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Visually-controlled leg movements embedded in awalking taskCOLIN G. DRURY a & SANDRA M. WOOLLEY ba Department of Industrial Engineering , State University of New York at Buffalo , 342 BellHall, Buffalo, NY, 14260, USAb Department of Rehabilitation Medicine , Medical College of Ohio , PO Box 10008, Toledo,OH, 43699-0008, USAPublished online: 28 Mar 2007.

To cite this article: COLIN G. DRURY & SANDRA M. WOOLLEY (1995) Visually-controlled leg movements embedded in a walkingtask, Ergonomics, 38:4, 714-722, DOI: 10.1080/00140139508925143

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ERGONOMICS, 1995 VOL. 38 NO.4, 714-722

Visually-controlled leg movements embedded in awalking task

COLIN G. DRURY

Department of Industrial Engineering, State University of New York at Buffalo,342 Bell Hall, Buffalo, NY 14260, USA

SANDRA M. WOOLLEY

Department of Rehabilitation Medicine, Medical College of Ohio, PO Box10008, Toledo, OH 43699-0008, USA

Keywords: Fitts' Law; Walking; Leg movement times.

Accurate control of alternating leg movements in walking was considered as avisually controlled target aiming task. Nine subjects aimed alternate feet at targetsalong a walkway, using nine combinations of amplitude and target width givingindex of difficultyvalues between 2·59 and 6·16. Movement time was compared tothe samesubjects performingreciprocal tapping tasks with arm and leg.Alternatingtarget aiming tasks were the most rapid of all tasks studied. Explanations of thiseffect in termsof learningand eliminationof directionchangeswere consistentwiththe data from all conditions. Visualcontrol can be expected in normal walkingonlyfor target sizes smaller than about 300 mm, i.e., under unusual accuracyrequirements.

1. IntroductionWalking has been described as a series of controlled falls requiring a repeated patternof using, losing, and regaining a balanced stance with the alternating lower extremity(Brooks 1986, Soderberg 1986). It is initiated from an upright standing position andrequires repositioning of the support leg beneath the body to maintain a moving baseof support as the body continuously falls forward. Das and McCollum (1988)characterized the functions of locomotion as progression of the body in the desireddirection and not falling down. These functions were called task invariants, which isthe minimal description for adequate performance. During the swing phase anindividual must perform the tasks of: (I) avoid obstacles; (2) move the foot to newsupport; and (3) control the transfer of angular momentum. During the stance or supportphase, an individual must perform the tasks of: (I) production of a force forward orbackward; and (2) provide upward support (Das and McCollum 1988). Given these taskinvariants or goals, the accuracy of initial foot placement is a critical task, since it willhave an impact on activities occurring later in the walking cycle, in particular, themaintenance of balance during forward progression.

Foot placement is an aiming task, in that the foot must hit the ground at a positionwhich will maintain balance, avoid any obstacles, and provide for the necessary forceson the next step. Because walking is so highly practised, we rarely need to consider itstarget aiming aspects, unless they become critical. Examples would be walking on rocksacross a stream or avoiding items strewn on the floor.

Target aiming tasks with both hands and feet have been studied extensively, withmost data fitting one of two models: ballistically-controlled movements where the endaccuracy constraints are low; and visually-controlled movements where the accuracyrequirements are high.

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Visually-controlled leg movements 715

Gan and Hoffmann (1988) showed that for hand movements, ballistic control datawas fitted by

Movement Time (MT) = kvA

where A is the movement amplitude, while visually-controlled movements were bestdescribed by Fitts' Law (Fitts 1954).

Movement Time (MT) = a + b (ID)

where 10 is Fitts' index of difficulty defined as

where Wis the target width in the same units asA. The critical 10 value for change fromballistic to movement control was approximately 3-4 for amplitudes studied, or moreaccurately:

IDeRIT = 2·86 + 0·10 IVA(mm)

For leg movements, up to 700 mm in amplitude, Hoffmann (1991) found a critical IDof about 3. If similar formulations apply to foot movements of the type encounteredin walking, it may be possible to define walking tasks where either:

I. no visual control is necessary and hence foot movements are pre-programmed; or2. visual control is necessary so that vision (and more central processing resources) is

required.

In a variety of walking studies that have examined normal males and females, thewalking stride length is around 1300 mm for females and 1500 mm for males (Finleyand Cody 1970, Murray et al. 1964). The critical 10 values would thus be, accordingto Gan and Hoffmann's equation, 6·5 for females and 6·8 for males. These imply criticaltarget widths of 28 mm and 27 mm respectively. As the Gan and Hoffmann equationhas not been tested at such large amplitudes, the more conservative value of critical 10on gives a critical target width of375 mm. Thus it is possible to conceive of the walkingtask being either ballistic or visually-controlled at target sizes which may beencountered under different conditions. At one extreme of walking conditions are thosewhere the only accuracy constraint is the preservation of balance and force, whichappears from general observations not to require visual control. At the other extremewould be walking over difficult terrain where careful foot placement is necessary toavoid obstacles, such as mountain hiking, or using stepping stones.

One study that reported results on target aiming in such a task was that of Warrenet al. (1986). They studied two long distance runners (two of the authors) whose taskwas to hit targets appearing in succession while treadmill running at 4 ms - I. Targetswere 300 mm long, and randomly spaced from 1000 to 1700 mm apart. If the task wasto hit any part of a target with a given part of the foot (this was not made clear) thenthe ID values ranged from 2·6 to 3·5, indicating that some visual control may benecessary over part of this range. The data were analysed in terms of gait parametersrather than a (MT, A, W) relationship. Mean absolute error, which is difficult to interpret,is given as 122 mm (SO = 83 mm) for one subject and 313 mm (SO = 185 mm) for theother. From this data, obtained in a paced task, it is difficult to determine how far theaiming task was under visual control, but the 10 values and implied movement times

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716 C. G. Drury and S. M. Woolley

(from the speed and target spacing) can be compared with experimental data collectedin the current study.

Foot movements have been studied for lateral movements (in the frontal plane) byDrury (1975) who fitted movement times with an equation appropriate to visuallycontrolled movements, i.e., Fitts' Law. Later analysis (Hoffmann 1991) suggests thatthe accuracy requirements were so low that a ballistic formulation may have been moreappropriate. Lateral movement times have also been measured by Hoffmann (1991),who used ID values in the region where visual control would be expected, and foundthat Fitts' Law was an excellent fit to the data.

Walking, on the other hand, requires foot movements mainly in the sagittal plane.In addition, it involves a series of alternating aiming movements with the left and rightfeet, instead of the single (discrete) or reciprocal (tapping) movements typically studied.Thus the objective of the current study was to determine the relationship betweenmovement time and ID for sagittal, alternating foot movements, and hence test whethera Fitts' Law formulation is appropriate, and if so, over what range ofID values. Becausealternating movement studies have not been reported. a separate experiment on sagittalreciprocal movements was included for comparison with data from the literature.Finally, a hand movement study using the same ID values was used to compareperformance of our subjects with that reported by other laboratories, and to determinewhether hand and foot movement control correlated across individuals.

2. Method2. I. SubjectsNine graduate students, three females and six males, with a mean age of24·8 years wererecruited for this study. No payment was given. The same subjects participated in eachof the three experiments. The experimental protocols were explained to each subjectprior to participation and written informed consent was obtained.

2.2. ProceduresDuring experiment I, lateral reciprocal arm movements, similar to those used by Fitts(1954) were examined. Experiment 2 consisted of fore-aft sagittal plane tappingmotions of both the arms and legs and experiment 3 examined the alternating motionsof the lower extremities during three walking trials. The task in experiments I and 2consisted of 20 movements being performed as quickly as possible. The task difficultywas determined by the distance between the targets and the width of the target. Nineamplitude/width combinations were used for each experiment with indices of difficultyranging between 2·59 and 6·16 bits per response covering the ranges of both ballistic(lD < 3·0) and visually-controlled movements as defined by Gan and Hoffmann (1988)and Hoffmann (1991). The subjects were permitted time to practice each condition priorto actual data collection. The movement time for each trial/condition was recorded bya video camera which contained a digital timer. Trials were terminated and rerun if thesubject committed one or more errors. The order of the nine amplitude-widthcombinations was randomised and different for each subject.

3. Experiments3.1. Experiment J: Arm movements in the frontal plane3.1.1. Method: During experiment I, subjects performed a continuous reciprocal

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Visually-controlled leg movements 717

Table I. Mean movement times (ms) for each condition in each experiment.

ID value Arm, Fr Arm, Sag Leg,Sag Walk, Toe Walk, Heel

4·]7 381 421 649 237 2695·]7 464 523 827 330 3646·16 616 671 980 424 4423·59 344 367 503 211 2544·59 429 479 682 304 3645·58 579 640 901 389 4482·59 272 289 391 166 2473·59 386 392 553 278 3624·58 501 502 752 393 443

Note: Where two ID values are the same in the first column, the earlier value refers to thecondition with the larger amplitude and width values.

tapping task using the dominant arm, similar to the task used by Fitts in 1954. Subjectswere comfortably seated at a desk and their forearms positioned to permit whole armmotion. The two targets, which were 0·152 m in length and symmetrically located Onsheets of blank paper, were placed on the desk directly in front of the subjects. The nineamplitude/width target combinations were composed of movement amplitudes of0·229, O· 152 and 0·076 m and target widths of 0·025,0·013 and 0·006 m. Following alOs practice trial, the subject was asked to perform 20 cycles for each amplitude/widthcombination. The mean movement time per touch was determined from the 10 middlecycles and used in the statistical analysis. Subjects had to repeat a trial if one or moreerrors of target aiming were made.

3.1.2. Results: A mixed model analysis of variance with three amplitudes (A), threewidths (W), and nine subjects was conducted Onthe mean movement times. Both A andW were significant at p < 0·01 with F(2, 70) = 19·6 and F(2, 70) = 103·0 respectively.The A X W interaction was not significant. Mean moment times are given in table I.Linear regression of mean movement time against index of difficulty gave the followingequation:

MT = 0·0138 + 0·0962 (10) ? = 0·917

The slope of 0·0962 falls within the range of 0·087 to 0·104 reported for a similarreciprocal tapping task by Fitts' (1954). Different slopes have been reported for discretemovements (Hoffman 1991) as the reciprocal movement task also includes time spentOn each target within the measured movement time. Figure I shows the regression lineand data with no evidence of a transition from ballistic to visually controlled movementsover this range.

3.2. Experiment 2: Arm and leg movements in sagittal plane3.2.1. Method: This experiment examined the movement times of fore-aft continuoustapping motions of both the dominant arm and dominant leg in the sagittal plane. Theprotocol used in the arm task was the same as that used in experiment I, except thatthe subject's arm motion was directed towards and away from the midline of the body.During the leg tapping task, the subjects stood on their non-dominant leg directlybetween the targets and moved their dominant leg back and forth between the targets,which were outlined Onthe floor. Upper body support was provided by resting the arms

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718 C. G. Drury and S. M. Woolley

~M;::o=ve=m=e=n=t=T=lm=e=,~mc:.s 1120°1

Arm, Frontal

1000 + Arm, Seglttal

* leg, 8aglttal

800

800

400

200

ol-_~--~-----'---~-~--~--

o 234567Index of Difficulty

Figure I. Movement time data and regressions on 10 for experiments I and 2.

on an adjacent bench to ensur~ that the subjects were balanced. The movementamplitudes and target widths for leg movements were respectively, 0·686, 0-457, and0·229 m and 0·076, 0·038 and 0·019 m. The resulting amplitude/width combinationswere selected so that the index of difficulty would be the same for both the arm andleg motions. Subjects were told that the objective of this task was to touch their greattoe and forefoot within the target area while moving as quickly and accurately aspossible. The amount of practice and actual data collection were the same asexperiment I.

3.2.2. Results: Mean movement times for arm and leg movements were subjected toseparate analyses of variance, using the same model as in experiment I. Againamplitude and width gave significant effects (arm: F(2, 70) = 26·7 and 74·3 and leg F(2,70) = 18·9 and 39·1 respectively) at p < 0-0 I while neither A x W interaction reachedsignificance at p < 0·05. Regression analyses against ID gave the following equations:

Arm: MT = - 0·0179 + 0·111 (ID)

Leg: MT = - 0·0741 + 0·173 (ID)

?-=0-967

?- =0-983

These two equations and their corresponding data, are presented in figure I. A two factormixed model ANOVA, with nine ID values and two limbs (arm, leg) gave a significantlimb effect (F(I, 144) = 134·5, p<O·OI) as well as the expected ID effect (F(8,144) = 31·7,p < OpOI),but no limb x ID interaction (F(8, 144) = \·67,p > 0·05). Thusequivalent arm and leg movements in the sagittal plane differ greatly in absolutemagnitude, but not significantly in slope. However, a direct test of the regressionsshowed that the slopes were significantly different, t(l4) = 5·30; p < 0-01, whileintercepts were not, t(14) = 1-06, P > 0·25. Both limbs gave excellent linearrelationships between movement time and ID, with no transition to ballistic movementsat low ID values.

A second comparison is useful to determine whether the two data sets for armmovements in the frontal and sagittal planes differed. A two factor mixed modelANOV A was performed on mean movement times for two directions (frontal, sagittal)and nine ID values. Direction was significant (F(l, 144) = 10·7, P < 0·01) as was ID(F(8, 144) = 54·6, p < 0·01) while their interaction was not significant (F(8,144) = 0·54, P > 0-05). Thus sagittal movements were slower than the equivalent

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Visually-controlled leg movements 719

frontal movements. Despite its significance, the direction effect is much smaller inmagnitude than the limb effect. Hoffmann (1991) also found slower movement timesfor legs, but for frontal plane movements. For his pilot study, which used a reciprocaltapping task, the slopes obtained were very similar to those found here:

Arm: Hoffmann slope = 0·117 slbitCurrent experiment slope = 0·111 slbit

Leg: Hoffmann slope = 0·178 slbitCurrent experiment slope = O· 173 slbit

For Hoffmann's main experiment, discrete movements were used, resulting inconsiderably lower movement times.

3.3. Experiment 3: Simulated walkingThe purpose of this experiment was to examine the application of Fitts' Law to humanlocomotion. The walking trials required the subjects to perform aiming tasks using thegreat toe and forefoot (experiment 3A) and the posterior-most aspect of the heel(experiment 3B). The toe and heel aiming trials were used to simulate overgroundwalking in circumstances where visual control may be required (as distinct from normalwalking).

Instead of reciprocal movements between a single pair of targets, subjects inexperiment 3 were presented with eight targets placed in a straight line along thelaboratory floor. Subjects placed alternate feet onto alternate targets (using toe or heelas appropriate) to give eight steps, four by each foot. Two practice trials at eachcombination of width and amplitude used in experiment 3 were given prior to datacollection. The trial was again repeated if an error occurred. Mean movement time foreach subject in each condition was made by timing the final three strides of the dominantleg.

3.3.1. Results: Analyses of variance were conducted for mean movement times for bothheel and toe walking, using the same model as in experiments I and 2. For both datasets only target width was significant at p < 0·01 (F(2, 70) = 35·2 for toe, F(2,70) = 25·1 for heel) with no amplitude or interaction affects significant at p < 0·05.

Regression of mean movement time onto index of difficulty gave the followingequations:

Toe: MT = - 0·192 + 0·0726 (ID)

Heel: MT = 0·0914 + 0·0592 (ID)

~=0·816

,1=0·642

Both equations and data are shown in figure 2 with the sagittal leg movement resultsfor comparison. A two factor mixed model ANOVA with foot position (heel, toe) andnine [D values as factors was performed on mean movement times. Foot position wassignificant (F(l, 144) = 12·6, P < 0·01) as was ID (F(8, 144) = 15·3, P < 0·01), but theinteraction was not significant of p < 0·05.

The leg sagittal reciprocal movements from experiment 2 were compared with theheel and toe data from experiment 3 using a two factor mixed model ANOV A of meanmovement times. The factors were task (reciprocal, heel alternating, toe alternating) andID at eight values. Both factors and their interaction were significant with F(2,216) = 275·0,p < 0·01 for task, F(8, 216) = 28·1 ,p < 0·01 for ID and F(l6, 216) = 3·5,

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720 C. G. Drury and S. M. Woolley

Table 2. Intercorrelation matrix of mean movement times across subjects.

Ann, FrontalArm, SagittalLeg, SagittalWalk, Toe

Arm, Sagittal

0·84

Leg, Sagittal

0·670·77

Walk, Toe

0·610·730·79

Walk, Heel

0·530·670·630·84

p < 0·0 I for the interaction. Thus reciprocal tapping was much slower than alternatingfor leg movements both in absolute magnitude, and in rate of change with ID.

As a final comparison, the ann movement data (frontal and sagittal) was comparedto the walking data (heel and toe) in a two factor mixed model ANOVA on meanmovement times. There were two levels of task (arm, walking) and eight levels of ID.All effects and interactions were significant at p < 0·0 I with F( I, 306) = 201·1 for task,F(8, 306) = 52·7 for ID and F(8, 306) = 3·32 for their interaction. Thus both versionsof the simulated walking task were more rapid than the typical arm movementtarget-aiming task, and less dependent upon index of difficulty.

An intercorrelation matrix of the subject means in each task gave the results shownin table 2. All correlations were significant at p < 0·05. In his pilot study, Hoffmann(1991) found a significant correlation of 0·68 between arm and leg frontal movementtimes, close to the value of o·77 shown in table 2 for the equivalent sagittal movements.For discrete movements Hoffmann's correlation of 0·20 did not reach significance ofp<0·05.

4. DiscussionThe three experiments described here produced linear relationships between movementtime and index ofdifficulty for five different conditions. In all five, there were significantID effects accounting for a high proportion of the mean movement time variance. Annmovements were more rapid than leg movements for reciprocal tapping tasks, asexpected from previous studies (Hoffmann 1991). The absolute magnitudes of the annmovement times were closely comparable to those found in the earliest experiments(Fitts 1954). For leg movements the movement times were closely comparable to thosefound in a lateral reciprocal tapping taks, but were slower than those published fordiscrete movements (Hoffmann 1991), as expected where time-on-target contributes tomeasured movement time.

Comparison of the current results with those ofWarren et al. (1986) is problematicalbecause of the pacing, the random ordering of amplitudes, and the lack of definitionof effective target sizes and errors, but some attempt may be made. At 4 ms - I treadmillspeed, the (paced) movement times for the amplitudes given must be 0·25 to 0-42s. Ifthe target sizes were as specified (300 mm) then ID values range from 2·6 to 3·5. SuchID and MT values would place the Warren et al. data between the lines for leg sagittaland heel walking on figure 2, which at least shows that their data are not incompatiblewith experiments 2 and 3.

When lower limb target aiming tasks were embedded within a simulation ofvisually-controlled walking, the results were considerably different from reciprocaltasks. Movement times for leg movements in this task were much more rapid than forreciprocal leg movements, and indeed more rapid than ann reciprocal movements ofequivalent index of difficulty. For the visually controlled movements in the walking

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Visually-controlled leg movements

Movement Time, me1200Fr=====::;--'-----------I

.1, . Walking. To.

1000 I + Walking, H••'

~ Lag, Sagittal

800

800

000

200

721

oIL-_---L__--'-__~_~_____'_____'____ _'

0234667lndex of Difficulty

Figure 2. Movementtime data and regressions on ID for experirnent 3, with leg sagittaldatafrom experiment2 for comparison.

task, there was also no significant amplitude effect, a lower rate of change of MT withID, and a lower fraction of variance explained by a linear relationship to ID.

There are a number of differences between these visually controlled movementsembedded in a walking task and the typical reciprocal tapping task which appear toaccount for some of these differences:

(i) Limb type: Legs are slower than arms for the equivalent task as shown in experiment2, presumably due to inertial considerations even when visual control is dominant.

(ii) Movement direction: Frontal plane movements were faster than sagittal planemovements (experiments I and 2).

(iii) Task type: Reciprocal movements were slower than the equivalent alternatingmovements (experiments 2 and 3). This may be partly a time-on-target consideration.With alternating movements, such as walking, the time-on-target for one leg isoverlapped with movement of the contra-lateral leg. It may also be an inertialconsideration, as the limb does not have to change direction in alternation. It is unlikelyto be due to a difference in visual conditions as the same eye to target distances weremaintained in the leg reciprocal task of experiment 2 and the two tasks in experiment3. There may be a greater visual difficulty with heel placement than toe placement,perhaps due to partial obscuration of the heel, as the heel walking times were slowerthan the toe walking times. Finally, the very high degree of practice in the alternationtask must be considered. Walking itself is an activity learned early in life and practicedconstantly, under both ballistic and (at times) visually-controlled conditions. It shouldthus be much more rapid than the equivalent reciprocal task.

Thus the walking simulation could be expected to produce faster times than armmovements due to its sagittal direction, its lack of time-on-target and its highly practicednature, but slower ones due to its larger limb inertia. This latter factor is offset by theelimination of inertia effects due to movement direction changes. The significantinterrelations in the movement times of the five tasks suggest that there is commonalityin the processes controlling each of these movement tasks. The higher correlationcoefficients obtained from movements utilizing the same segmental inertias (over 0·8)

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722 Visually-controlled leg movements

further support the contention that inertia accounts for the slower movement times inthe leg tasks, particularly the alternating leg movements.

Given that the accurate movement task embedded in a walking task gave consistentresults, the larger question of its application to non-laboratory walking tasks can beconsidered. At the ID values used here (2·59-6·16) there was no evidence of ballisticrather than visual control. Indeed, the significant width effect and insignificantamplitude effect in experiment 3 are directly counter to ballistic results. Thus, if thereis a transition to ballistic movements, the critical ID value cannot be higher than about3·0. Thus the higher critical ID values ofover six predicted by Gan and Hoffmann (1988)for arm movements do not appear to hold for leg movements. If the critical ID valueis about 3·0, then the critical target size would be over 300 mm for a normal stridelengths for both men and women. Such large targets, about the foot length, imply thatmuch walking can be ballistic as only body balance and force exertion are needed todefine foot placement. Target widths below foot length can be common, however, innegotiating uneven terrain, or avoiding obstacles on a floor, When the need arises forsuch accurate control, these experiments have shown that Fitts' Law provides a suitableformulation.

S. ConclusionsFitts' Law provides a good model for visually-controlled movements embedded in awalking task. The speed of such movements is faster than either equivalent reciprocalleg movements or reciprocal arm movements. A critical index of difficulty marking achange from ballistic to visually-controlled movements was not found for ID values inthe 2·59 to 6·16 range, suggesting that visual control is only operating for target sizessmaller than the foot.

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