, (4) intuitive interface, (5) motion capture, (6) muscle and physiology modeling, (7) posture and motion prediction, (8) spine modeling, and (9) real-time simulation and virtual reality (VR). Currently, the capabilities of Santos include whole-body posture prediction, advanced inverse kinematics, reach envelope analysis, workspace zone differentiation, muscle force and stress analysis, muscle fatigue prediction, simulation of walking and running, dynamic motion prediction, physiologic assessment, a user-friendly interface, a hand model and grasping capability, clothing modeling, thermo discomfort assessment, muscle wrapping and sliding, whole-body vibration analysis, and collision avoidance.

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.

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.

Modeling dual-arm coordination for posture : An optimization-based approach

In the field of human modeling, there is an increasing demand for predicting human postures in real time. However, there has been minimal progress with methods that can incorporate multiple limbs with shared degrees of freedom (DOFs). This paper presents an optimization-based approach for predicting postures that involve dual-arm coordination with shared DOFs, and applies this method to a 30-DOF human model. Comparisons to motion capture data provide experimental validation for these examples. We show that this optimization-based approach allows dual-arm coordination with minimal computational cost. This new approach also easily extends to models with a higher number of DOFs and additional end-effectors.

Santos: A Physics-Based Digital Human Simulation Environment:

This paper presents a comprehensive human modeling and simulation environment under development by the University of Iowa Virtual Soldier Research (VSR) program. This environment, called SantosTM, is a new generation of digital human simulation systems that allows for a user to interact with a digital character with full and accurate biomechanics and a complete muscular system, subject to the laws of physics. Highlighting major results in the areas of dynamic motion prediction, modeling of clothing, modeling of muscle activation and loading, and the Santos intuitive interface will be presented. This paper will feature the various modules that comprise the Santos environment.

A new multi-objective optimization formulation for rail-car fleet sizing problems

With potential application to a variety of industries, fleet sizing problems present a prevalent and significant challenge for engineers and managers. This is especially true of applications involving rail cars, where origins and destinations have capacity restrictions. This paper presents a new multi-objective optimization formulation, solution method, and analysis for multi-periodic fleet sizing problems of various sizes. Profit and quality (minimal unmet demands) represent conflicting objectives and are maximized simultaneously. The Pareto optimal set is depicted and is used for trade-off analysis. The solution involves the optimal fleet size as well as the optimal rail-car allocation strategy. The proposed approach is applied to an example problem and is shown to be successful, ultimately providing a new managerial tool for planning and analyzing rail-car fleets more effectively.

Neural network for dynamic human motion prediction

Successful use of artificial neural network (ANN) to simulate human model motion.Experimentation results of using ANN to simulate the tasks of walking and jumping.The use of ANN reduces the simulation time from 1-40?min to a fraction of a second. Digital human models (DHMs) are critical for improved designs, injury prevention, and a better understanding of human behavior. Although many capabilities in the field are maturing, there are still opportunities for improvement, especially in motion prediction. Thus, this work investigates the use of an artificial neural network (ANN), specifically a general regression neural network (GRNN), to provide real-time computation of DHM motion prediction, where the underlying optimization problems are large and computationally complex. In initial experimentation, a GRNN is used successfully to simulate walking and jumping on a box while using physics-based human simulations as training data. Compared to direct computational simulations of dynamic motion, use of GRNN reduces the calculation time for each predicted motion from 1-40?min to a fraction of a second with no noticeable reduction in accuracy. This work lays the foundation for studying the effects of changes to training regiments on human performance.