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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.

Vehicle motions can adversely affect the ability of a driver or occupant to quickly and accurately push control buttons located in many advanced vehicle control, navigation and communications systems. A pilot study was conducted using the U.S. Army Tank Automotive and Armaments Command (TACOM) Ride Motion Simulator (RMS) to assess the effects of vertical ride motion on the kinematics of reaching. The RMS was programmed to produce 0.5 g and 0.8 g peak-to-peak sinusoidal inputs at the seat-sitter interface over a range of frequencies. Two participants performed seated reaching tasks to locations typical of in-vehicle controls under static conditions and with single-frequency inputs between 0 and 10 Hz. The participants also held terminal reach postures during 0.5 to 32 Hz sine sweeps. Reach kinematics were recorded using a 10-camera VICON motion capture system. The effects of vertical ride motion on movement time, accuracy, and subjective responses were assessed.

A research effort was initiated to establish an empirical data base and to develop predictive models of normal human in-vehicle seated reaching motions while driving. A driving simulator was built, in which a variety of targets were positioned at typical locations a driver would possibly reach. Reaching motions towards these targets were performed by demographically representative subjects and measured by a state-of-the-art motion analysis system. This paper describes the experiment conducted to collect the movement data, and the new techniques that are being developed to process, analyze, and model the data. Some initial findings regarding the role of torso assistive motion, the effect of speed used in completing a motion on multi-segment dynamic postures, and illustrative results from kinematic modeling are presented.

Vehicle operators are required to perform a variety of reaching tasks while the vehicle is in motion. The vibration transmitted from the terrain-vehicle coupling can prevent the operator from successfully completing the required task. The level to which vibration inhibits the completion of these tasks must be more clearly understood in order to effectively design controls and displays that minimize these performance decrements. The Ride Motion Simulator (RMS) at the U.S. Army Tank-Automotive Research, Development, and Engineering Center (TARDEC) simulated single-axis and 6DOF ride motion, in which twelve participants were asked to perform push-button reaching tasks to eight RMS-mounted targets. In order to better ascertain the effects of dynamic ride motion on in-vehicle reaching tasks, we used a twelve-camera VICON optical motion capture system to record and UGS PLM Solutions’ Jack™ to analyze the associated kinematic and kinetic motions.

This book presents seven case studies in which digital human models were used to solve different types of physical problems associated with proposed human-machine interaction tasks. This book includes contributions from researchers at Ford, Boeing, DaimlerChrysler, General Motors, the U.S. Air Force, and others.