Vision Performance Measures for Optimization-Based Posture Prediction

Abstract

Although much work has been completed with modeling head-neck movements as well with studying the intricacies of vision and eye movements, relatively little research has been conducted involving how vision affects human upper-body posture. By leveraging direct human optimized posture prediction (D-HOPP), we are able to predict postures that incorporate one's tendency to actually look towards a workspace or see a target. D-HOPP is an optimization-based approach that functions in real time with Santos TM , a new kind of virtual human with a high number of degrees-of-freedom and a highly realistic appearance. With this approach, human performance measures provide objective functions in an optimization problem that is solved just once for a given posture or task. We have developed two new performance measures: visual acuity and visual displacement. Although the visual-acuity performance measure is based on well-accepted published concepts, we find that it has little effect on the predicted posture when a target point is outside one's field of view. Consequently, we have developed visual displacement, which corrects this problem. In general, we find that vision alone does not govern posture. However, using multi-objective optimization, we combine visual acuity and visual displacement with other performance measures, to yield realistic and validated predicted human postures that incorporate vision.

Studying Visibility as a Constraint and as an Objective for Posture Prediction

“…It is evident that using visual displacement for SOO is not advisable. This is in agreement with the results of Marler et al (2006) and suggests that vision alone does not govern human posture.…”

“…This study involved subjective evaluation of postures, but more objective analyses will be conducted in the future. Finally, as discussed in Marler et al (2006), with the development of the vision performance measure used in this work, only one element of vision is considered, albeit the most critical element with respect to posture prediction.…”

“…The vision performance measures are reviewed extensively by Marler et al (2006). Although many studies concerning the neck and vision are experimental, some work has been completed with modeling.…”

“…A thorough explanation of the vision performance measures is provided by Marler et al (2006). In general, although visual acuity depends on a variety of elements, most relevant to posture is its inverse dependence on the angular distance of a target from the eye fovea, with a value of zero when outside one's peripheral vision.…”

Development of a Zone Differentiation Tool for Visualization of Postural Comfort

“…This model involves three factors: (1) the tendency to gravitate to a reasonably comfortable position, (2) the tendency to move body segments in sequence (i.e., move the arm, then the torso if necessary, and then the clavicle), and (3) the tendency to avoid postures where ligaments and/or tendons are stretched. In this work, we use multiobjective optimization (MOO) to combine the musculoskeletal discomfort (Marler et al 2005) with a vision performance measure (Marler et al 2006) and an energy performance measure (Yang et al, 2004b; in press) to yield a general indication of comfort. Based on above different human performance measures we developed a new mathematical feeling model for discomfort, and we refer to this as the MOO function.…”