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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).

This paper reports on the development and validation of an automated seat-dimension extraction system that can efficiently and reliably measure SAE J2732 (2008) seat dimensions from 3D seat scan data. The automated dimension-extraction process consists of four phases: (1) import 3D seat scan data along with seat reference information such as H-point location, back and cushion angles, (2) calculate centerline and lateral cross-section lines on the imported 3D seat scan data, (3) identify landmarks on the centerline and cross-section lines based on the SAE J2732 definitions, and (4) measure seat-dimensions using the identified landmarks. To validate the automated seat measurements, manually measured dimensions in a computer-aided-design (CAD) environment and automatically extracted ones in the current system were compared in terms of mean discrepancy and intra- and inter-observer standard deviations (SD).