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Head movements contribute to the acquisition of targets in visually guided tasks such as reaching and grasping. It has been found that head orientation is generally related to the spatial location of the visual target. The movements of the head in a three-dimensional space are described using six degrees of freedom including translations along x-, y- and z-axis plus rotations about x-, y- and z-axis. While the control of head movement is heavily dependent upon visual perception, head movements lead to a change in the visual perception of the task space as well. In the present study we analyzed head movements in a set of driving simulation experiments. Also a theoretical reconstruction of the perceived task space after head movements was modeled by a statistical regression. This process included the transformation of the task space from a global reference frame (earth-fixed) into a perceived space in a head-centered reference frame (head-fixed).

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.

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.

The relationship between motion and posture was investigated from the kinematics of unconstrained head movements. Head movements for visual gazing exhibited an initial component whose amplitude does not exceed 20.3° for target eccentricity up to 120°. This component was truncated by subsequent corrective movements whose occurrence generally increases with target eccentricity, although with a large variability (R2 ≤ 0.46). The head is finally stabilized at 72% of target eccentricity (R2 ≥ 0.92). These results indicate that the final head posture can be achieved through a number of loosely-programmed kinematic variations. Based on these results, unconstrained head movements were simulated, within the context of application to posture prediction for estimation of the visual field.

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.