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SAE TECHNICALPAPER SERIES 2000-01-2180

Comparison of Methods for PredictingAutomobile Driver Posture

Matthew P. Reed, Miriam A. Manary,Carol A. C. Flannagan and Lawrence W. Schneider

University of Michigan Transportation Research Institute

Digital Human Modeling for Design and EngineeringConference and Exposition

Dearborn, MichiganJune 6-8, 2000

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2000-01-2180

Comparison of Methods for PredictingAutomobile Driver Posture

Matthew P. Reed, Miriam A. Manary,Carol A. C. Flannagan and Lawrence W. Schneider

University of Michigan Transportation Research Institute

Copyright © 2000 Society of Automotive Engineers, Inc.

ABSTRACT

Recent research in the ASPECT (Automotive Seat andPackage Evaluation and Comparison Tools) program hasled to the development of a new method for automobiledriver posture prediction, known as the Cascade Model.The Cascade Model uses a sequential series ofregression functions and inverse kinematics to predictautomobile occupant posture. This paper presents analternative method for driver posture prediction usingdata-guided kinematic optimization. The within-subjectconditional distributions of joint angles are used to inferthe internal cost functions that guide tradeoffs betweenjoints in adapting to different vehicle configurations. Thepredictions from the two models are compared to in-vehicle driving postures.

INTRODUCTION

The widespread use of human figure models to developand evaluate vehicle interiors has created a need foraccurate vehicle occupant posture prediction. Vision,reach, and other analyses performed with figure modelsin virtual vehicle mockups are limited by the accuracy ofthe manikin postures. A recent program to develop newtools for vehicle design (ASPECT) included themeasurement of vehicle occupant postures in hundredsof vehicle and seat conditions (1, 2). As part of thatprogram, a new approach to posture prediction forautomobile occupants was developed (3).

The Cascade Prediction Model (CPM) places the highestpriority on accurate prediction of hip and eye locations,two of the posture characteristics that are most importantfor vehicle interior assessment. Regression equationscreated from laboratory data and adjusted using in-vehicle data are applied to predict hip and eye locationsfrom occupant anthropometry, vehicle interiorconfiguration, and seat characteristics. An inverse-kinematics approach is used to fit torso and limbsegments to the calculated landmark locations within thekinematic constraints of the driving task. Reed et al. (3)demonstrated that the CPM accurately predicts driverpostures in vehicles.

An important earlier model to predict driving postureswas developed by Seidl (4). The Siedl approach used akinematic optimization guided by the distributions of jointangles observed in laboratory testing. As part of therecent ASPECT work, a conceptually similar approachwas explored as an alternative to the Cascade Model.The Optimization Prediction Model (OPM) identifies themost likely posture among the kinematically feasiblepostures based on the observed distribution of jointangles from laboratory experiments. This paper presentsthe development of the OPM and compares the results tothe Cascade Model. The development and performanceof the OPM provides insight into the posture-selectionbehavior of automobile drivers.

METHODS

DATA SOURCES – The CPM and OPM were developedusing the same data from a laboratory study of drivingposture (2, 3). An anthropometrically diverse group of 68men and women selected their preferred driving posturesin a vehicle mockup that was configured to represent awide range of vehicle interior conditions. External bodylandmark data recorded with a sonic digitizer were usedto calculate joint locations defining a three-dimensionalkinematic-linkage representation of the body (5). Theresulting lengths, positions, and orientations of thelinkage segments were used in the development of theposture prediction models.

GENERAL MODEL FORMULATION

VEHICLE GEOMETRY DEFINITIONS AND MODELINPUTS – Posture prediction is conducted in a vehiclepackage coordinate system, defined by severalcommonly used vehicle reference points. Completedefinitions of these points can be found in Society ofAutomotive Engineers Recommended Practice J1100and associated practices (6). The X axis in the packagecoordinate system runs positive rearward, the Y axispositive to the driver’s right, and the Z axis positive up.The origin is defined by a different point on each axis.The origin X coordinate is defined by the Ball of Foot

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(BOF) reference point, while the origin Z coordinate isdefined by the Accelerator Heel Point (AHP). In generalterms, vertical dimensions are measured from the floorand fore-aft dimensions are measured from a point onthe accelerator pedal. For the current analysis, the originY coordinate is the centerline of the driver seat. Figure 1illustrates these reference points on a sideview schematicof the driver’s station.

A number of vehicle package dimensions are used asinputs to the posture prediction models. Theseparameters have been varied systematically in testing orare those whose specification is necessary to sufficientlycharacterize the locations of components. The weighted,contoured H-point manikin (SAE J826) measures areference point on the seat known as the H-point (a hip-joint location estimate). When the seat is moved forwardand rearward along its adjustment track, the orientationof the path of the H-point relative to the horizontal definesthe seat track angle. The seating reference point (SgRP)is the H-point location that lies on the 95th-percentileselected seat position curve given by SAE J1517 (3).This curve is a second-order polynomial describing thehorizontal position of the 95th-percentile of the seatposition distribution as a function of seat height. Seatheight is defined by the vertical distance between theSgRP and the AHP, and is termed H30, following thedimension definitions in SAE J1100.

Figure 1. Vehicle package geometry. Expressions in parentheses are Society of Automotive Engineers nomenclature from SAE J1100 (3).

Seat cushion angle (L27) specifies the orientation of thelower part of the seat (seat pan) with respect tohorizontal, and is measured using the H-point manikinwith a procedure described in SAE J826. Seat cushionangle does not generally correspond to any measure ofthe unloaded centerline contour of the seat, but insteadrepresents the cushion orientation experienced by astandardized sitter. The steering wheel is characterizedby the coordinates of the center of the front surface of thewheel, the angle of the front surface of the wheel withrespect to vertical, and the diameter of the wheel. Thehorizontal distance from the center of the steering wheel

to BOF is a key package dimension and is denoted L6 inSAE J1100.

Table 1 lists vehicle geometry inputs to the postureprediction model in two categories: parameters thatsolely affect kinematic constraints imposed on themodels, and those that are variables in the predictiveequations. Only three variables are used in the postureprediction models: H30, L6, and L27. Notably, thevertical position of the steering wheel and the degree offorward vision restriction imposed by the instrumentpanel or vehicle cowl are not included. The verticalposition of the steering wheel is highly constrained invehicle design, because of the conflicting requirements ofsufficient leg space beneath the wheel and sufficientvision above the wheel. The leg depth of large driversand the eye height of small drivers tend to constrain thevertical steering wheel position to a small range relativeto the SgRP location. Restrictions on forward vision, inthe range that is reasonable for vehicle design, do nothave important effects on posture (7).

The driver’s characteristics are represented in the modelsusing four parameters: gender, stature, weight, andsitting height. Additional anthropometric data, such asarm or leg lengths, do not provide substantially betterprediction. Because stature, weight, and sitting heightare correlated in the data set, two transformations of thevariables were used as regressors. The ratio of sittingheight to stature (SH/S), a measure of body proportion,was used in lieu of sitting height, and the Body MassIndex (BMI), the ratio of mass (kg) to stature (m) squared,was used instead of mass. Each of these two ratiovariables is only moderately correlated with stature in thisdataset (r = –0.34 and 0.32 for SH/S and BMI,respectively). The predictive ability of regressions usingthese variables, assessed using the adjusted R2 value,was within 0.01 of the values obtained using sitting heightand mass directly, while reducing the problemsassociated with correlated regressors.

In the CPM, driver anthropometric characteristics areused as input to regression equations that calculate themost important postural degrees of freedom. The OPMuses anthropometry primary to scale the kinematic

Accelerator Heel Point (AHP)

Seating Reference Point (SgRP)

Steering Wheel Diameter

Steering Wheel Angle

Seat Track Angle

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Seat Cushion Angle (L27)

Ball of Foot Reference Point (BOF)Accelerator

Pedal

Package Origin (0,0)

Z

X

Seat Height (H30)

Steering Wheel to BOF X

(L6)

Table 1. Vehicle Geometry Inputs

Prediction Variables Kinematic Constraints

Seat Height (H30)

Steering-Wheel to Ball-of-Foot X: SWBOFX (L6)

Cushion Angle (L27)

Seating Reference Point (X,Y,Z)

Steering Wheel Center (X,Y,Z)

Steering Wheel Angle

Steering Wheel Diameter

Seat Track Angle

Center of Accelerator Pedal Y Coordinate (with respect to seat centerline)

3

linkage (i.e., figure model). Subject stature and sittingheight are used in one part of the algorithm to tune theoptimization (see below).

KINEMATIC MODEL – Driving posture is representedusing a kinematic linkage model of the human body. Thelinkage and its derivation from external body landmarkdata are described in detail elsewhere (3, 5). Figure 2shows the linkage and defines variables that are used inthe posture prediction models.

MODEL SIMPLIFICATIONS AND RESTRICTIONS –Several simplifying assumptions are made to reduce themodel complexity. Normal driving posture is consideredto be sagittally symmetric, with the posture of the left sideof the body mirroring the right. In the data collectionused to develop the models, subjects were asked tochoose a “normal, comfortable driving posture” with theirhands located at the 10-o’clock and 2-o’clock position onthe steering wheel. By observation, the only importantdeviations from sagittal symmetry occurred when leftlower-extremity postures did not match the right lower-extremity, which was constrained by the requirement ofoperating the accelerator pedal. Data from the rightupper and lower extremities were used exclusively fordeveloping the models, since the geometric taskconstraints imposed by the accelerator and brake pedalsoperate solely through the right lower-extremity (novehicles or laboratory mockups with foot-operatedclutches were used in this part of the study). The hand-position constraint in testing was imposed so that theelbow angle would be a reliable measure of the distancebetween the steering wheel and torso. The performanceof the models in predicting postures measured inconditions with free hand placement suggests that thisconstraint provides useful upper-extremity posture datawithout otherwise affecting posture (see Results). Tosimplify limb kinematics calculations, the hands areassumed continuous with the forearms. Foot posture isneglected in favor of direct prediction of ankle jointlocation. Prediction of foot and ankle positions is basedon data from Schneider et al. (8).

CASCADE PREDICTION MODEL

Figure 3 shows a schematic of the CPM, as presentedpreviously (3). The vehicle and seat geometry, along withthe subject anthropometry, are input to a series ofregression equations. The fore-aft hip location and hip-to-H-point offset are calculated independently, thencombined with the seat track geometry to predict hiplocation. Eye location is calculated relative to hiplocation, using independent regression equations forhorizontal and vertical position. The torso is then fitbetween the calculated hip and eye locations using data-guided inverse kinematics (3). Upper extremities are fitbetween the calculated hip/shoulder locations and thepedal/steering wheel positions, again using inversekinematics with heuristics developed from measuredposture data.

Figure 2. Definitions of kinematic linkage and posture measures (5). Angles referenced to horizontal or vertical are XZ (sagittal) plane angles. Angles between segments (elbow angle, knee angle, and ankle angle) are measured in the plane formed by the segments (included angles). Note: Neck angle is negative as shown. All other angles are positive as shown.

OPTIMIZATION PREDICTION MODEL

The optimization prediction model (OPM) uses acompletely different approach to posture prediction fromthe regression-based methods used with the CPM, andhas antecedents in a number of previous postureprediction schemes. Many researchers have proposedthat there are joint angles, various called comfort anglesor neutral postures, that result when the moments acrossthe joint are passively balanced (9-17). If there are

Head

Neck

Thorax

Abdomen

PelvisThigh

Leg

Forearm

Foot

Arm

Z

X

Upper Neck Joint (Atlanto-Occipital)

Lower Lumbar Joint (L5/S1)

Hip

Knee

Wrist

Ankle

Elbow

Upper Lumbar Joint (T12/L1)

Lower Neck Joint (C7/T1)

Shoulder

Center Eye Point

Knee Angle Pelvis Angle

Abdomen Angle

Thorax Angle

Neck AngleHead Angle

Elbow Angle

4

comfort costs associated with deviations from theseangles, then posture might be predicted by assuming thatpeople select postures that allow as many joints aspossible to be close to these neutral angles (13). Thereare three essential components to this approach. Theneutral angles, the cost functions associated withdeviations from the neutral posture, and the manner inwhich these costs are traded off or optimized must bedetermined.

The neutral angles have been identified in a number ofways, most notably by observing postures underwaterand in zero-g environments (16), and by assuming thatthe average postures observed over a wide range of taskconditions represent the preferred or neutral posture (13,15, 17). The cost functions and optimization procedure,which are interdependent, have generally beenparameterized a priori, using, for example, a minimizationof the deviations from the neutral angles (13).

Figure 3. Schematic of Cascade Prediction Model (CPM).

Gender Stature Weight Sitting Height

SgRP (XYZ) Steering Wheel Center (XYZ) Seat Track Angle SW Diameter SW Angle Accelerator Pedal Center (Y)

Grip Location (2- and 10-o’clock)

Seat Height SW-to-BOF X Seat Cushion Angle

Kinematic Constraints and Geometric Referents

Model Input VariablesAnthropometric Variables

Regression Predictions 1. Ankle Location 2. H-Point-to-Hip Offset 3. Hip X re BOF 4. Eye Location (X,Z)

Hip Location on Hip Travel Path

Torso Segment Fitting (Inverse Kinematics)

Upper Extremity Fitting (Inverse Kinematics)

Lower Extremity Fitting (Inverse Kinematics)

Whole-Body Driving Posture

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Figure 4. Schematic of Optimization Prediction Model (OPM).

More recently, Seidl (4) proposed a novel method ofsimulating the joint-angle comfort tradeoffs that arefrequently assumed to underlie posture selectionbehavior. The actual distributions of joint anglesmeasured over a range of task conditions (vehiclepackage geometries) are used to determine the joint costfunctions. A posture is selected within thosekinematically possible that simultaneously maximizes thelikelihood of each of the joint angles with respect to theobserved distributions. This procedure, developed for theRAMSIS software manikin, applies this method globallyto all joints in the model for each posture prediction.

The OPM uses a modified version of Seidl’s approach,illustrated schematically in Figure 4. To begin, thekinematic linkage is scaled and the ankle location, grip

location, and hip travel path are calculated as with theCPM. The OPM algorithm calculates the most likelyposture, based on the input data, that is consistent withthe specified kinematic constraints.

The kinematic optimization is conducted using the three-dimensional linkage depicted in side view in Figure 5.Intersegment motion in the torso is governed by thesame motion distribution parameter values used in theCPM (3, 7). Three angles are used in the optimizationprocess: elbow angle, knee angle, and torso angle. Theelbow and knee angles are the angles formed by theadjacent model segments at the respective joints (largerangles represent greater extension), and torso angle isthe XZ-plane angle of the vector from hip to shoulder withrespect to vertical.

Gender Stature Weight Sitting Height

SgRP (XYZ) Steering Wheel Center (XYZ) Seat Track Angle SW Diameter SW Angle Accelerator Pedal Center (Y)

Grip Location (2- and 10-o’clock)

Elbow Angle Knee Angle Torso Angle

Kinematic Constraints and Geometric Referents

Empircal Joint Angle Distributions

Anthropometric Variables

Regression Predictions 1. Ankle Location 2. H-Point-to-Hip Offset 3. Leg Splay 4. Arm Splay

Hip Travel Path

Torso Segment Fitting (Inverse Kinematics)

Whole-Body Driving Posture

Maximize Joint Probability of Elbow, Knee, and Torso Angles within Kinematic

Constraints

Optimization:

6

Figure 5. Posture variables used in OPM.

In the reference dataset, the mean values of knee angleand torso angle are not significantly related to theanthropometric variables, but mean elbow angle is afunction of stature and the ratio of sitting height to stature.The mean values and predictive equation, used todetermine the neutral values in the optimization, aregiven in Table 2. While Seidl used the pooled anglevalues from all subjects to model the distribution ofangles, a more direct interpretation of the relative sizes ofthe angle distributions can be obtained by firstsubtracting off each subject’s mean values. The spreadsof the resulting distributions reflect the average within-subject joint-angle tradeoffs. Shapiro-Wilk W-test valuesgiven in Table 2 indicate that, for each variable, thewithin-subject angle distribution is not significantlydifferent from normal.

In the within-subject analysis, the relative sizes of theangle distributions represent quantitatively the joint angletradeoffs used by the subjects in adjusting to a widerange of vehicle and seat geometries. Angle changes atthe elbow were largest, followed by knee angle, with onlysmall angle changes occurring in the torso.

The objective of the OPM is to select, from the posturesthat meet the kinematic constraints, the posture that ismost likely. This means choosing the vector of jointangles

Φ = {knee angle, elbow angle, torso angle} = {φ1, φ2, φ3} (1)

such that the joint (conditional) probability of Φ ismaximized. In the original approach developed by Seidl,the range of test conditions was restricted in a way whichreduced the correlation among the variables to the pointwhere they could be neglected. In that case, thecombined probability is simply the product of theprobabilities at the individual joints. However, in thebroader dataset used for the development of the OPM,there are potentially important correlations among thejoint angles, notably between the elbow and knee angles(r = -0.39). Therefore, the likelihood of a particular angleat one joint is dependent on the value of another joint. Tocompute the overall likelihood of a posture, it isnecessary to consider the conditional probability.

Using the marginal normality findings from Table 2, thethree individual joint angle distributions can beconsidered as a single multinormal distributioncharacterized by mean vector µµµµ and covariance matrix Σ.The probability density function for the random vector Y,where Y has multivariate normal distribution, is given by

where µµµµ is the mean vector, r is the dimension of Y (3, inthis case), Σ is the covariance matrix, |Σ | denotes thedeterminant of Σ, and Σ-1 denotes the inverse. For theknee, elbow and torso angles used in the OPM, the meanvalues are given by the expressions in Table 2 and thecovariance matrix is given in Table 3. The optimizationproblem, then, is to find the vector Y = Φ for which ƒ(µ, Σ)is a maximum.

Because of the kinematic constraints imposed by theankle location, grip location, hip travel path, and torsomotion distribution, the kinematic linkage has only twodegrees of freedom (neglecting arm and leg splay). If theknee angle and torso angle are given, the elbow anglecan be computed from the constraints. This reduces theoptimization problem to the search of a two-parameterspace, and the objective function (posture likelihood) canbe plotted as a surface for any particular vehicle-geometry/anthropometry combination, as shown inFigure 6. The single local maximum is also a global

Table 2. Angle Distribution Parameters for OPM

Angle

Between-Subject Mean

(degrees)

Within-Subject Standard Deviation (degrees)

Shapiro-Wilk Test for

Normality (W, p)*

Torso 23.8 2.8 0.994, 1.00Knee 118.0 7.9 0.986, 0.35Elbow -297.0 +

0.12 Stature + 406.6 SH/S†

11.6 0.987, 0.63

*p values less than 0.05 (or some other Type-I error level) would support a conclusion that the distribution is not normal. †Regression on stature (mm) and the ratio of sitting height to stature, R2 = 0.32, RMSE = 19 degrees.

Table 3. Covariance Matrix (Σ)

Angles Knee Elbow TorsoKnee 62.41 -35.73 5.08Elbow -35.73 134.56 -1.29Torso 5.08 -1.29 7.84

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maximum, so a gradient-based approach is adequate tocompute the posture. After determining the knee, torso,and elbow angles using the optimization algorithm, thetorso segments are fit to the calculated hip and shoulderlocations using the same inverse kinematics approachused with the CPM (3, 7).

Figure 6. Empirical posture likelihood (arbitrary units) as a function of knee angle and torso angle for midsize-male anthropometry in a mid-range vehicle package.

RESULTS

MODEL COMPARISONS – Figure 7 illustrates driverposture predictions from the CPM and OPM for fourdifferent body sizes in one typical passenger carconfiguration. The two models produce similar postures,with the greatest differences observed with small femaleanthropometry. In particular, the CPM predicts a morereclined torso posture for the small female than ispredicted by the OPM.

Figure 8 shows the effects of a 200-mm change in fore-aft steering wheel position at a midrange seat height withmidsize male anthropometry. There are small differencesbetween models in the predicted postures, but the effectsof the steering wheel position change are similar. Figure8 illustrates the tradeoff between torso recline and limbposture that drivers adopt in adjusting to differentsteering wheel positions. Table 2 showed that the within-subject standard deviation of knee angle across a widerange of vehicle configurations is about 2.8 times as largeas the standard deviation of torso angle. This indicatesthat drivers adapt to changes in fore-aft steering wheelposition primarily by changes in limb posture, while torsorecline changes only slightly.

Small Female

Midsize Female

Midsize Male

Large Male

Figure 7. Comparison of postures predicted by CPM (thin lines) and OPM (thick lines) for a range of body sizes. Refer to Figure 2 for kinematic linkage description.

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Figure 8. Illustration of effects of fore-aft steering wheel position on posture for midsize male for CPM (thin lines) and OPM (thick lines).

COMPARISON TO IN-VEHICLE DATA – The accuracyof both models for predicting driving posture wasassessed using data from another study of drivingposture. In this in-vehicle study, 60 men and 60 womenranging in stature from 1441 to 1952 mm drove fivevehicles over a 15-minute road route, adjusting the seattrack position and seatback angle to obtain a comfortabledriving posture. Each car was equipped with anautomatic transmission and was tested with the seattrack adjustment restricted to two-way (fore-aft) travel.After returning from the road route, the driver’s preferredposture was recorded using a FARO coordinate

measurement arm and procedures similar to those usedin the laboratory studies (2, 5). Table 4 lists some of thecharacteristics of the vehicles. The vehicles wereselected to represent a substantial part of the range ofthe interior geometry available in current passenger cars.

The CPM and OPM were exercised using the vehicleconfigurations and individual subject anthropometry. Theresulting eye position predictions were compared with theobserved eye positions to assess the model accuracy.Table 5 lists the means and standard deviations of theprediction errors by vehicle for each posture model.

The CPM predicted the mean eye location for the fivevehicles with considerable accuracy, as reportedpreviously (3). The predicted horizontal coordinate waswithin 10 mm in all cases, with average errors of 3.6 mm.Both the CPM and OPM tended to predict eye locationshigher than those measured. During the in-vehicle study,measurements of eye location taken before and after thedrive indicated that eye locations averaged 9 mm lowerafter the drive, a difference attributed to seatcompression. Applying this dynamic Z correction bringsthe average vertical prediction error for the CPM to about2 mm and the OPM to about –12 mm. The standarddeviation of the eye location errors were similar for thetwo models, but the overall range of average error acrossvehicles was larger for the OPM on the horizontal axis(22.3 mm compared with 8.7 mm for the CPM).

Table 4. Vehicle Characteristics*

Vehicle Seat Height (mm) SWtoBOFX (mm) Seat Cushion Angle (deg)Plymouth Voyager 326 504 14.0Chrysler LHS 250 597 17.7Dodge Avenger 189 577 16.6Jeep Grand Cherokee 298 607 11.3Plymouth Laser 194 550 11.3

* Some vehicles were modified from design intent..

Table 5. Comparison of Model Predictions vs. Observed Eye Locations in Vehicle Data:Mean Observed minus Predicted (Standard Deviation)

Vehicle EyeX (Obs - Pred) (mm) Eye Z (Obs - Pred) (mm)CPM OPM CPM OPM

Plymouth Voyager 0.7 (52.2) -1.2 (50.4) -4.8 (20.6) -15.2 (20.4)Chyrsler LHS 0.0 (46.5) 9.2 (46.3) -6.5 (18.1) -20.6 (18.3)Avenger 2.5 (47.5) 20.0 (47.1) -7.4 (18.8) -22.7 (19.7)Jeep Grand Cherokee 5.9 (49.6) 16.8 (50.3) -13.6 (18.9) -22.9 (18.9)Plymouth Laser 8.7 (46.2) 21.1 (45.0) -2.2 (17.3) -24.9 (17.9)Overall Mean 3.6 (48.4) 13.2 (47.8) -6.9 (18.7) -21.3 (19.0)Dynamic Z Correction -- -- 2.1 -12.3Overall Range 8.7 22.3 11.4 9.7

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DISCUSSION

The posture prediction method developed by Seidl (4)used observed posture selection behavior to infer therelative costs of deviation from average, preferred jointangles. The OPM presented in this paper refines theapproach by using a within-subject analysis and data-guided inverse kinematics to simplify the calculations.The resulting conditional joint angle distributions can beregarded as a measure of the within-subject tradeoffsbetween limb and torso posture changes when adaptingto different vehicle configurations. Drivers adapt tochanges in steering wheel position (relative to thepedals) primarily by changes in limb posture (elbow andknee angles) with only small changes in torso recline.

When developed using the same data, the CPM andOPM produce essentially equivalent predictions,although the CPM is slightly more accurate for predictingin-vehicle postures. It is likely that the OPM could berefined to replicate the accuracy of the CPM. Theprimary advantage of the CPM is that the predictions forimportant postural characteristics, such as hip and eyelocation, are written as closed-form, linear equations, thecoefficients of which quantify the relative effects ofstature, steering wheel position, and other inputs. Thisclosed-form approach allows the predictions to be usedindependent of any particular human figure model. Incontrast, the OPM must be implemented using some typeof computerized search algorithm, and is tied to aparticular kinematic linkage.

In spite of its limitations, the OPM provides someinteresting insights into posture-selection behavior.When confronted with a range of workstation geometries,people select postures that can be interpreted as anoptimization of an internal, unknown cost function.Provoking a wide range of postural responses by testingwith an large variety of workstation geometries will elicit arange of angles at each joint that imply the relative cost tothe worker of deviations from the average, preferredpostures. Looked at in this way, the experiments withautomobile driving postures suggest that changes intorso posture typically have greater internal cost thanchanges in limb posture.

One potential advantage of the OPM is that it mayprovide greater generalizability to novel tasks. The CPMcan adjust the predicted posture only in response tochanges in the specific input parameters, such as seatheight and steering wheel position. However, the OPMwill produce a different posture whenever the kinematicconstraints at the hands and feet are changed. This isactually a liability for prediction of normal drivingpostures, because driving postures are not significantlydifferent when a driver’s hands are constrained to grasp

the steering wheel at different locations (3). For othertasks, such as reaching and grasping shifters, brakelevers, or other controls, the OPM method providesgreater adaptability. Yet, the predictions are not likely tobe accurate unless movements of this type were includedin the input database. In general, the CPM approach willalways be more accurate for well-studied tasks, becauseit is not constrained by the limitations of a kinematicmodel.

Both of the models included here have limitations arisingfrom test data on which they were developed andvalidated. The models are applicable only to seats withfore-aft adjustment, although comparison to in-vehicledata collected with height- and angle- adjustable seatsshows that the CPM is similarly accurate under thoseconditions. In all conditions, drivers were free to select apreferred seatback angle. Fixed or imposed seatbackangles would result in torso postures substantiallydifferent from those predicted. Finally, fore-aft adjustablepedals that have recently been introduced in popularvehicle models provide an additional adjustment degreeof freedom that was not included in the development ofthese models. Addition study will be necessary todetermine the resulting effects on posture.

The accuracy of ergonomic analyses using computermanikins is strongly dependent on the accuracy of themanikin postures. The CPM and OPM compared in thispaper are two accurate ways of predicting normal drivingposture that may be used to facilitate vehicle interiorassessment.

ACKNOWLEDGMENTS

The research described in this paper was conducted inpart under the ASPECT program, an industry-sponsoredeffort to develop a new generation of vehicle and seatdesign tools. ASPECT participants are BMW, Chrysler,Ford, General Motors, Johnson Controls, Lear, MagnaInterior Systems, PSA-Peugeot-Citroen, Toyota,Volkswagen, and Volvo. The American AutomobileManufacturers Association also supported part of theresearch through grants to UMTRI to support thedevelopment of new industry-standard packagingmodels.

Many people at UMTRI contributed to this research.Special thanks go to Ron Roe for applications wisdom, toCathy Harden and Beth Eby for accurate data collection,and to Stewart Simonett, Brian Eby, and Jim Whitley forfabricating and maintaining the test facilities. Theauthors also thank Andreas Seidl and Hartmut Speyer ofTecMath, Inc. for their insights regarding postureprediction.

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REFERENCES

1. Schneider, L.W., Reed, M.P., Roe, R.W., Manary,M.A., Hubbard, R.P., Flannagan, C.A.C., and Rupp(1999). ASPECT: The next-generation H-pointmachine and related vehicle and seat design andmeasurement tools. Technical Paper 990962.Warrendale, PA: Society of Automotive Engineers,Inc.

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