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This paper proposes a method to make diagnostic/prognostic judgment about the health of a tire, in term of its wear, using existing on-board sensor signals. The approach focuses on using an estimate of the effective rolling radius (ERR) for individual tires as one of the main diagnostic/prognostic means and it determines if a tire has significant wear and how long it can be safely driven before tire rotation or tire replacement are required. The ERR is determined from the combination of wheel speed sensor (WSS), Global Positioning sensor (GPS), the other motion sensor signals, together with the radius kinematic model of a rolling tire. The ERR estimation fits the relevant signals to a linear model and utilizes the relationship revealed in the magic formula tire model. The ERR can then be related to multiple sources of uncertainties such as the tire inflation pressure, tire loading changes, and tire wear.

The Human Motion Simulation Framework (Framework) is a hierarchical set of algorithms for predicting and analyzing task-oriented human motion. The Framework was developed to improve the performance of commercial human modeling software by increasing the accuracy of predicted motions and the speed of generating simulations. This paper presents the addition of stair ascending and descending to the Transition Stepping and Timing (Transit) model, a component of the Framework that predicts gait and acyclic stepping.

The Human Motion Simulation Framework is a hierarchical set of algorithms for physical task simulation and analysis. The Framework is capable of simulating a wide range of tasks, including standing and seated reaches, walking and carrying objects, and vehicle ingress and egress. In this paper, model predictions for the terminal postures of standing object transfer tasks are compared to data from 20 subjects with a wide range of body dimensions. Whole body postures were recorded using optical motion capture for one-handed and two-handed object transfers to target destinations at three angles from straight ahead and three heights. The hand and foot locations from the data were input to the HUMOSIM Framework Reference Implementation (HFRI) in the Jack human modeling software. The whole-body postures predicted by the HFRI were compared to the measured postures using a set of measures selected for their importance to ergonomic analysis.

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.

Industrial tasks performed by standing workers are among those most commonly simulated using digital human models. Workers often walk, turn, and take acyclic steps as they perform these tasks. Current h uman modeling tools lack the capability to simulate these whole body motions accurately. Most models simulate walking by replaying joint angle trajectories corresponding to a general gait pattern. Turning is simulated poorly if at all, and violations of kinematic constraints between the feet and ground are common. Moreover, current models do not accurately predict foot placement with respect to loads and other hand targets, diminishing the utility of the associated ergonomic analyses. A new approach to simulating stepping and walking in task-oriented activities is proposed. Foot placements and motions are predicted from operator and task characteristics using empirical models derived from laboratory data and validated using field data from an auto assembly plant.

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.

Simulations of humans performing seated reaches require accurate descriptions of the movements of the body segments that make up the torso. Data to generate such simulations were obtained in a laboratory study using industrial, auto, and truck seats. Twelve men and women reached to push-button targets located throughout their right-hand reach envelopes as their movements were recorded using an electromagnetic tracking system. The data illustrate complex patterns of motion that depend on target location and shoulder range of motion. Pelvis motion contributes substantially to seated reach capability. On padded seats, the effective center of rotation of the pelvis is often within the seat cushion below the pelvis rather than at the hips. Lumbar spine motions differ markedly depending on the location of the target. A categorization of reach targets into four zones differentiated by torso kinematics is proposed.

A complete scheme for motion prediction based on motion capture data is presented. The scheme rests on three main components: a special posture representation, a diverse motion capture database and prediction method. Most prior motion prediction schemes have been based on posture representations based on well-known local or global angles. Difficulties have arisen when trying to satisfy constraints, such as placing a hand on a target or scaling the posture for a subject of different stature. Inverse kinematic methods based on such angles require optimization that become increasingly complex and computationally intensive for longer linkages. A different representation called stretch pivot coordinates is presented that avoids these difficulties. The representation allows for easy rescaling for stature and other linkage length variations and satisfaction of endpoint constraints, all without optimization allowing for rapid real time use.

Assessments of reach capability using human figure models are commonly performed by exercising each joint of a kinematic chain, terminating in the hand, through the associated ranges of motion. The result is a reach envelope determined entirely by the segment lengths, joint degrees of freedom, and joint ranges of motion. In this paper, the validity of this approach is assessed by comparing the reach envelopes obtained by this method to those obtained in a laboratory study of men and women. Figures were created in the Jack human modeling software to represent the kinematic linkages of participants in the laboratory study. Maximum reach was predicted using the software's kinematic reach-envelope generation methods and by interactive manipulation. Predictions were compared to maximum reach envelopes obtained experimentally. The findings indicate that several changes to the normal procedures for obtaining maximum reach envelopes for seated tasks are needed.

The reach capability of drivers is currently represented in vehicle design practice in two ways. The SAE Recommended Practice J287 presents maximum reach capability surfaces for selected percentiles of a generic driving population. Driver reach is also simulated using digital human figure models. In typical applications, a family of figure models that span a large range of the target driver population with respect to body dimensions is positioned within a digital mockup of the driver's workstation. The articulated segments of the figure model are exercised to simulate reaching motions and driver capabilities are calculated from the constraints of the kinematic model. Both of these current methods for representing driver reach are substantially limited. The J287 surfaces are not configurable for population characteristics, do not provide the user with the ability to adjust accommodation percentiles, and do not provide any guidance on the difficulty of reaches that are attainable.

For human motion studies, which are to be used for either dynamic biomechanical analyses or development of human motion simulation models, it is important to establish an empirical motion database derived from efficient measurement and well-standardized data processing methodologies. This paper describes the motion recording and data processing system developed for modeling seated reach motions at the University of Michigan's HUMOSIM Laboratory. Both electromagnetic (Flock of Birds) and optical (Qualysis) motion capture systems are being used simultaneously to record the motion data. Using both types of devices provides a robust means to record human motion, but each has different limitations and advantages. The amount of kinematic information (DOFs), external sources of noise, shadowing, off-line marker identification/tracking time, and setup cost are key differences.

Modeling normal human reach behavior is dependent on many factors. Anthropometry, age, gender, joint mobility and muscle strength are a few such factors related to the individual being modeled. Reach locations, seat configurations, and tool weights are a few other task factors that can affect dynamic reach postures. This paper describes how two different modeling approaches are being used in the University of Michigan Human Motion Simulation Laboratory to predict normal seated reaching motions. One type of model uses an inverse kinematic structure with an optimization procedure that minimizes the weighted sum of the instantaneous velocity of each body segment. The second model employs a new functional regression technique to fit polynomial equations to the angular displacements of each body segment. To develop and validate these models, 38 subjects of widely varying age and anthropometry were asked to perform reaching motions while seated in simulated vehicle or industrial workplace.

A research effort was initiated to establish an empirical data base and to develop predictive models of normal human in-vehicle seated reaching motions while driving. A driving simulator was built, in which a variety of targets were positioned at typical locations a driver would possibly reach. Reaching motions towards these targets were performed by demographically representative subjects and measured by a state-of-the-art motion analysis system. This paper describes the experiment conducted to collect the movement data, and the new techniques that are being developed to process, analyze, and model the data. Some initial findings regarding the role of torso assistive motion, the effect of speed used in completing a motion on multi-segment dynamic postures, and illustrative results from kinematic modeling are presented.