Computer-Aided Design 39 (2007) 548–558www.elsevier.com/locate/cad

A new digital human environment and assessment of vehicle interior design

Jingzhou Yanga,∗, Joo H. Kima, Karim Abdel-Maleka, Timothy Marlera, Steven Becka,Gregory R. Koppb

a Virtual Soldier Research Program, Center for Computer Aided Design, The University of Iowa, Iowa City, IA 52242, United Statesb Caterpillar Inc., Peoria, IL 61656, United States

Received 18 August 2006; accepted 28 November 2006

Abstract

Vehicle interior design directly relates to driver performance measures such as comfort, efficiency, risk of injury, and vehicle safety. A digitalhuman is a convenient tool for satisfying the need to reduce the design cycle in order to save time and money. This paper presents a digital humanenvironment, SantosTM, developed at The University of Iowa, and its assessment as applied to the interior design of a Caterpillar vehicle. Thedigital human environment involves male models and accommodates a large percentage of the operator population (from the 5th percentile tothe 95th percentile). It has a user-friendly interface and includes various tools such as posture prediction, reachability check, zone differentiation,and biomechanics assessment for the upper body and hand. The key difference from a traditional digital human environment is that Santos’senvironment is optimization-based. This can answer design questions regarding whether the operator can reach relevant controls, what the comfortlevel is if one can reach the control, and what strength is required of the operator to pull a shift, etc. The illustrative example of a Caterpillar cabis demonstrated using this digital human environment.c© 2006 Elsevier Ltd. All rights reserved.

Keywords: Digital human environment; Interior design; Human performance measures; Biomechanics

1. Introduction

Digital humans are increasingly being incorporated intoproduct design, worker environments, and driver–vehiclesystems. Current digital human models provide little feedbackfor human performance measures, and it is necessary to couplekinematics and dynamics in the same model. Therefore, inthis paper, we present a new digital human environmentwhere SantosTM has a real human-like appearance withdeformable skin, optimization-based posture, and dynamicmotion prediction capabilities. This environment can givedifferent types of feedback, such as whether Santos can reach itstarget, what the discomfort level is in such a case, and the jointactuator torques in all joints involved when SantosTM pulls alever. All this feedback is important for evaluating the interiordesign.

To date, most digital human development has concernedkinematic and dynamic functionality, with little attention being

∗ Corresponding author. Tel.: +1 319 353 2249.E-mail address: jyang@engineering.uiowa.edu (J. Yang).

0010-4485/$ - see front matter c© 2006 Elsevier Ltd. All rights reserved.doi:10.1016/j.cad.2006.11.007

paid to the appearance of the avatar. The digital humanpresented in this paper has human-like appearance.

Several commercial software programs such as Jack R©,RAMSIS R©, and Safework R© are available. In addition, manystudies have been conducted on different digital humanmodels such as SAMMIE [1], the Boeing Human ModelingSystem (BHMS), Anybody [2], Dhaiba [10], and Man3D[11]. However, all of the above-mentioned approaches ortools involve either experimental data or the user-manipulationof digital humans. To simulate operators engaged in themanipulation of the tools inside the vehicle the vehicle, bothkinematics and dynamics are involved. It is thus necessaryto have a task-based digital human environment that cancouple kinematics and dynamics aspects. In this paper, we firstintroduce the human representation, then describe the advancedinterface, then illustrate the assessment of a vehicle interiordesign example, and finally present a conclusion.

2. Digital human representation

The human body is arranged in series, where eachindependent anatomical structure is connected to another via

J. Yang et al. / Computer-Aided Design 39 (2007) 548–558 549

Fig. 1. Santos model.

a joint. Consider, for example, that there exists a globalcoordinate system located at the waist. From that coordinatesystem, one may be able to draw a branch by identifying a rigidlink, connected through a joint to another rigid link, connectedto another link and so on, until one reaches the hand. Here theterm ‘joint’ refers to a kinematic joint, instead of an anatomicalhuman joint. An anatomical joint may entail several kinematicjoints. For example, the anatomical shoulder joint includesthree revolute kinematic joints, while the anatomical knee jointhas only one revolute kinemtic joint. Each finger also comprisesa number of segmental links connected via joints. Similarly,starting from the waist, one may follow the connection toreach the head, the other hand, the left foot, and the rightfoot. We shall refer to one such chain as a branch. Therefore,the human body can be modeled as a kinematic system, aseries of links connected by rotational degrees of freedom(DOF) that collectively represent musculoskeletal joints suchas the wrist, elbow, vertebra, or shoulder. In order to betterrepresent human motion, a 109-DOF model for the human bodyhas been developed [13–15]. Fig. 1 illustrates Santos with thekinematic model (skeleton). In this model, the kinemtic jointsstarting from the waist to the right hand have 21 DOFs and arerepresented by q1, . . . , q21, respectively. The kinemtic jointsfrom left clavicle to the left hand have 9 DOFs and are describedby q22, . . . , q30. The kinematic joints for the neck has 5 DOFsand is denoted by q31, . . . , q35.

The hands of a digital human are among the most importantparts because they are a bridge between the digital human andthe virtual world. In the Santos model, we develop a 25-DOFhand model for one hand (i.e., two hands have total of 50

Fig. 2. Dorsal view right hand model.

DOFs). Fig. 2 only shows the right hand model. Each cylinderrepresents a revolute joint. The left hand model is similar to theright hand.

The Denavit–Hartenberg [3] method was created tosystematically represent the relation between two coordinatesystems, but was not used extensively until the early 1980s

550 J. Yang et al. / Computer-Aided Design 39 (2007) 548–558

Fig. 3. DH parameters.

with the appearance of the computational methods andhardware that enable the necessary calculations. The methodis currently used to a great extent in the analysis and controlof robotic manipulators. This method has also been successfulin addressing human motion, in particular steps towards abetter understanding of the mechanics of human motion. Themethod, now referred to as the DH method, is based uponcharacterizing the configuration of link i with respect to link(i − 1) by a (4 × 4) homogeneous transformation matrix. Ifeach pair of consecutive links represented by their associatedcoordinate system is related via a matrix, then using the matrixmultiplication, it is possible to relate any of the segmentallinks (e.g., the hand) with respect to any other segmental link(e.g., the shoulder).

For an n-DOF model, the position vector of a point ofinterest on the end-effector of a human articulated model (e.g., apoint on the thumb with respect to the global coordinate system)attached to the waist can be written in terms of joint variables as

x = x(q)

where q ∈ Rn is a vector of n-generalized coordinates, andx(q) can be obtained from the multiplication of the homoge-neous transformation matrices defined by the DH method as

0Tn =0 T1

1T2 . . . n−1Tn =

[0Rn(q) x(q)

0 1

]where i R j is the rotation matrix relating coordinate framesi and j . The vector function x(q) characterizes the set ofall points touched by the end-effector. The four parameters(θi , di , αi , ai ) are defined in Fig. 3 and the transformation ma-trix can be defined as

(1)

where qi = θi since all joints are revolute joints.

Table 1Reference anthropometry for male Santos

Avatar Stature (mm) Weight (kg)

Small operator (5%) 1550 55Medium operator (50%) 1715 76.5Large operator (95%) 1880 98

The avatar’s “skin” is comparable to an infinitely thin,but hollow shell that defines the avatar’s shape. To visuallysimulate the elasticity of human skin as the joints are exercised,the amount of movement in the area of skin around aparticular point must be defined when those points in the skinmove during joint rotation. This is done with a traditionalanimation technique called “skin weighting”, which addressesthe aesthetic issue.

To accommodate a wide range of the population, we definedifferent anthropometries for different-percentile operatorsbased on ISO 3411 [6]. Table 1 lists the basic data for three keymale percentiles: 5%, 50%, and 95%. Other body dimensionsare obtained using the scaling functions, which are based onmultivariate regression analyses of the anthropometric data.The corresponding avatars are shown in Fig. 4.

3. Santos advanced environment

In this section, we review some advanced tools for interiordesign, such as the user-friendly interface, posture predictionand zone differentiation, and biomechanics analysis.

3.1. Interactive interface

Santos has intuitive interfaces for users [15]. Theseinterfaces (1) provide a communication channel between thesoftware core and the visualization core, (2) integrate newresearch modules with current research modules; this is ofgreat importance when the output of one module is an inputto another module, and (3) ensure that the order of inputsand outputs are verified and correct. Sometimes the outputfrom one module may be modified by another, and thismodified version is required by a third module. The interfacesprovide the user with a set of interactive menus, buttons, pop-up lists, editable text windows, and selection boxes that areimmediately familiar. We have developed the interface withdifferent areas of functionality including posture prediction(Fig. 5), joint modifications based on anthropometric data(Fig. 6), link-length (skeletal dimensions) modifications basedon anthropometric data (Fig. 7), workspace evaluation (Fig. 8),dynamic motion prediction (Fig. 9), and hand link-length andjoint modifications (Figs. 10 and 11).

3.2. Posture prediction and zone differentiation

In Santos’s environment, the core code is an optimization-based approach. Posture prediction is one of the core codes.It states that given one or more locations of the end-effectors(fingertips), one can solve the inverse kinematic problem

J. Yang et al. / Computer-Aided Design 39 (2007) 548–558 551

Fig. 4. Different-percentile male Santos models. (a) 5th percentile (b) 50th percentile (c) 95th percentile.

Fig. 5. Posture prediction.

by optimization techniques. The formulation is described asfollows:Find: q (joint angles).To minimize: Human performance measures (objectivefunctions).

Fig. 6. Joint limit interface.

Subject to:

(1) The end-effectors can touch the corresponding targetpoints.

(2) Joint angles should be within the joint limits.

552 J. Yang et al. / Computer-Aided Design 39 (2007) 548–558

Fig. 7. Link-length modification.

Fig. 8. Workspace zone differentiation.

Fig. 9. Dynamic motion prediction.

The human performance measures can be joint displacement,musculoskeletal discomfort, vision, and potential energy. Thedetails are provided by Marler [8,9] and Yang et al. [12,15].

Fig. 10. Scalable link length.

Fig. 11. Scalable joint limit.

The methodology for workspace zone differentiation ismeant to predict the posture for a set of target points, whichare generated by sampling a large cube that surrounds theworkspace. The cube is sampled in three directions (x , y,and z) according to a resolution that is determined by theuser, and creates a set of 2D planes that look like slicesinside the box. Thus every plane consists of a set of targetpoints that are sampled inside it. The inverse kinematics (IK)problem is solved for each point. For every predicted posture,corresponding human performance measure (HPM) values arerecorded. Since not all the points in the cube are expected to bereachable, each point is checked to determine whether it lies inthe workspace (reachability check).

After the objective function values (discomfort, effort, andjoint displacement) for all the reachable points in the cube areobtained through the posture prediction capability, the humanperformance measure values are normalized according to theglobal maximum of all the values that are achieved during thecomputations. Based on the normalized values, each voxel isthen assigned a binary number (that lies between 00 and FF)that reflects a gray-level value. Hence, a grayscale picture canbe visualized for each plane. It is similar to finite element

J. Yang et al. / Computer-Aided Design 39 (2007) 548–558 553

Fig. 12. Flow chart of the optimization-based zone differentiation algorithm.

stress visualization. The difference is that here the different graylevels represent the discomfort levels. Therefore, the result is aset of pictures that reflect the zone-differentiated workspace ofeach slice inside the sampled cube created earlier. A 3D viewof the zone-differentiated workspace is achieved by assemblingthe resultant slices and visualizing them all at the same time.The flow chart of the optimization-based zone differentiationapproach is shown in Fig. 12.

3.3. Dynamic motion prediction

To predict the motion, required torques, and energyconsumption for lever-pulling, we use the formulationintroduced by Kim [7]. The equations of motion with generalexternal loads were derived (the open chain system shown inFig. 13), and are shown below. The implementation of theseequations of motion is the key for the physics-based or dynamichuman motion simulation.

τ = M(q)q + V(q, q)

+

∑i

JTi mi g +

∑k

JTk

[−Fk−Ck

]+ K(q − qN ). (2)

Each term of the above vector–matrix form of the equationsof motion can be briefly described as follows:

(1) τ = [τ1, τ2, . . . , τn]T is the joint actuator torque vector

(actuated by muscles).

(2) M(q) is a symmetric matrix that represents the mass andinertia effects on the motion. The (i, k) element of this matrix,

Fig. 13. Joint-link system with external loads.

Mik , is written as follows:

Mik(q) =

n∑j=max(i,k)

Tr

∂0T j (q)

∂qkI j

[∂0T j (q)

∂qi

]T

i, k = 1, 2, . . . , n

Ii =

−Ixx + Iyy + Izz

2−Ixy −Ixz mi xi

−IxyIxx − Iyy + Izz

2−Iyz mi yi

−Ixz −IyzIxx + Iyy − Izz

2mi zi

mi xi mi yi mi zi mi

where Tr is the trace of a matrix, I j is an extended inertiamatrix, mi is the mass of link i , (xi , yi , zi ) is the location of thecenter of mass of link i expressed in terms of i th local frame,and Ixx , . . . , Ixy, . . . are the moments and products of inertiaof link i with respect to the i th local frame. For simplicity, eachlink is approximated as a thin rod.(3) V(q, q) is the Coriolis and centrifugal vector, which isformulated as

Vi (q, q) =

n∑k=1

n∑m=1

n∑j=max(i,k,m)

Tr

∂2 0T j (q)

∂qk∂qmI j

×

[∂ 0T j (q)

∂qi

]T qk qm i, k, m = 1, 2, . . . , n.

(4) Jk is the Jacobian matrix of the point at krk (4 × 1) locationvector with respect to the kth local frame as

Jk(q) =

∂ 0Tk(q)

∂q1

krk . . .∂ 0Tk(q)

∂qi

krk . . .∂ 0Tk(q)

∂qk

krk

Z0(q) . . . Zi−1(q) . . . Zk−1(q)

6×k

.

(3)

Here, we take only the first three elements of the (4×1) vectors(∂ 0Tk(q)/∂qi )

krk . Zi−1 represents the (3 × 1) local z-axisvector of joint i , expressed in terms of the global coordinatesystem. For JT

i , it is defined by Eq. (3) with i ri , where i ri

554 J. Yang et al. / Computer-Aided Design 39 (2007) 548–558

denotes location vector of the center of mass for link i . g isthe (6 × 1) gravity vector.

(5) Fk =

[Fk

x Fky Fk

z

]T, Ck =

[Ck

x Cky Ck

z

]T, and they

represent the force and moment vectors applied to the pointat krk (4 × 1) location with respect to the kth local frame,respectively.

(6) The last term of the equations of motion is the restoringtorque vector τRestoring resulting from any change in the jointangle from the neutral position.

τRestoring= −K(q − qN ) (4)

where qN is the vector of neutral joint angle and K is a diagonalmatrix of joint stiffness.

K =

k1 0. . .

0 kn

n×n

. (5)

The human metabolic energy expenditure rate is approximatedas the sum of the (absolute) mechanical power, heat rate, andbasal metabolic rate (BMR). It is approximated as

EMetabolic ≈

n∑i=1

|τi (t)qi (t)| +

n∑i=1

him |τi (t)| + B. (6)

The total amount of human metabolic energy expenditure is thetime-integration of the energy rate from time t1 to t2:

EMetabolic ≈

∫ t2

t1

n∑i=1

|τi (t)qi (t)|dt

+

∫ t2

t1

n∑i=1

him |τi (t)|dt +

∫ t2

t1Bdt (7)

where him (i = 1, . . . , n) is the generalized coefficient of the

maintenance heat at joint i . The BMR is defined as the rate atwhich heat is produced by an individual in a resting state, andis derived in [5].

We use the SNOPT software package developed by Gillet al. [4] to run the optimization based on a sequential quadraticprogramming (SQP) method. This is based on our assumptionthat a human moves in such a way as to minimize certain humanperformance measures, subject to several constraints.

For motion prediction, joint profiles are defined as B-Splinecurves. The optimization procedure calculates the joint angleprofiles of every joint in the form of B-Spline curves fornatural human upper-body motion. The optimization problemfor motion prediction can be stated as follows:

• Find: Control points (Pi, j i = 1, . . . , m; j = 1, . . . , n).• To minimize: Metabolic energy (EMetabolic), where the

equations of motion is τ = M(q)q + V(q, q) +∑

JTi mi g +∑

JTk

[−Fk−Ck

]+ K(q − qN ).

• Subject to:– Joint limits (q L

i ≤ qi ≤ qUi i = 1, . . . , n).

Fig. 14. Contact forces and joint torques diagram.

– Actuator torque limits (τ Li ≤ τi ≤ τU

i i = 1, . . . , n).– Path constraints (‖x(q(t)) − p(t)‖ ≤ ε).

3.4. Hand biomechanics

The musculoskeletal and grasping model for the hand isshown in Fig. 14. The assumption for grasping is that thecontact point is at the middle point of each link. τ j representsthe joint torque vectors for finger j , where j = 1, . . . , 5, τ j

=

[τj

1 , τj

2 , . . . , τj

n j ]T, n j denotes the total degrees of freedom for

finger j , and F jk =

[j Fk

xj Fk

yj Fk

z 0 0 0]T

representsthe contact forces between the object and link k at the locationkrk with respect to the kth local coordinate system for finger j ,where k = 1, . . . , n j .

Since the hand grasping velocity and acceleration are small,we consider hand grasping as one static problem. Therefore,finger joint torques will be the general static torques. Therelationship between the joint torque vector and end-effectorforce vector F j

n j for finger j is then given by

τjn j = JT(−F j

n j ). (8)

The joint torque vector due to the external load F jk applied at

point krk of link k is

τjk = JT

k (−F jk )

where the augmented Jacobian matrix Jk correspondingto frame k is similar to Eq. (3). From the principle ofsuperposition, the total joint torques due to external loads areobtained as a sum of all joint torques, as follows:

τ j=

n j∑k=1

JTk (−F j

k ). (9)

4. Vehicle interior design evaluation example

A CAD model of the Caterpillar vehicle is shown in Fig. 15,where the interior has been designed. In this study, we want toevaluate several ergonomic aspects using the new digital humanenvironment, Santos, including posture prediction, reachabilitycheck, comfort level for buttons, dynamic motion prediction,and hand biomechanics.

J. Yang et al. / Computer-Aided Design 39 (2007) 548–558 555

Fig. 15. A Caterpillar vehicle.

Fig. 16. Posture prediction.

The first task is to check the posture for the controls. Fig. 16shows one of the examples for posture prediction using a 95th-percentile Santos model. During design, we would like to checka regular task for a driver: The driver holds the steering wheelusing his left hand while pushing a button on the panel with theindex finger of his right hand. Therefore, there are two targetpoints; one is the button on the panel for the right hand andthe other is the steering wheel for the left hand. The predictedjoint angles in radians for upper body are q1 = 0.08816, q2 =

0.234, q3 = −0.100646, q4 = −0.035195, q5 = 0.138655,q6 = −0.089757, q7 = −0.123196, q8 = −0.025754,q9 = −0.092642, q10 = −0.09279, q11 = −0.130051,q12 = −0.105991, q13 = −0.00013, q14 = −0.523492,q15 = 1.052729, q16 = 1.421713, q17 = −1.38092,q18 = −1.139158, q19 = −0.048067, q20 = 0.16366,q21 = −0.132037, q22 = 0.000131, q23 = −0.523294,q24 = 1.148771, q25 = 0.450997, q26 = −1.819085,q27 = −1.417784, q28 = 0.263092, q29 = 0.150728, q30 =

0.164638, q31 = 0.204098, q32 = 0.133143, q33 = −0.4931,q34 = 0.275783, and q35 = −0.370443. The musculoskeletaldiscomfort level is 0.0614. The musculoskeletal discomfort

Fig. 17. Workspace zone differentiation.

Fig. 18. Dynamic motion prediction.

function [8,12] incorporates three aspects: (1) the tendencyto move towards a generally comfortable position, (2) thetendency to avoid postures in which joint angles are pushedto their limits, and (3) the idea that people strive to reach orcontact a point using one set of body parts at a time. For abetter design, the musculoskeletal discomfort value should berelatively smaller.

Based on the posture prediction tool, the workspace zonedifferentiation is implemented and tested for this vehicle.Fig. 17 shows the workspace zone differentiation results whilethe driver puts his feet on the pedals and has the belt on. Theresults show that the points at the back of the driver are the onescausing the most discomfort. Visually, we can test whether thecontrols are inside the reach envelope, and the layout of thebuttons is based on the gray level (discomfort level) of the zonedifferentiation.

The driver’s dynamic motion of pulling the transmissionlever is predicted. Fig. 18 shows that the driver uses his left

556 J. Yang et al. / Computer-Aided Design 39 (2007) 548–558

fil

(a) Joint profiles for q1 to q6.

(c) Joint profiles for q13 to q17.

Fig. 19. Predicted joint angle pro

hand to hold the steering wheel and pulls the lever with hisright hand. We consider the motion of pulling the transmissionlever with constant load from a given initial position to a givenfinal position in two seconds. The initial position of the lever is(−30, 10, 60) (cm), and the final position of the lever is (−30,10, 20) (cm) in the global frame. The load required to pull thelever is given as 5 N. Using our optimization-based method, theresults are obtained in the same manner as those presented byKim [7]. Figs. 19–21 show the results of the predicted profilesof the joint variables, actuator torques, and metabolic rate. Notethat since the torso, right arm and the right hand are involvedin pulling the lever, we only report the joint angles and torquesfor joints q1, . . . , q21. The joint torques for the right hand arediscussed separately.

The total metabolic energy consumed for this task is242.98 J. This value represents the amount of effort requiredto perform this task.

While the driver pulls the lever, the contact forces betweenthe hand and the lever are generated by muscles in the hand. The

(b) Joint profiles for q7 to q12.

(d) Joint profiles for q18 to q21.

es for a 5 N lever-pulling motion.

predicted torques for the right hand are shown in Fig. 22. Thisresult is a metric to check whether the hand will get injured.

5. Conclusion and future work

A new digital human environment has been presented andimplemented to evaluate the design of a vehicle interior. Thisenvironment has demonstrated special features in terms of ahigh-degree-of-freedom human model, real-time, human-likeappearance, optimization-based algorithms, and kinematics andpredictive dynamics. It was shown that this environment cannot only give a real-time posture prediction with the necessaryfeedback, but can also can illustrate the workspace zonedifferentiation. It was also shown that it has the biomechanicsfeatures necessary to assess any potential injuries in the jointsrelated to specific tasks.

We have set up the groundwork for a complete digital humanenvironment that can be deployed in the design of vehicles.Future work will include extending the upper body postureand dynamic motion prediction presented here to the lower

J. Yang et al. / Computer-Aided Design 39 (2007) 548–558 557

e p

(a) Joint torques for q1 to q6.

(c) Joint torques for q13 to q17.

Fig. 20. Predicted joint actuator torqu

Fig. 21. Predicted metabolic rate profile for 5 N lever-pulling motion.

extremities to test the pedals and report the discomfort levelfor the legs.

Acknowledgements

This research was partly funded by the US Army TACOMproject: Digital Humans and Virtual Reality for Future CombatSystems (FCS) (Contract No.: DAAE07-03-D-L003) and bythe Caterpillar Inc. project: Digital Human Modeling and

(b) Joint torques for q7 to q12.

(d) Joint torques for q18 to q21.

rofiles for a 5 N lever-pulling motion.

Fig. 22. Finger joint torques.

Simulation for Safety and Serviceability. The authors wouldlike to thank other members of the VSR group, especially AnithMathai and Chris Murphy for preparing the simulation results.

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