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The existing models and algorithms for predicting human reachable workspaces do not allow users to specify important performer and task parameters, such as body weight and muscular strengths of reach performer and hand-held object's weight. This makes it difficult to consider individuals with unique physical characteristics (e.g., obesity, muscle strength deficiencies and injuries) and many common tasks involving hand-held objects during reach analyses. To address this, this study presents a novel, biomechanically based workspace generation algorithm. Given a set of input data specified in terms of body dimensions, joint ranges of motion, body joints muscular strengths, gender, body weight of a reach performer and a hand-held load weight, the algorithm generates the corresponding reachable workspace. The algorithm combines the existing human figure based modeling approach with empirically obtained biomechanical data and established biomechanical models and constraints.

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.

Where is my head? Knowing head orientation in space is necessary to estimate the extent of the visual field in tasks requiring visual feedback such as driving or manual materials handling. Visually guided tasks are generally dependent on head and eye movements for visual acquisition of the target, and head movements are of significant importance when target eccentricity from the neutral reference point is large. The aim of the present work was to investigate head orientation in space in hand pointing tasks and to model the head response. Standing subjects were required to direct their gaze at one of three targets, equally distributed (vertically) in the sagittal plane. The task was performed while standing a) with the arms next to the body, b) holding a load in a static condition, c) aiming at targets with a heavy or light load held in the hands. Movements of the head and the body segments were recorded by the motion capture systems.