Procedural human motion generation is still an open area of research. Most research into procedural human motion focus on two problem areas: the realism of the generated motion and the computation time required to generate the motion. Realism is a problem because humans are very adept at spotting the subtle nuances of human motion and so the computer generated motion tends to look mechanical. Computation time is a problem because the complexity of the motion generation algorithms results in lengthy processing times for greater levels of realism. The balancing human problem poses the question of how to procedurally generate, in real-time, realistic standing poses of an articulated human body. This report presents the balancing human algorithm that addresses both concerns: realism and computation time. Realism was addressed by integrating two existing algorithms. One algorithm addressed the physics of the human motion and the second addressed the prediction of the next pose in the animation sequence. Computation time was addressed by identifying techniques to simplify or constrain the algorithms so that the real-time goal can be met. The research methodology involved three tasks: developing and implementing the balancing human algorithm, devising a real-time simulation graphics engine, and then evaluating the algorithm with the engine. An object-oriented approach was used to model the balancing human as an articulated body consisting of systems of rigid-bodies connected together with joints. The attributes and operations of the object-oriented model were derived from existing published algorithms.

The role of digital humans in product design and assessment is ever increasing. Accurate digital human models are used to provide feedback on virtual prototypes of products, thus reducing costs and shortening the design cycle. An essential part of product assessment in the virtual world is the ability of the human model to interact correctly and naturally with the product model. This involves reaching, grasping and manipulation. This work addresses the difficult problem of grasp planning for digital humans. We develop a semi-interactive system for synthesizing grasps based on the object's shape, and implement this system for SantosTM, the digital human developed at the Virtual Soldier Research Program at the University of Iowa. The system is composed of three main parts: First, a shape matching module that creates an initial power grasp for the object based on a database of pre-calculated grasps. Second, an optimization based module provides control of the fingertip locations. This can be used to synthesize precision grasps under the user's guidance. Finally, a grasp quality module provides feedback about the grasp's mechanical stability. The novelty of our approach lies in the fact that it takes into consideration the upper body posture when planning the grasp, so the whole arm and the torso are involved in the grasp.

The vast majority of products and processes in industry and academia require human interaction. Thus, digital human models (DHMs) are becoming critical for improved designs, injury prevention, and a better understanding of human behavior. Although many capabilities in the DHM field continue to mature, there are still many opportunities for improvement, especially with respect to posture- and motion-prediction. Thus, this thesis investigates the use of artificial neural network (ANN) for improving predictive capabilities and for better understanding how and why human behave the way they do. With respect to motion prediction, one of the most challenging opportunities for improvement concerns computation speed. Especially, when considering dynamic motion prediction, the underlying optimization problems can be large and computationally complex. Even though the current optimization-based tools for predicting human posture are relatively fast and accurate and thus do not require as much improvement, posture prediction in general is a more tractable problem than motion prediction and can provide a test bead that can shed light on potential issues with motion prediction. Thus, we investigate the use of ANN with posture prediction in order to discover potential issues. In addition, directly using ANN with posture prediction provides a preliminary step towards using ANN to predict the most appropriate combination of performance measures (PMs) - what drives human behavior. The PMs, which are the cost functions that are minimized in the posture prediction problem, are typically selected manually depending on the task. This is perhaps the most significant impediment when using posture prediction. How does the user know which PMs should be used? Neural networks provide tools for solving this problem. This thesis hypothesizes that the ANN can be trained to predict human motion quickly and accurately, to predict human posture (while considering external forces), and to determine the most appropriate combination of PM(s) for posture prediction. Such capabilities will in turn provide a new tool for studying human behavior. Based on initial experimentation, the general regression neural network (GRNN) was found to be the most effective type of ANN for DHM applications. A semi-automated methodology was developed to ease network construction, training and testing processes, and network parameters. This in turn facilitates use with DHM applications. With regards to motion prediction, use of ANN was successful. The results showed that the calculation time was reduced from 1 to 40 minutes, to a fraction of a second without reducing accuracy. With regards to posture prediction, ANN was again found to be effective. However, potential issues with certain motion-prediction tasks were discovered and shed light on necessary future development with ANNs. Finally, a decision engine was developed using GRNN for automatically selecting four human PMs, and was shown to be very effective. In order to train this new approach, a novel optimization formulation was used to extract PM weights from pre-existing motion-capture data. Eventually, this work will lead to automatically and realistically driving predictive DHMs in a general virtual environment.

Conventional ergonomics analysis involves building physical mockups and conducting simulated operations, such that the constraints experienced by the human subjects can be directly observed. The limitations of this approach are that, they are resource intensive, less flexible for testing design variability and difficult to involve large number of subjects to account for population variability and thus, it is a reactive approach. With the advent of computer aided techniques, efforts are on to support ergonomics analysis processes for proactive design approaches. To achieve this, real scenarios are being simulated in virtual environments which include induction of representative human subjects into such envi-ronments and are termed as Digital Human Models (DHMs). The main challenge in the simulation of humans is to obtain the naturalness that is perceived in human interaction with the environment. This naturalness can be achieved by synergetically modeling the physical performance and cognitive aspects of humans in such a way that one aspect caters the requirements posed from the other. But in current DHMs, the various elements in the physical performance aspect are not in line with the requirements of higher level behav¬ioral/cogntive aspects. Towards meeting this objective, the influence of physical performance aspects of humans on achieving naturalness when DHM interacts with the virtual environment has been studied. In this work, the task of ’reach’ has been chosen for studying the influence of kinematic structure, posture modeling and stability aspects on achieving naturalness for both discrete and statistical humans. Also, a framework has been developed to give instructions based on relations between the segments of the body and objects in the environment. Kinematic structure is modeled to simulate the humans with varied dimensions taking care of the change of link fixations necessary for various tasks. The conventional techniques used to define kinematic structures have limitations in resolving the issues that arise due to change in link fixations. In this work a new scheme is developed to effectively handle precedence relationship sand change of configuration of the existing posture whenever link fixations change. The advantage with this new approach is that complex maneuvers which involve different link fixations and multiple fixations at a time can be managed automati¬cally without the user’s intervention. Posture prediction involves estimation of the whole body posture which a human operator is likely to assume while performing a task. It involves finding a configuration satisfy¬ing the constraints like placing the body-segments in preferred locations of the task space and satisfying the relations specified between body segments. There are two main chal¬lenges in this regard; one is achieving naturalness in the predicted postures and the other is minimizing the mathematical complexity involved in finding the real time solutions. A human-specific posture prediction framework is developed which can handle a variety of constraints and realize the natural behavior. The approach is completely geometry based and unlike numerical methods, the solutions involve no matrix inversions. Digital human models (DHMs), both as avatars and agents, need to be controlled to make them manipulate the objects in the virtual world. A relations based description scheme is developed to instruct the DHM to perform the tasks. The descriptions as a set of relations and postures involve simple triplets and quadruplets. As the descriptions constitute only the relations between actors, incorporating different behavior models while executing the relations is feasible through this framework. Static balancing is one of the crucial factors influencing the posture of humans. The stim¬ulus for the static balancing is the body’s self weight and is governed by the location of its point of application, namely the center of mass (COM). The main focus is on determin¬ing suitable locations for COM to infer about the mobility of the segments which supports the human structure in slow motion scenarios. Various geometric conditions necessary for support retaining, altering are deduced and developed strategies for posture transitions for effective task performance while maintaining stability. These conditions are useful in de¬termining the posture transition required to shift the COM from one region to the other and thus the behaviors realized while accomplishing the tasks are realistic. These behaviors are simulated through statically stable walking and sit to stand posture transition. One of the advantages of employing DHMs in virtual simulations is the feasibility of creat¬ing human models with varied dimensions. A comparative study is conducted on different methods based on probabilistic and statistic theory as an alternative to the percentile based approach with a view to answer the questions like ’what percentage of people can success-fully accomplish a certain task’ and ’how well can people perform when they reach a point in the operational space’. The case study is done assuming upper and lower arms of hu¬mans as a two link planar manipulator and their link lengths as random variables. Making use of statistical DHMs, the concept of task dependent boundary manikins is introduced to geometrically characterize the extreme individuals in the given population who would ac-complish the task. Simulations with these manikins would help designers to visualize how differently the extreme individuals would perform the task. All these different aspects of DHM discussed are incorporated in our native DHM developed named ’MAYAMANAV’. Finally this thesis will end with conclusions and future work discussing how these different aspects of DHM discussed can be combined with behavioral models to simulate the human error.

Simulating realistic human behavior on a virtual avatar presents a difficult task. Because the simulated environment does not adhere to the same scientific principles that we do in the existent world, the avatar becomes capable of achieving infeasible postures. In an attempt to obtain realistic human simulation, real world constraints are imposed onto the non-sentient being. One such constraint, and the topic of this thesis, is self-collision avoidance. For the purposes of this topic, a posture will be defined solely as a collection of angles formed by each joint on the avatar. The goal of self-collision avoidance is to eliminate the formation of any posture where multiple body parts are attempting to occupy the exact same space. My work necessitates an extension of this definition to also include collision avoidance with objects attached to the body, such as a backpack or armor. In order to prevent these collisions from occurring, I have implemented an effort-based approach for correcting afflicted postures. This technique specifically pertains to postures that are sequenced together with the objective of animating the avatar. As such, the animation's coherence and defining characteristics must be preserved. My approach to this problem is unique in that it strategically blends the concept of keyframe interpolation with an optimization-based strategy for posture prediction. Although there has been considerable work done with methods for keyframe interpolation, there has been minimal progress towards integrating a realistic collision response strategy. Additionally, I will test this optimization-based approach through the use of a complex kinematic human model and investigate the use of the results as input to an existing dynamic motion prediction system.

Introduction: External support behaviours, which include leaning (supporting with the non-task hand) or bracing (supporting with the body), are frequently employed by workers in manufacturing settings. However, current ergonomic assessment tools are limited by our limited understanding of these behaviours. Recent studies have investigated these behaviours, however, the designs of these studies are limited in their applicability to real-world scenarios. The purpose of this study was to assess how different task parameters affect the prediction of external support behaviours, as well as the effect of support on task hand, and body, kinematics and kinetics, in a minimally constrained experimental design. Methods: Female participants (n = 18) performed a series of one-handed maximal exertions (in the six orthogonal directions), and one precision task, in four hand Locations. Trials either featured support (as chosen by the participant), or no support. Results & Discussion: Three logistic regression models were developed, with inputs from individual and task characteristics, and they correctly predicted the occurrence of leaning, bracing, or simultaneous leaning and bracing, 74-86% of the time. Leaning and/or bracing were found to provide: 1) oppositional forces to increase task hand force generation, 2) balance, by countering destabilizing moments about the feet, and 3) a reduction in moment arm of the task hand force, with respect to the upper body joints, by bringing the shoulder closer to the task hand. Participants were able to exert 64.8% more force at the task hand as a result of support. Leaning hand placement depended on the task force direction and location. However, the positioning of the leaning hand varied very little. Finally, the precision condition showed that fine motor demands may also affect external support choice. / Thesis / Master of Science in Kinesiology