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International Journal of Industrial Ergonomics 33 (2004) 265–278

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Modeling spatial interaction through full-scale modeling

Edward Steinfeld

School of Architecture and Planning, RERC on Universal Design at Buffalo, University at Buffalo, State University of New York,

Buffalo, NY 14214-3079, USA

Received 22 December 2002; received in revised form 15 June 2003; accepted 22 June 2003

Abstract

As conventionally practiced, anthropometry leaves some significant gaps in our knowledge about how people with

disabilities interact with their environment. In particular, most environmental design for this group focuses on specific

and unique body movements and postures in and around constrained spaces. The data provided by conventional

anthropometry cannot always be applied directly to these problems. Structural measurements are not sufficient to

understand how the body moves in space. And even functional measurements, like reach envelope data, do not provide

information on the adaptations people make when interacting with space or their psychological response to the level of

adaptation required. Studying spatial interaction using full-scale models can be used to measure functional abilities in

context and obtain data on outcomes, i.e. measures of successful fit between environment and a person’s abilities.

A full-scale modeling study of 24 adult females with mobility impairments is described to demonstrate how the

approach can be used to supplement traditional anthropometric studies to help improve the fit between person and

environment.

Relevance to industry

Understanding how people with disabilities interact with their environment can be used to develop more realistic

human models to evaluate the design of occupational, public and living settings.

r 2003 Elsevier B.V. All rights reserved.

Keywords: Anthropometry; Disability; Full-scale modeling; Accessibility; Environmental design

1. Introduction

Anthropometric research methods focus onmeasuring body dimensions (structural measure-ments) and general human performance abilities(functional measurements). Data are then orga-nized for use in design in the form of tables,diagrams, templates, manikins and jigs. Recently,parameterized computer simulations have been

ss: [email protected] (E. Steinfeld).

front matter r 2003 Elsevier B.V. All rights reserve

on.2003.06.004

developed that allow designers and researchers totest the anthropometric fit of a design in virtualspace using digital manikins that can be adjustedalong various parameters. Although such mani-kins have been available for the able bodiedpopulation for a long time, accurate and reliablemanikins of people with disabilities are still notavailable. Before reliable virtual applications canbe implemented, we need more data on structuraland functional abilities, but we also need data onthe outcomes of interaction with the environment,

d.

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E. Steinfeld / International Journal of Industrial Ergonomics 33 (2004) 265–278266

e.g. degree of independence a space with specificparameters supports.Conventional anthropometry alone cannot pro-

vide all this information. Although the dimensionsand general abilities of a sample of users may beknown, knowledge about how people choose touse the environment in specific situations and howthe environment constrains or shapes their beha-vior in ways that would modify the application ofanthropometric data requires another source ofinformation. Not only are physical factors criticalin determining this interactive effect, but also,psychological and social variables come into play,including motivation, perception, cognition, socialnorms, self-concept and social interaction dis-tances (Danford and Steinfeld, 1999).Anthropometric data of the conventional sort

has an inherent limitation when used in design fordisability that no amount of data will overcome.Such data provides generalized information onpeople, not information specific to a particularproduct or environment under development. Full-scale modeling is a valuable foundation fordeveloping valid and reliable computer models.Even though manikins can be manipulated withinfull-scale models or virtual spaces, we still can onlyimagine how real people with disabilities wouldactually behave unless we have data on how theyinteract with environments. The shape of physicalobjects and spaces is as diverse as the abilities ofpeople with disabilities. Thus, actual use ofproducts and spaces may vary significantly fromwhat we assume. Furthermore, spaces used ineveryday life are dynamic and change as peopleinteract with them in the course of their activities.Full-scale architectural modeling provides designspecific information that can inform computermodeling. Although it cannot substitute for largedatabases, it can be very useful as a complement toconventional anthropometry by providing anunderstanding of the interaction between bodysize, abilities, environmental context and out-comes.The purpose of this paper is to present an

argument for use of full-scale modeling in anthro-pometric research. A case study and otherexamples are used to demonstrate how this canbe accomplished.

2. Use of full-scale modeling in design research

2.1. Past research

In full-scale modeling, people demonstrate theuse of an environment or product under condi-tions similar to naturalistic situations. The impactof spatial configurations, space sizes and thelocation of objects in a space can be studied bycomparing different environments, or throughsystematic variation of parameters in a model, atechnique called ‘‘fitting trials’’ (Jones, 1969).Perhaps the most extensive use of this methodhas been in the design of vehicle interiors. Usingadjustable jigs, researchers have been able todevelop a complex understanding of humanfactors issues and make detailed design recom-mendations (Roe, 1993). Product designers alsogenerally mock up their prototypes and test themout prior to final design. Sophisticated instrumen-ted jigs are now used to rapidly collect detaileddata on human response for testing and design ofvehicle interiors and control systems. At thearchitectural scale, however, the implementationof full-scale modeling methods has been lessadvanced (see, for example, Dalholm, 1991),reflecting both the lack of interest and lack offunding from sponsors for such research.Architectural accessibility is concerned with

making buildings and facilities accessible forpeople with disabilities. Full-scale modeling wasused to study the anthropometrics of accessibilityas early as the late 1960s. A series of early studiesin the late 1960s and early 1970s used fitting trialsto study wheelchair maneuvers (Brattgard et al.,1974; Walter, 1971; Floyd, 1966). Moveable wallswere assembled and participants turned wheel-chairs in a systematic manner (e.g. straight line,360� turn, 90� turn, turn into doorways) while thewalls were adjusted until an individual could justmake the turn within the resulting space withouthitting the walls.Based on this early work, Steinfeld et al. (1979)

completed a study to identify accessible designcriteria for 14 basic building elements. In thisproject, protocols were developed for full-scalemodeling of wheelchair maneuvers, pick and placetasks at kitchens, comfortable work surface

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E. Steinfeld / International Journal of Industrial Ergonomics 33 (2004) 265–278 267

heights in kitchens, use of bathroom plumbingfixtures, toilet stalls and doorways. The protocolswere developed to avoid problems with learningeffects and other issues of research quality andthey were also documented more fully thanprevious reported research. This study produceda database that became the foundation for theAmerican National Standards Institute standard(ANSI A117.1 now ICC/ANSI A117.1) on makingbuildings and facilities accessible to and usable forpeople with disabilities.Additional studies were completed in following

years by Bails and Seeger (1988), Sanford andMegrew (1999), Steinfeld (1990), Danford andSteinfeld (1999) and Mullick (1999). Many of thesestudies incorporated user preferences and someincluded psychophysical and observer ratings ofperformance (Danford and Steinfeld, 1999;Mullick, 1999; Sanford and Megrew, 1999).Several types of experimental tasks can be

implemented easily with full-scale modeling. Theyinclude fitting trials to establish comfort zones,reach to target tasks to identify reach envelopeswithin specific contexts, pick and place tasks toinvestigate how different task requirements affectperformance and comparisons of different designs.In design participation experiments, participantsvoice their choices about how spaces should beorganized and a record of their decisions becomesthe database of the research (see for example,Steinfeld, 1990).

2.2. Measurement issues

There are some important external validityconcerns in this type of research. It is importantthat preliminary field research, using ethnographicor survey research methods, be used to insure thatprotocols are representative of the activities foundin the real world and incorporate all the issues ofconcern to the user population. For example, inthe use of a bathroom, the location where towels,soap and other hygiene products are stored canmake a significant difference in the safety andusability of a space. Thus, it is important touncover what products people store in the bath-room and the preferred locations of storage beforeundertaking full-scale modeling research.

There are also reliability issues that have to beaddressed. In fitting trials, identification of com-fort ranges rather than optimal conditions orminimal thresholds is preferable because it helps toidentify the parameters of flexible and adaptableenvironments. Ranges also are useful in establish-ing standards; they provide more flexibility fordesign decisions that are determined by more thanone factor. Much of the research in this field,however, has not controlled the testing of comfortrange. With one fitting trial, the range will tend tobe weighted toward the starting position, e.g.higher if the starting position was at the high endof the range. Two trials are needed to control forthis effect, one starting from the large dimensiontested to determine the high end of the range andone from the smallest dimension tested to deter-mine the low end of the range.The number of trials needed to control the

effects of familiarity with a protocol should bedetermined during preliminary research. Withrepeated exposure, individuals learn better meth-ods of using a space. Learning peaks out fairly fastin architectural studies but it still can affect results.Therefore, participants may be more effective incompleting the protocol in early trials, even if thetrials are in different environments. Counterbalan-cing trials is useful for reducing the learning effectsassociated with repeated trials. An alternative isthe use of a training session prior to running theactual research trials so that changes in perfor-mance caused by increased experience are notdramatic. However, we have found that, even withtraining and counterbalancing, repeated trialsshow changes in preferences as individuals adaptto the setting, particularly if it is complex orunfamiliar (Steinfeld and Danford, Unpublishedresearch).Learning effects can also be introduced by the

length and complexity of the protocol. Simplemodular protocols with short periods of demon-stration are easier to standardize and allowresearchers to give shorter discrete instructions tothe research participants.If data are to be used to improve design

standards like accessibility codes or existing designpractices, it is important that existing coderequirements or reference designs are modeled

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accurately in the research. Otherwise, externalvalidity will be compromised because the data willnot reflect existing best practices. For example, inone study mentioned above (Steinfeld et al., 1979),countertops were first modeled with a 4 in woodenledger strip supporting the counter surface above(see Fig. 1b). These strips restricted access bywheelchairs with fixed arm rests unless the counterwas raised very high. At first, the findingssuggested that the design of the counter forcedpeople with this type of wheelchair to choosebetween two poor conditions, a counter that wasactually too high for comfortable use, because ofthe 4 in (approx. 10 cm) barrier, or, a counter thatwas very low but required a wheelchair positionfar back from the work area. The design of themockup was not consistent with current bestpractice in the field. A follow-up cycle of testingcorrected that limitation with an apparatus thateliminated the ledger strip. We discovered, how-ever, that when it was removed, participantspreferred to lower the counter even more. In otherwords, they preferred a counter height that was asclose to their knees as possible rather than a higherposition that allowed them to get their wheelchairarms under the counter. This finding was contraryto what some experts in the field of accessibledesign had expected and, together with other

Fig. 1. Jigs used for stu

findings led to a recommendation that counterheights in kitchens should be adjustable.This example also demonstrates the value of an

outcome measure, preference, in providing moreinsight into how people adapt to architecturalspace. There are several other outcome variablesthat have been used in accessibility research. Theseinclude:

1. comfort as defined by the individual participant(Steinfeld et al., 1979; Sanford and Megrew,1999),

2. independence or the ability of an individualto complete the protocol without assistance(Steinfeld et al., 1979; Danford and Steinfeld,1999),

3. level of effort (Danford and Steinfeld, 1999),and

4. assistance needed (Danford and Steinfeld,1999).

Measurement methods should strive to be asaccurate as those used in conventional anthro-pometry. They should include three-dimensionalmeasurements since space is not experienced as atwo-dimensional plane. Adjustable jigs and mod-ular systems increase measurement sensitivitybecause they include more measuring points andcomparison options. They can be instrumented to

dying accessibility.

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obtain direct readings as the apparatus is adjusted.For example, In Fig. 1a and b, the jigs weremarked to provide the research staff with a simpleway to accurately identify the positions that wereusable and preferred. Today, automated capturetechniques can provide far more detailed andaccurate data. For example, video motion analysiscan be used to collect data on both humanmovement and the adjustments of the jig itself.Utilizing psychophysical scales that measure

level of effort or difficulty and level of accept-ability increase understanding of user responseand are critical for making comparisons. Observerratings can also be implemented effectively. Open-ended briefings are very useful for uncoveringdesign problems. Extensive training and inter- andintra-rater reliability analysis is required whererating methods are used (Danford and Steinfeld,1999). When completing reliability analysis, it isimportant to determine the degree of reliabilityneeded. A level of 80% agreement is generallyconsidered acceptable while a level of 90% orbetter is desirable. When reliability falls below the80% level and additional training does not resultin improvements, changes to the protocol or ratingmethods should be considered.Movement of participants can easily block

visual access to performance in full-scale model-ing. More than one observer or multiple camerasare often necessary to capture a complete record ofactivities. Automated data collection methodsimprove accuracy, allow the capture of three-dimensional information and improve the com-munication of findings significantly. But, theinstrumentation used must allow freedom ofmovement and not be so intrusive that itconstrains behavior, either physically or psycho-logically. Therefore, it is desirable to compareinstrumented and non-instrumented conditions toinsure that the equipment does not introduce suchconstraints.Unfortunately, in much of the previous research

on accessibility where modeling was used, themany reliability and validity issues describedabove were not addressed adequately. In somecases, the researchers were not knowledgeableenough to understand the issues and, in others,there were not sufficient resources to adequately

address them all (see, for example, Bails andSeeger, 1988; Floyd, 1966; Walter, 1971). The lackof three-dimensional data can be attributed to thelack of suitable automated data collection tools.Even recently, the resources devoted to accessi-bility research have not allowed the researchersworking in this field to utilize the most sophisti-cated equipment on the market. Finally, auto-mated data collection equipment still has somelimitations, for example, distortion of measure-ments collected with electromagnetic sensor sys-tems caused by metal in the surroundings andsensor systems that limit freedom of movementdue to wires that connect sensors to the dataprocessing unit. These problems can be overcomewith careful design of protocols and apparatus.

2.3. Benefits of full-scale modeling

A comparative example demonstrates the valueof full-scale modeling in architectural accessibilitystudies. Figs. 2 and 3 provides a comparison of areach envelope derived from conventional anthro-pometry with plots of findings taken from a full-scale modeling study on accessibility of storage inbathrooms (Steinfeld, 1990). In Fig. 2, ranges ofmotion are shown clear of all obstacles. But it doesnot easily communicate where the best places tolocate objects might be with respect to how theywill be used in a particular context. Fig. 3, on theother hand, maps ability directly on to a repre-sentation of a bathroom. The influence of thebathroom environment on reaching abilities is veryevident. Whole sections of the wall are inaccessibleto some individuals and the differences betweenpeople with varying abilities in using parts of theenvironment are pronounced. Note that theplumbing fixtures in the bathroom intrude intothe approach space for body positioning and thuslimited the reach envelope for several participants.For wheelchair users, the intrusion of the fixturesis significant but it has an impact even forambulant individuals. Fig. 4 shows the preferredlocations for storage in one of the prototypebathrooms studied. This plot demonstrates thatpreferences can be used to uncover a comfort zoneby modeling interactions with the environment.

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Fig. 3. Reaching abilities of individuals mapped onto wall elevations (Steinfeld, 1990). Each number represents a different individual.

Fig. 2. Reach envelopes (examples from Steenbekkers and Beijsterveldt, 1998). The values indicate the percent of the study sample

(n ¼ 74) who could reach to the corresponding distances.


There are two basic approaches to full-scalemodeling: discrete parts of the environment orwhole rooms and spaces. Fig. 1a shows a jig forevaluating the space needed to maneuver in a toilet

stall. Discrete models like this allow each part ofthe environment to be studied independently andthus there are more degrees of freedom foradjusting the rig. Several testing stations can be

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set up and research participants moved from oneto the other, a cost-effective approach to imple-menting data collection. However, there is also aneed to study rooms as whole entities in order tounderstand the relationships between parts andhow psychological and social issues play a role indetermining how the space will be used. Thus, inour more recent research, we explored methods forsimulating whole rooms while still introducingenough controls to standardize experiments. Overa period of several years and several pilot studies,we developed an easy-to-construct panel system

Fig. 4. Preferred storage locations plotted on wall elevation

(Steinfeld, 1990). Each number represents a different individual.

The rectangle encompasses preferred storage locations of all the

research participants.

Fig. 5. Full-scale room

that can be used to mock up realistic small spaces(see Fig. 5a and b). Reach ranges can be markeddirectly on the walls using stickers as individualsreach to specific targets. Storage devices andfixtures can be temporarily located according touser preferences and the positions recorded. Thepanels can be used to construct adjustable wallsthat can be fitted to a person or to compare onespatial configuration with another. The systemincludes simulated fixtures like toilets and sinksconstructed of wood, safe enough for load bearingtasks but lighter and easier to move around thanreal fixtures. The wall panels are made of woodand are bolted together at the bottom and clippedtogether at the top. Grab bars and other loadbearing devices can be bolted to the walls. Theyare strong enough to sustain the weight of aperson. Holes can be cut into the walls to facilitateobservation and videotaping. Used in a high bayspace, we can also observe, photograph and recordvideo from above.

3. Case study

This case study demonstrates how full-scalemodeling can be implemented in research onanthropometry to produce data needed to evaluateperson-environment fit. The objective of theproject, called the Measuring Enabling Environ-ments Project (Steinfeld and Danford, 1997;

simulation system.

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Danford and Steinfeld, 1999) was to developmeasures that could be used to evaluate the fitbetween people with disabilities and their environ-ment. Prior to this work, we experimented withmany different approaches, including elapsed timeand the frequency and types of collisions with theenvironment. In these experiments, we discoveredthat measures of observed behavior alone are notsensitive enough to capture the real impact of thedifference between environments. For example,how significant is a difference of a few seconds inthe elapsed time to use one bathroom compared toanother? Does the fact that a person bumps a fewwalls or other objects really mean anything interms of independence in use or difficulty in use?Without measures utilizing outcomes such aslevel of independence, assistance and subjec-tive difficulty, observed performance is not verymeaningful. Carefully developed and testedoutcome measures are not only more sensitiveto design differences but also simplify the taskof comparison, and, they provide much moreflexibility in the analysis and presentation offindings.

3.1. Measures

The three measures of performance we devel-oped were the Usability Rating Scale (URSSM),the Environmental Functional Independence Mea-sure (Enviro-FIMSM) and the Functional Perfor-mance Measure (FPMSM) (Danford and Steinfeld,1999).The URSSM is a sequential judgment ordinal

scale. It was developed to provide a subjectivemeasure of usability of an environment. FollowingPitrella and Kappler’s (1988) recommendations,the 7-point bi-polar scale is divided into subsec-tions. First, the respondent indicates whether theyconsider the challenge of the activity in question tobe ‘‘difficult,’’ ‘‘moderate’’ or ‘‘easy.’’ Next, therespondent, using the related subsection of thescale, specifies the precise degree of ease ordifficulty by referring to a number and/or corre-sponding descriptor along a horizontal axis, e.g.‘‘0’’ or ‘‘neither easy nor difficult.’’ The URSSM isadministered immediately after the completion ofan activity or set of activities.

The Enviro-FIMSM is a derivative of the widelyused 7-point Functional Independence Measure(FIMSM) instrument (Granger et al., 1986). TheEnviro-FIMSM has a 10-point measurement scaleto provide more sensitivity. It is used by a trainedobserver to rate the degree of functional indepen-dence an individual demonstrates while complet-ing an activity in a particular environment. Therating is completed using a decision-makingalgorithm based on the following indicators:

* amount of time taken,* number of attempts needed,* need for use of assistive devices,* observed risk to personal safety,* extent of caregiver assistance received.

The Enviro-FIMSM algorithm is simple enoughto use immediately after an activity takes place.However, it can also be implemented using a videorecording of the activity which is the approach weused in this study.The Functional Performance Measure (FPMSM)

is also administered by a trained rater. It has two7-point subscales: Level of Effort and Level ofAssistance. The former is used to evaluate the levelof effort expended by an individual during anactivity in an environment and the second toevaluate the degree of caregiver assistance pro-vided.The Effort rating is determined by these aspects

of task performance:

* frequency of complaint by the individual as anexpression of aggravation, inconvenience oranxiety,

* frequency of interruptions in task performance,* amount of time required,* number of attempts required.

The Assistance rating is determined by whetherassistance provided:

* is merely incidental to task performance,* directly facilitates performance, or* constitutes direct performance of the task.

Although the FPMSM scales can be used tomeasure an entire activity (e.g. using a doorway,bathing, toileting, etc.) or even a sequence ofactivities (e.g. using a bathroom), in this study, the

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component tasks of an activity (e.g. approaching adoor, activating the handle, passing through thedoor, etc.) were the unit of analysis. This level ofdetail required using a video recording to completethe evaluations. The recording was replayed asmany times as necessary to rate each individualcomponent tasks.The URSSM or the Enviro-FIMSM scales can

identify the presence of a problem, either from theconsumer or professional perspective. Takentogether, the results can confirm professionaljudgment about the usability of a design. TheFPMSM scales provide data that ties the problemsthat people encounter using an environment withspecific parts of the setting. Thus, the URSSM andEnviro-FimSM identify problems and the FPMSM

identifies the cause of the problem.The scales have good sensitivity, reliability and

validity. For the Enviro-FIMSM agreement scoresfor inter-rater reliability were as high as 96% andas high as 92% for intra-rater reliability. Inter-rater-reliability for the FPMSM was 84% for Levelof Effort and 96% for Level of Assistance. Intra-rater reliability was 97% and 99%, respectively.Analysis of data for all three measures demon-strated that they discriminate between level ofaccessibility and between different user popula-tions very well and in the expected pattern (formore information see Danford and Steinfeld,1999).

3.2. Methods

As part of our research we had 24 adult femaleswith mobility impairments (i.e., 12 wheelchairusers and 12 walking aid users) simulate theperformance of several activities of daily livinglike grooming, toileting, and bathing in three full-scale simulations of residential bathrooms usingthe system shown in Fig. 5. By systematicallyvarying all the design features in a bathroom, threedistinct levels of accessibility were provided: a‘‘challenging’’ bathroom, an ‘‘intermediate’’ bath-room and a ‘‘supportive’’ bathroom (see the plansin Fig. 6). Two simulations were constructed priorto the research sessions. While the second simula-tion was being used, we reconfigured the first oneto the third variation.

The protocol included a series of six separateactivities: using the door to enter and exit the room,using the grooming area (sink), transferring to thetoilet, using the toilet, transferring to the bathtub orshower, using the bathtub or shower. Each of theseactivities was broken down into a series ofcomponent tasks (see Tables 1 and 2 for anexample). Each participant was instructed to com-plete each component task one at a time while thetrial was videotaped. Activities were counterba-lanced to control for order effects. A trainedattendant was available to assist in transfer activitiesif a person needed assistance. Each participant ratedthe difficulty of each activity using the URSSM Scale.The videotapes were used to complete the ratings forthe Enviro-FimSM and the FPMSM scales.

3.3. Results

In brief, mean scores for the URSSM andEnviro-FIMSM across all activities confirmed thatthe ‘‘challenging’’ bathroom was much moredifficult to use than the other two and that the‘‘supportive’’ bathroom was the easiest to use, thepattern of results one would expect. These resultsconfirm the construct validity of the measures. Theresults also demonstrated that wheelchair usershad more difficulty than walking aid users, alsoconfirming construct validity. We identified ‘‘dooruse’’ as an important contributor to the difficultyof using the ‘‘challenging’’ bathroom. The FPMSM

findings for ‘‘door use’’ identified the specificproblems that participants encountered. Maneu-vering to close to the door (i.e., ‘‘closing maneu-ver’’) and then actually closing the door afterentering the room (i.e., ‘‘closing’’) are tasks thatthe wheelchair users found virtually impossible toperform in the ‘‘challenging’’ bathroom, even withmaximum effort (i.e., FPMSM mean Level ofEffort scores of 3.67 and 4.25, respectively). Thus,substantial assistance was typically required (i.e.,FPMSM mean Level of Assistance scores of 2.25and 2.58, respectively).

3.4. Design implications

These findings are quite interesting in thatgetting into spaces is usually the main concern of

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Fig. 6. Plans of three bathrooms.


accessibility. This study discovered that, even withdoors on the challenging bathroom that werenarrower than standards require (30 in, or 76 cm,clear versus 32 in, or 81 cm, clear for standards),obtaining privacy in a bathroom by closing thedoor independently is actually more difficult toachieve than passing through the doorway forpeople who use wheelchairs (Table 3).

Two specific design features of the ‘‘challen-ging’’ bathroom combined to create barriers forthe wheelchair users: the ‘‘open floor space’’ insidethe bathroom was only 15 ft2 (approx. 1.4m2) andthe hinged door swung into the bathroom andacross large portions of that open space. Insuffi-cient space was left for users to move out of theway in order to close the door when they were

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Table 2

Mean FPMSM level of assistance scores for door use (challen-

ging bathroom)

Door use

tasks

Level of assistance

Wheelchair

users (n ¼ 12)

Walking aid

users (n ¼ 12)

Total

(N ¼ 24)

Approach 0.00 0.00 0.00

Opening

maneuver

0.00 0.00 0.00

Latch use 0.25 0.00 0.13

Opening 0.00 0.00 0.00

Through

passage

0.00 0.00 0.00

Closing

maneuver

2.25 0.00 1.13

Closing 2.58 0.29 1.44

Note: 0=no assistance required; 1=minimal assistance,

2=moderate assistance, 3=maximum assistance, 4=impossi-

ble even with maximum assistance, 5=declined to do the task,

6=blocked by the environment.

Table 1

Mean FPMSM level of effort scores for door use (challenging

bathroom)

Door use

tasks

Level of effort

Wheelchair

users (n ¼ 12)

Walking aid

users (n ¼ 12)

Total

(N ¼ 24)

Approach 1.33 1.25 1.29

Opening

maneuver

1.00 1.42 1.21

Latch use 1.58 1.25 1.42

Opening 2.42 1.83 2.13

Through

passage

2.17 1.83 2.00

Closing

maneuver

3.67 1.86 2.77

Closing 4.25 1.86 3.06

Note: 0=no effort required, 1=minimal effort, 2=moderate

effort, 3=maximum effort, 4=impossible even with maximum

effort, 5=declined to do the task, 6=blocked by the environ-

ment.

Table 3

Metric equivalents for dimensions in figures

Dimension In cm

1 ft 6 in 18 46

1 ft 8 in 20 51

5 ft 1 in 61 155

5 ft 8 in 68 173

7 ft 5 in 89 226

7 ft 8 in 92 234

8 ft 8 in 104 264


inside the room. Increasing the size of the ‘‘openfloor space’’ and reversing the swing of the door toopen out are two design interventions that canfacilitate usability, especially for wheelchair users.This case study demonstrates how full-scale

modeling can be used to systematically compare

designs and identify the level of anthropometric fitusing carefully developed outcome measures. Theresults of research like this can also be used toevaluate building standards and identify revisionsthat are necessary to improve them.In this work, we discovered two important

characteristics of protocols that are critical tosystematic evaluation with full-scale modelingtechniques. First, the activities simulated need tobe standardized in order to compare the results ofone trial or research participant with another.Second, these activities should be divided intocomponent tasks that can be kept discrete.This makes it possible to isolate and analyzehow individuals use specific features in theenvironment.

4. The challenge of computer modeling

It is very useful to include full-scale modelingactivities as part of the design process. Forexample, during the design of an assisted livingfacility, it would be desirable to test out severaloptional designs for the bathrooms in full scale,but such a research activity is usually beyond themeans of the design team because full-scalemodeling is a time-intensive, expensive activity.Virtual modeling methods like SAMMIE andMannequin Pro could provide a low cost efficientsubstitute for designers.But, at the present time, such models do not

contain accurate information on the anthropome-try of people who use wheelchairs. To address thisdeficiency, data on large samples of wheelchairusers are needed. These surveys can collect detailed

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structural data as well as functional data oncommon tasks, e.g. reaching. Although full-scalemodeling cannot provide data on large samples, itcan be very effective in providing other types ofinformation needed for computer models.Existing modeling programs do not realistically

simulate the behaviors of people with disabilities inarchitectural spaces. This group has a morecomplex interaction style due to the limitationson their body movements and their use of assistivetechnology like wheelchairs. Even with an ade-quate underlying anthropometric database, com-puter modeling would still not capture preferences,level of effort and assistance that may be requiredto accomplish various activities and the degree ofindependence possible in different conditions. Tomake computer simulations more effective, meth-ods to incorporate outcome measures are needed.There are some outcome measures that can be

built into computer models, like the degree ofinterference between the movement of a manikinand the objects in the virtual environment (see, forexample, Lantrip, 1999, pp. 139–163). However, thisdata is of little value without understanding howpeople with disabilities move in space, the impact ofdifferent spatial conditions and how people adapt toobstacles and spatial conflicts. To develop mean-ingful models, we also need information on thekinematics and kinetics of various tasks completedby people with disabilities so that we can modelthem in virtual space. We also need information onthe difficulty and acceptability of tasks underdifferent spatial constraints. This information canonly be generated from full-scale modeling studies.Thus, for the time being, full-scale modeling is veryimportant to develop a foundation for the develop-ment of realistic computer simulations.The research referenced in this article used

relatively unsophisticated methods. New datacollection technologies, such as those used insports training or work task analysis are availablethat can provide streaming data on kinematics andkinetics and automatically generate simulationsusing figure models. These technologies can beused in conjunction with full-scale modeling.Combined with outcome measures, like theURSSM, Enviro-FimSM and FPMSM, they offergreat promise for filling the gaps in our knowledge.

5. Conclusions

Conventional anthropometry does not measuremany aspects of how people interact with the builtenvironment. Full-scale modeling can be usedeffectively to study the usability of products orenvironments. Using relatively small samples ofdiverse individuals, it is possible to rapidly identifyextremes of functional performance, preferencesand other user responses. The effectiveness of suchstudies would be enhanced if the participants wereselected from larger traditional study samples sothat results could be generalized to the largerpopulation. Such data is very useful for designers.It can be used to determine how conventionalanthropometric data needs to be modified forapplication to specific conditions in the real world.Existing full-scale modeling methods, however,

have limitations. Without expensive equipment,the precision of the data collected is not near thelevel of conventional anthropometrics. In particu-lar, three-dimensional data, kinematics and ki-netics of people with disabilities have not beenrecorded completely and precisely. Although itmay be that such precision is not necessary fordesign of built environments, it is required for theconstruction of computer models. To be mosteffective, computer models should incorporateoutcome measures that provide information onthe degree of fit between people and environment.Such measures provide far more useful informa-tion than simply scalar qualities, e.g. space neededto accommodate a person.Technology available today allows traditional

human modeling approaches and the full-scalemodeling approach to be integrated. Such studieswill be far more precise and the data generated willbe more usable by designers. One new approach isbeing pilot tested now at the Department ofArchitecture at Lund University in Sweden. Aresearch team is developing a virtual space analysissystem. They installed a video motion analysissystem with 12 cameras in a large high bay spacedesigned specifically for full-scale design explora-tion. These systems are used widely in research andclinical practice. Typically, targets are located onbody landmarks of participants and the systemrecords the location of the landmarks as they

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move. The results can be displayed as a movingimage. At the Lund laboratory, targets will also beattached to the pieces of the environment used infull-scale modeling, e.g. partition walls, counters,furniture. The positions of both body landmarksand environment will then be traced at the sametime, automatically capturing the interaction ofenvironment and person. This system couldsimplify data collection and analysis considerably.The dynamic representation could also be avail-able for detailed analysis and display in manydifferent orientations.

6. Recommendations for future research

1. Full-scale modeling should be used togetherwith conventional structural and functionalanthropometry to provide more useful informa-tion for accessible design and universal design.The value of this method has been demon-strated extensively in the automotive and aero-space industries.

2. Research protocols should incorporate realworld tasks, preferences and evaluative re-sponses to understand the true complexities ofbehavior in representative naturalistic condi-tions.

3. Automated measurement systems need to bedeveloped to make full-scale modeling moreaccurate and practical. These systems must beunobtrusive and ideally include automatedvisualizations of data.

4. Ethnographic or survey research studies are avaluable preliminary step in developing proto-cols for full-scale modeling research becausethey provide information on how people withdisabilities operate in the real world. Thisinformation can be used to develop realisticprotocols.

5. Agencies that fund research in this field shouldbe investing in the development of infrastruc-ture for full-scale modeling at the architecturalscale. This includes providing funding levelsthat will allow the purchase and maintenance ofapparatus and instrumentation as well as pureequipment purchases to build capacity.

6. Research Centers with interests in this fieldshould find ways to collaborate. Sharing pro-tocols and developing complementary resourceswould result in a more effective effort than ifeach center worked independently.

Acknowledgements

Funding for the research described in thisarticle was provided by the Department ofEducation, National Institute on Disability andRehabilitation Research through the Rehabilita-tion Research & Training Center on FunctionalAssessment & Evaluation of Rehabilitation Out-comes, Center for Functional Assessment Re-search, Department of Rehabilitation Medicine,State University of New York at Buffalo. Fundingfor the development of this article was supportedwith a grant provided by the Department ofEducation, National Institute on Disability andRehabilitation Research through the Rehabilita-tion Engineering Research Center on UniversalDesign at Buffalo (Grant #H133E990005). Theopinions expressed in this paper are those of theauthor and do not represent those of the Depart-ment of Education or those of the NationalInstitute on Disability and Rehabilitation Re-search. Thanks to G. Scott Danford, who co-directed the research described in the Case Study.

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