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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.

A new approach to driver seat-position modeling is presented. The equations of the Seating Accommodation Model (SAM) separately predict parameters of the distributions of male and female fore/aft seat position in a given vehicle. These distributions are used together to predict specific percentiles of the combined male-and-female seat-position distribution. The effects of vehicle parameters-seat height, steering-wheel-to-accelerator pedal distance, seat-cushion angle, and transmission type-are reflected in the prediction of mean seat position. The mean and standard deviation of driver population stature are included in the prediction for the mean and standard deviation of the seat-position distribution, respectively. SAM represents a new, more flexible approach to predicting fore/aft seat-position distributions for any driver population in passenger vehicles. Model performance is good, even at percentiles in the tails of the distribution.

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.

Most current applications of digital human figure models for ergonomic assessments of manual tasks focus on the analysis of a static posture. Tools available for static analysis include joint-specific strength, calculation of joint moments, balance maintenance capability, and low-back compression or shear force estimates. Yet, for many tasks, the inertial loads due to acceleration of body segments or external objects may contribute significantly to internal body forces and tissue stresses. Due to the complexity of incorporating the dynamics of motion into analysis, most commercial software packages used for ergonomic assessment do not have the capacity to include dynamic effects. Thus, commercial human modeling packages rarely provide an opportunity for the user to determine if a static analysis is sufficient.

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.

In many task analyses using digital human figure models, only the terminal or apparently most stressful posture is analyzed. For reaches from a seated position, this is generally the posture with the hand or hands at the target. However, depending on the characteristics of the tasks and the people performing them, analyzing only the terminal posture could be misleading. This possibility was examined using data from a study of the reaching behavior of people with spinal cord injury. Participants performed two-handed forward reaching tasks. These reaches were to three targets located in the sagittal plane. The terminal postures did not differ significantly between those with spinal cord injury and those without. However, motion analysis demonstrated that they employed distinct strategies, particularly in the initial phase of motion.

A major limitation of ergonomic analyses with current digital human models (DHM) is the speed and accuracy with which they can simulate worker postures and motions. Ergonomic analysis capabilities of DHM would be significantly improved with the addition of a fast, deterministic, accurate movement simulation model for the upper extremities. This paper describes the development of an important component of such a model. Motion data from twelve men and women performing one-handed, push-button reaches in a heavy truck seat were analyzed to determine patterns of motion of the clavicle relative to the thorax. Target direction and reach distance were good predictors clavicle segment motion, particularly for fore-aft clavlcle motion.

Reliable, accurate data on vehicle occupant characteristics could be used to personalize the occupant experience, potentially improving both satisfaction and safety. Recent improvements in 3D camera technology and increased use of cameras in vehicles offer the capability to effectively capture data on vehicle occupant characteristics, including size, shape, posture, and position. In previous work, the body dimensions of standing individuals were reliably estimated by fitting a statistical body shape model (SBSM) to data from a consumer-grade depth camera (Microsoft Kinect). In the current study, the methodology was extended to consider seated vehicle occupants. The SBSM used in this work was developed using laser scan data gathered from 147 children with stature ranging from 100 to 160 cm and BMI from 12 to 27 kg/m2 in various sitting postures.

Little information is available on passenger preferences for posture and support in highly reclined seat configurations. To address this gap, a laboratory study was conducted with 24 adult passengers at seat back angles from 23 to 53 degrees. Passenger preferences for head and neck posture with and without head support were recorded. This paper presents the characteristics of the passengers’ preferred head support with respect to thorax, head, and neck posture.

This study evaluated the ISO 5353 Seat Index Point Tool (SIPT) as an alternative to the SAE J826 H-point manikin for measuring military seats. A tool was fabricated based on the ISO specification and a custom back-angle measurement probe was designed and fitted to the SIPT. Comparisons between the two tools in a wide range of seating conditions showed that the mean SIP location was 5 mm aft of the H-point, with a standard deviation of 7.8 mm. Vertical location was not significantly different between the two tools (mean - 0.7 mm, sd 4.0 mm). A high correlation (r=0.9) was observed between the back angle measurements from the two tools. The SIPT was slightly more repeatable across installations and installers than the J826 manikin, with most of the discrepancy arising from situations with flat seat cushion angles and either unusually upright or reclined back angles that caused the J826 manikin to be unstable.

Analyzing dynamic postures of vehicle occupants in various situations is valuable for improving occupant accommodation and safety. Accurate tracking of an occupant’s head is of particular importance because the head has a large range of motion, controls gaze, and may require special protection in dynamic events including crashes. Previous vehicle occupant posture studies have primarily used marker-based optical motion capture systems or multiple video cameras for tracking facial features or markers on the head. However, the former approach has limitations for collecting on-road data, and the latter is limited by requiring intensive manual postprocessing to obtain suitable accuracy. This paper presents an automated on-road head tracking method using a single Microsoft Kinect V2 sensor, which uses a time-of-flight measurement principle to obtain a 3D point cloud representing objects in the scene at approximately 30 Hz.

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.

For many industrial tasks (push, pull, lift, carry, etc.), restrictions on grip locations and visibility constrain the hand and head positions and help to define feasible postures. In contrast, foot locations are often minimally constrained and an ergonomics analyst can choose several different stances in selecting a posture to analyze. Also, because stance can be a critical determinant of a biomechanical assessment of the work posture, the lack of a valid method for placing the feet of a manikin with respect to the task compromises the accuracy of the analysis. To address this issue, foot locations and orientations were captured in a laboratory study of sagittal plane and asymmetric manual load transfers. A pilot study with four volunteers of varying anthropometry approached a load located on one of three shelves and transferred the load to one of six shelves.

Current finite element (FE) human body models (HBMs) generally only represent young and mid-size male occupants and do not account for body shape and composition variations among the population. Because it generally takes several years to build a whole-body HBM, a method to rapidly develop HBMs with a wide range of human attributes (size, age, obesity level, etc.) is critically needed. Therefore, the objective of this study was to evaluate the feasibility of using a mesh morphing method to rapidly generate skeleton and whole-body HBMs based on statistical geometry targets developed previously. THUMS V4.01 mid-size male model jointly developed by Toyota Motor Corporation and Toyota Central R&D Labs was used in this study as the baseline HBM to be morphed. Radial basis function (RBF) was used to morph the baseline model into the target geometries.

Motion sickness in road vehicles may become an increasingly important problem as automation transforms drivers into passengers. The University of Michigan Transportation Research Institute has developed a vehicle-based platform to study motion sickness in passenger vehicles. A test-track study was conducted with 52 participants who reported susceptibility to motion sickness. The participants completed in-vehicle testing on a 20-minute scripted, continuous drive that consisted of a series of frequent 90-degree turns, braking, and lane changes at the U-M Mcity facility. In addition to quantifying their level of motion sickness on a numerical scale, participants were asked to describe in words any motion-sickness-related sensations they experienced.

Automotive seats are commonly described by one-dimensional measurements, including those documented in SAE J2732. However, 1-D measurements provide minimal information on seat shape. The goal of this work was to develop a statistical framework to analyze and model the surface shapes of seats by using techniques similar to those that have been used for modeling human body shapes. The 3-D contour of twelve driver seats of a pickup truck and sedans were scanned and aligned, and 408 landmarks were identified using a semi-automatic process. A template mesh of 18,306 vertices was morphed to match the scan at the landmark positions, and the remaining nodes were automatically adjusted to match the scanned surface. A principal component (PC) analysis was performed on the resulting homologous meshes. Each seat was uniquely represented by a set of PC scores; 10 PC scores explained 95% of the total variance. This new shape description has many applications.

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.

Digital human models (DHM) are now widely used to assess worker tasks as part of manufacturing simulation. With current DHM software, the simulation engineer or ergonomist usually makes a manual estimate of the likely worker standing location with respect to the work task. In a small number of cases, the worker standing location is determined through physical testing with one or a few workers. Motion capture technology is sometimes used to aid in quantitative analysis of the resulting posture. Previous research has demonstrated the sensitivity of work task assessment using DHM to the accuracy of the posture prediction. This paper expands on that work by demonstrating the need for a method and model to accurately predict worker standing location. The effect of standing location on work task posture and the resulting assessment is documented through three case studies using the Siemens Jack DHM software.

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.