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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.

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.

Although biomechanics models can predict the stress on the musculoskeletal system, they cannot predict how the muscle load associated with exertion is perceived. The short-term goal of the present study was to model the perception of effort in lifting and reaching tasks. The long-term goal is to determine the correlation between objective and subjective measures of effort and use this information to predict fatigue or the risk of injury. Lifting and reaching tasks were performed in seated and standing situations. A cylindrical object and a box were moved with one hand and two hands, respectively, from a home location to shelves distributed in the space around the subject. The shoulder and torso effort required to perform these tasks were rated on a ten point visual analog scale.

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.

Vehicle ride motion produces a dynamic response of the seated operator, which disturbs the intended fingertip trajectory during reach activities. This perturbation induces deviations that must be corrected to successfully complete the reach. Visual and/or proprioceptive information are necessary to detect these deviations and provide feedback to the controller of the neuromuscular system. In an attempt to predict movement alterations and adjustments under whole body vibration exposure, a trajectory planning and feedback controller was developed using split sample data from a series of reaching experiments on a six degree of freedom motion platform.

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.

The evaluation of maintenance tasks is increasingly important in the design and redesign of many industrial operations including vehicles. The weight of subsystems can be extreme and often tools are developed to abate the ergonomic risks commonly associated with such tasks, while others are unfortunately overlooked. We evaluated a member of the family of medium-sized tactical vehicles (FMTV) and chose the battery handling from a list of previously addressed concerns regarding the vehicle. Particularly in larger vehicles, similar to those analyzed in this paper, batteries may exceed 35 kg (77 lbs). The motions required to remove these batteries were simulated using motion prediction modules from the Human Motion Simulation (HUMOSIM) laboratory at the University of Michigan. These motions were visualized in UGS PLM Solutions' Jack™ and analyzed with the embedded 3-D Static Strength Prediction program.

Posture selection during standing exertions is a complex process involving tradeoffs between muscle strength and balance. Bodyweight utilization reduces the amount of upper-body strength required to perform a high force push/pull exertion but shifts the center-of-gravity towards the limits of the functional stability region. Thus balance constraints limit the extent to which bodyweight can be used to generate push/pull forces. This paper examines a two-handed sagittal plane pulling exertion performed during a battery maintenance task on a member of the family of medium-sized tactical vehicles (FMTV). Percent capable strength predictions and functional balance capabilities were determined for various two-handed pulling postures using the University of Michigan's 3D Static Strength Prediction Program (3DSSPP). Through this simulation study, preferred postures that minimize joint torques while maintaining balance were identified.

In this study, an angle-time-based motion modification method was developed. This method allows the use of existing motion data by modifying them to fit new scenarios given as new initial and final posture constraints. The motion modification method can generalize an existing motion data and derive, within a portion of space, a family of motions retaining the angular velocity characteristics of the original motion. It was found that the proposed method is capable of predicting realistic human motions with various new initial and final posture constraints in a robust manner. We expect that this motion modification method provides a way of using existing motion data more flexibly and economically.