Search Results

The potential of digital human modeling to improve the design of products and workspaces has been limited by the time-consuming manual manipulation of figures that is required to perform simulations. Moreover, the inaccuracies in posture and motion that result from manual procedures compromise the fidelity of the resulting analyses. This paper presents a new approach to the control of human figure models and the analysis of simulated tasks. The new methods are embodied in an algorithmic framework developed in the Human Motion Simulation (HUMOSIM) laboratory at the University of Michigan. The framework consists of an interconnected, hierarchical set of posture and motion modules that control aspects of human behavior, such as gaze or upper-extremity motion. Analysis modules, addressing issues such as shoulder stress and balance, are integrated into the framework.

Simulation of human motions in virtual environments is an essential component of human CAD (Computer-aided Design) systems. In our earlier SAE papers, we introduced a novel motion simulation approach termed Memory-based Motion Simulation (MBMS). MBMS utilizes existing motion databases and predicts novel motions by modifying existing ‘root’ motions through the use of the motion modification algorithm. MBMS overcomes some limitations of existing motion simulation models, as 1) it simulates different types of motions on a single, unified framework, 2) it simulates motions based on alternative movement techniques, and 3) like real humans, it can learn new movement skills continually over time. The current study evaluates the prediction accuracy of MBMS to prove its utility as a predictive tool for computer-aided ergonomics. A total of 627 whole-body one-handed load transfer motions predicted by the algorithm are compared with actual human motions obtained in a motion capture experiment.

Workstation design is one of the most essential components of proactive ergonomics, and digital human models have gained increasing popularity in the analysis and design of current and future workstations (Chaffin 2001). Using digital human technology, it is possible to simulate interactions between humans and current or planned workstations, and conduct quantitative ergonomic analyses based on realistic human postures and motions. Motion capture has served as the primary means by which to acquire and visualize human motions in a digital environment. However, motion capture only provides motions for a specific person performing specific tasks. Albeit useful, at best this allows for the analysis of current or mocked-up workstations only. The ability to subsequently modify these motions is required to efficiently evaluate alternative design possibilities and thus improve design layouts.

Posture and external loads such as hand forces have a dominant effect on ergonomic analysis outcomes. Yet, current digital human modeling tools used for proactive ergonomics analysis lack validated models for predicting postures for standing hand-force exertions. To address this need, the effects of hand magnitude and direction on whole-body posture for standing static hand-force exertion tasks were quantified in a motion-capture study of 19 men and women with widely varying body size. The objective of this work was to identify postural behaviors that might be incorporated into a posture-prediction algorithm for standing hand-force tasks. Analysis of one-handed exertions indicates that, when possible, people tend to align their bodies with the direction of force application, converting potential cross-body exertions into sagittal plane exertions. With respect to the hand-force plane, pelvis position is consistent with a postural objective of reducing rotational trunk torques.

Interference between physical objects in the workspace and the moving human body may cause serious problems, including errors in manual operation, physical damage and trauma from the collision, and increased biomechanical stresses due to movement reorganization for avoiding the obstacles. Therefore, a computer algorithm to detect possible collisions and simulate human motions to avoid obstacles will be an important tool for computer-aided ergonomics and optimization of system design in the early stage of a design process. In the present study, we present a method of modifying motions for obstacle avoidance when the object intrudes near the center of the planned motion. We take the motion modification approach, as we believe that for a certain class of obstacle avoidance problems, a person would modify a pre-planned motion that would result in a collision to a new one that is collision-free, as opposed to organizing a totally unique motion pattern.

Cyclical stepping (gait) has been studied extensively. Some of these results are reflected in the straight and curved path step-following algorithms in commercial digital human modeling (DHM) implementations. With the aid of these algorithms, DHM users define start, intermediate, and end path points and the software generates a walking-like motion along the path. Most of these algorithms have substantial limitations, among them that the figures exhibit “foot skate,” meaning that the kinematic constraint of foot contact with the ground is not respected. Turning is accomplished by pivoting the entire figure, rather than through realistic lower-extremity motions. The simulation of the non-cyclical stepping motions accompanying manual material handling pickup and delivery tasks requires manual manikin manipulation. This paper proposes a paradigm for the simulation of stepping behavior in digital human models based on a model of foot placements and motions.