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Simulation of reach movements is an essential component for proactive ergonomic analysis in digital human modeling and for numerous applications in vehicle design. Most studies on reach kinematics described human movements in static conditions. Earlier studies of reach performance in vibration environments focused mainly on fingertip deviation without considering multi-body dynamics. However, for the proper assessment of reach performance under whole-body vibration exposure, a multi-body biodynamic model needs to be developed. This study analyzes three dimensional reach kinematics of the upper extremity during in-vehicle operations, using a multi-segmental model of the upper body in the vibratory environment. The goals are to identify the characteristics of upper body reach movements and to investigate vibration-induced changes in joint kinematics. Thirteen subjects reached to four target directions in the right hemisphere.

A research scheme and preliminary results of a pilot study concerning upper body coordination in reach movements is presented. Techniques for multi-joint arm movements were used to obtain the kinematics of each body segment in reach movements to targets spatially distributed in a horizontal plane. Further understanding of the control mechanisms associated with coordination is investigated by combining the information of gaze orientation and body segment movements during reach activities. The implicit sequence of body segments in reach movement can be derived from their kinematic characteristics. Moreover, an identification of phases composing a reach movement is attempted.

This study investigates the organization of upper body coordination in tasks involving complex visual and manual demands. In the past, bimanual coordination has been approached in the context of symmetric or asymmetric interactions of the two hands. But routine behavior associated with work tasks requires synchronization in time and space of multiple components across multiple concurrent actions. Hence the problem of upper body coordination involves a combination of both symmetric and asymmetric modes of interaction, with a dynamic switching between the two. Although current models may explain the two modes of interaction individually, none of the existing models account for an integration of the two modes from the perspective of task performance. A pilot study was conducted in which subjects performed assembly tasks involving object transfers and manipulations with varying levels of visual and manual demands and performance constraints, such as speed and precision.

Exposure of a driver to vehicle vibration is known to disrupt manual performances, and more specifically affect the speed and accuracy of reaching tasks associated with vehicle operation. The effects of whole body vibration (WBV) can be analyzed as a function of the vibration characteristics of each body link. This information can then be used to identify movement strategies and predict biodynamic responses. Conceptual principles derived from the understanding of human behavior in a vibratory environment can then be used for the design of controls or interfaces adapted for vehicle operation in this context. The transfer functions of individual upper body links were estimated to investigate their biodynamic properties as a function of vehicle vibration frequency and spatial location of targets to be reached. In the present study, fourteen seated participants performed pointing movements to eight targets distributed in the right hemisphere.

The potential of digital human modeling to improve the design of products and workspaces has been limited by the time-consuming manual manipulation of figures that is required to perform simulations. Moreover, the inaccuracies in posture and motion that result from manual procedures compromise the fidelity of the resulting analyses. This paper presents a new approach to the control of human figure models and the analysis of simulated tasks. The new methods are embodied in an algorithmic framework developed in the Human Motion Simulation (HUMOSIM) laboratory at the University of Michigan. The framework consists of an interconnected, hierarchical set of posture and motion modules that control aspects of human behavior, such as gaze or upper-extremity motion. Analysis modules, addressing issues such as shoulder stress and balance, are integrated into the framework.

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.

The organization of upper body and gaze movements was quantified as an attempt to identify the types of task descriptors associated with the visual and manual functions of movement control. Nine subjects were asked to either read a word (high visual demand), reach a target (low visual demand), or simultaneously read a word and reach the object target placed just below the word (high visual demand). Similarly the manual demand condition was either low or high, depending on the target distance from the shoulder (either 80 or 120% of extended arm length, respectively). Torso flexion and gaze-on-target duration showed that movements are influenced by the both visual and manual demands in an interactive manner. Also both torso posture and gaze movements were predominantly changed by the visual demand. These results suggest that tasks to be simulated should be described in terms of both visual and manual demand.

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.

Hand reach movements for manual work, vehicle operation, and manipulation of controls are planned and guided by visual images actively captured through eye and head movements. It is hypothesized that reach movements are based on the coordination of multiple subsystems that pursue the individual goals of visual gaze and manual reach. In the present study, shared control coordination was simulated in reach movements modeled using differential inverse kinematics. An 8-DOF model represented the torso-neck-head link (visual subsystem), and a 9-DOF model represented the torso-upper limb link (manual subsystem), respectively. Joint angles were predicted in the velocity domain via a pseudo-inverse Jacobian that weighted each link for its contribution to the movement. A secondary objective function was introduced to enable both subsystems to achieve the corresponding movement goals in a coordinated manner by manipulating redundant degrees of freedom.

Occupational populations have become increasingly diverse, requiring novel accommodation technologies for inclusive design. Hence, further attention is required to identify potential differences in work perception between workers with varying physical limitations. The major aim of this study was to identify differences in shoulder loading and perception of effort between a control population (C) and populations affected by chronic back pain (LBP) and spinal cord injury (SCI) in one-handed seated transfer tasks to targets. The effects of the injuries, and associated pain, are likely to produce variations in movement patterns, muscle loading and perceived effort.

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.

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).

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.

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.

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.