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Proceedings of the 8th Annual

International Conference on Industrial

Engineering Theory, Applicationsand Practice, Las Vegas, Nevada, USA,

November 10-12, 2003

442

KINEMATIC ANALYSIS OF MOUSE CURSOR POSITIONING AS A

FUNCTION OF MOVEMENT SCALE AND JOINT SET

Michael Bohan1, 2, Shelby G. Thompson 2, and Peter J. Samuelson 2

1National Institute for Aviation Research2Department of Psychology

Wichita State University

1845 FairmountWichita, Kansas 67260-0034

Corresponding authors e-mail: [email protected]

Abstract: This study examined the impact of movement scale and joint set on the trajectories of rapid pointing movement

made with a mouse. Participants manipulated a mouse to point a cursor to targets on a display of varying size and distance.

The mouse movement was scaled independently of the display size by varying the control gain. Large-scale movements

involving proximal joints (forearm) yielded longer movement times than small-scale movements involving distal joints

(wrist, fingers). However, participants had particular difficulty placing the cursor on precise targets located in near space.This was evidenced in the kinematic analysis which revealed a relatively shorter distance traveled in the primary

submovement and a greater proportion of movement time spent making secondary adjustments. These results suggest that

the primary limiting factor in mouse pointing is its affordance of a power grip which limits the mobilization of the fingersfor fine-grained movement.

1. INTRODUCTION

Pointing movements with a mouse are used to control a cursor to point to targets on a graphical display. For pointing

movements with various gain settings, gain affects the scale of the mouse movement regardless of the display scale.

Previous studies used Fitts law (MT = a + b ID, where ID = Log 2 2A/W) to describe pointing behavior with variousinput devices (see Douglas & Mithal, 1997 for a review). However, Fitts law considers only the distance to be covered and

the size of the target in display space. It is not concerned with the motor activities involved in manipulating the mouse incontrol space, which can vary considerably depending on the gain and thus the control movement scale. For example, largevariations in scale produce dramatic biomechanical changes (Guiard, Beaudouin-Lafon & Mottet, 1999; Lacquaniti, et al.,

1987). Large-scale movements are carried out primarily using the proximal joints of the upper limb (shoulder and elbow)

whereas small-scale movements are carried out by the most distal joints (wrist and fingers). Previous studies have reported

large variations in the slope of Fitts ID-MT relationship depending on the limb segments used to execute a motor response(Balakrishnan & MacKenzie, 1997; Langolf & Chaffin, 1976; Zhai, Milgram & Buxton, 1996). Gibbs (1962) also reported

differences in the gain-movement time slope depending on whether a joystick was controlled by the fingers, wrist or arm.

In the present study, participants made rapid pointing movements to targets on a display using a mouse under varying

conditions of gain, target distance and target size. By varying target distance, in conjunction with gain, the goal was to

investigate the contributions of the different joints of the upper limb to mouse cursor control. Since our previous study(Bohan, Thompson, Scarlett & Chaparro, 2003) found that display target size was an important factor in determining the

effects of gain on mouse pointing time, the present study also manipulated this variable.

To further investigate the contributions of control movement scale and joint segments, a kinematic analysis examinedthe trajectories of the mouse motions. Specifically, movements were segmented into a primary submovement with a single

velocity peak and secondary movement with one or more peaks. The primary submovement denotes the preprogrammed

portion of the movement whereas the secondary submovement denotes the visual feedback-controlled portion of themovement that accurately aligns the cursor with a precise target (Walker, Meyer & Smelcer, 1993). In addition to overall

pointing time, the analysis considered the relative duration and distance travelled in the primary and secondary

submovements as a function of gain, target distance and target size. Previous studies using this type of analysis have

yielded additional insights into the movement control mechanisms underlying pointing device performance (Douglas &

Mithal, 1997; Phillips & Triggs, 2001; Walker, et al., 1993).Repetitive Strain Injuries (RSIs) from mouse use are increasing, as are the associated costs to industry (Fagarasanu &

Kumar, 2003). Little is known about the upper extremity kinematics and biomechanics involved in operating a mouse,

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which may provide important information for the design and optimization of pointing devices. The data from the current

study should also shed some light on those issues.

2. METHOD

2.1 Participants

Ten introductory psychology students, six females and four males below age of twenty-four, participated in the study. Allparticipants were right-handed and reported having normal or corrected-to-normal vision. All participants were experienced

mouse users. They were given extra credit for their involvement in the experiment.

2.2 Materials

The experimental apparatus consisted of a PC running Linux, a 19-inch monitor running at 1400 x 1050 with a resolution of

100 DPI and a refresh rate of 75 Hz, and a Logitech Mouseman Traveler optical mouse (800 DPI). A program written in-house (using C and Perl) controlled the experiment and collected the data. The program sampled the mouse position (X and

Y) at a rate of 125 Hz and then transformed the values according to various criteria, such as the configured mouse

resolution and gain value, before presenting them on the screen as rescaled cursor position. There was no perceptible lag

between movement of the mouse and associated movement of the cursor.

2.3 Experimental Design

The experiment varied control gain (1, 2, 4, and 8), display target distance (18.75, 75, and 300 mm) and display target size

(1.5, 3, 6, & 12 mm) in a discrete pointing task. The specific amplitudes were chosen to manipulate the joints or sets ofjoints used to execute the response. For example, at a gain of 1, the 18.75 mm amplitude afforded a finger strategy, the 75

mm amplitude a wrist strategy, and the 300 mm an arm strategy. As gain increased, and thus mouse movement scale

decreased, there was a further shift in joint combinations. Consequently, there could have been five different joint-setcombinations: fingers, fingers-wrist, wrist, wrist-arm, or arm. Participants were free to employ any mousing strategy, or

combination of strategies, that was most comfortable to them. The strategy used for each amplitude-gain combination was

noted by the experimenter, and used for further analysis of joint segments.

2.4 Procedure

Participants sat approximately 60 cm from the screen with the mouse positioned for right-hand use and were free to utilize

the entire workspace (i.e., 36 X 18 desktop) to accommodate their various mousing strategies. They performed a routinetarget acquisition task with the cursor randomly appearing on the screen and the target displayed on the horizontal plane to

the right. The participants were asked to move the cursor from the starting position onto the target as quickly and

accurately as possible. They used the left mouse button to begin and end each trial. Participants heard a warning tone(beep) if the button was pressed while the cursor was outside of the target. Participants were given 3 warm-up trials with

each (amplitude by gain by target size) condition followed by 10 trials for each. Amplitude and gain was counterbalanced

across subjects, while target sizes were randomized.

2.4 Data Analysis

The data was collected as samples of time-stamped mouse coordinates, relative to the starting point of a trial. From this we

derived Pythagorean distance, then velocity. These in turn were smoothed by a weighted averaging process to remove high-

frequency noise.We devised a set of heuristics to extract movement characteristics from each trial. Any sudden acceleration at the end

of the trial was discarded, as we attributed this to inadvertent mouse motion due to clicking the button. The end of the data

stream was also cut off back to a point where velocity exceeded 2% of the trial peak velocity. The beginning of the datawas cut off in a similar way, but at 5% of peak velocity, to account for a slight jerk observed on many trials, which we

attributed to friction with the table surface.

Sub-movement boundaries were only counted when (a) the velocity amplitude (i.e the difference between a local

maximum and its neighboring minima, on the velocity curve) exceeded 15% of peak velocity, and (b) the bounding velocity

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minima fell below 50% of the local maximum and below 15% of the global peak velocity. Finally, on a decelerating region,

the deceleration was declared to stop whenever the deceleration rate fell below 25% of global peak velocity per second.

3. RESULTS

Because of the nature of the dependent measure (movement time) sphericity could not be assumed. A log10 transformation

of the dependent scores normalized the distribution and allowed the assumptions of normality, homogeneity of variance,

and sphericity to be met. Any trials deemed as outliers (i.e., 2 SD) were excluded from the analysis. Untransformeddescriptive statistics are shown in Table 1. For movement time, a 3 (amplitude) by 2 (target) by 4 (movement scale) within-

subject ANOVA was performed.

One of the research goals in the current study was to examine the effects of specific joint or joint-sets (i.e.,

mousing strategies) on movement time, proportion of movement time in primary submovement, and percentage of distancetraveled in primary submovement. To this end, three 5 (joint-set) X 2 (target) ANOVAs were performed on each dependent

measure. All pairwise analyses used a Bonferroni correct for multiple comparisons. Most frequently used joint-set for each

amplitude by movement scale condition is shown in Table 2. Descriptive statistics for movement time, proportion of time inprimary submovement, and percentage of distance traveled in primary submovement by joint-set are shown in Table 3.

Amplitude Target Size Movement Scale SD

18.75mm 1.5mm 1 1.002 0.232

2 0.935 0.139

4 0.989 0.215

8 1.151 0.348

12mm 1 0.537 0.122

2 0.474 0.113

4 0.461 0.067

8 0.499 0.098

75mm 1.5mm 1 1.383 0.223

2 1.211 0.132

4 1.180 0.2888 1.389 0.306

12mm 1 0.784 0.112

2 0.727 0.114

4 0.650 0.123

8 0.640 0.078

300mm 1.5mm 1 1.927 0.364

2 1.736 0.308

4 1.579 0.196

8 1.506 0.242

12mm 1 1.295 0.186

2 1.046 0.111

4 1.002 0.091

8 0.889 0.097

Note. N = 10.

Table 1. Descriptives for Amplitude by

Target Size by Movement Scale

3.1 ANOVA of Movement Time

All main effects were discovered to be significant; amplitude,F(2, 18) = 286.496,p < .001, partial 2 = .970; target size,F(1, 9) = 1018.914,p < .001, partial 2 = .991; and movement scale,F(3, 27) = 16.568,p < .001, partial 2 = .648. Apairwise comparison revealed that all amplitude and target conditions significantly differed from one another (p < .001). In

the movement scale condition, however, only the movement scale of 1 differed significantly from 2, 4 and 8 (p < .001),

Amplitude Movement Scale Joint-set

18.75mm 1 Fingers/Wrist

2 Fingers/Wrist

4 Fingers

8 Fingers

75mm 1 Wrist/Arm

2 Wrist

4 Wrist

8 Fingers/Wrist

300mm 1 Arm

2 Arm

4 Wrist/Arm

8 Wrist/Arm

Table 2. Joint-set for each Amplitude by

Movement Scale Conditions

Joint-set Target Size Movement Time % in Primary % Distance Traveled

Fingers 1.5mm 1.037 45.129 80.795

12mm 0.473 83.921 93.608

Fingers/Wrist 1.5mm 1.073 49.881 87.631

12mm 0.537 82.976 93.875

Wrist 1.5mm 1.179 48.834 90.367

12mm 0.678 79.839 94.457

Wrist/Arm 1.5mm 1.472 46.136 93.265

12mm 0.882 74.733 95.392

Arm 1.5mm 1.803 54.041 96.18512mm 1.155 83.520 98.245

Note. Movement time is reported on seconds.

y Joint-Set and Target Size

Table 3. Descriptives for Movement Time, Proportion of Movement Time in

Primary Submovement, and Percentage of Distance Traveled in Primary Submovement

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indicating that performance was worse for a movement scale of 1 versus all other scales. No other disparities were revealed

with the movement scales.In addition, all three two-way interactions were found to be significant; amplitude by target,F(2, 18) = 16.512,p

< .001, partial 2

= .647; amplitude by movement scale,F(6, 54) = 8.631,p < .001, partial 2

= .490; and target bymovement scale,F(3, 27) = 6.167,p = .002, partial 2 = .407. No significance was discovered for the three-way interaction.

3.2 Joint and Joint-sets

3.2.1 Movement Time

Both main effects were found to be significant; joint-set,F(4, 36) = 192.142,p < .001, partial 2 = .955; target,F(1, 9) =

902.809,p < .001, partial 2 = .990. A pairwise analysis revealed no disparities between the fingers and fingers-wrist joint-sets. However, all other joint-sets did differ significantly (p < .003). In addition, a significant difference was found of the

two-way interaction; joint-set by target,F(4, 36) = 16.107,p < .001, partial 2 = .642.

3.2.2 Proportion of Movement Duration in Primary Submovement

Both main effects were found to be significant; joint-set,F(4, 36) = 4.078,p = .008, partial 2 = .312; target,F(1, 9) =

1010.345,p < .001, partial 2 = .991. Pairwise analysis revealed that only the fingers-wrist joint-set differed significantlyfrom the wrist-arm (p = .025). Moreover, the wrist-arm joint-set differed from the arm (p = .035). A significant two-way

interaction was revealed, joint-set by target,F(4, 36) = 3.642,p = .014, partial 2 = .288.

3.2.3 Percentage of Distance Traveled in Primary Submovement

Both main effects were found to be significant; joint-set,F(4, 36) = 30.096,p < .001, partial 2 = .770; target,F(1, 9) =

82.390,p < .001, partial 2 = .902. Pairwise analysis found that the fingers strategy differed significantly from the wrist (p= .011), wrist-arm (p = .002), and arm (p < .001); fingers-wrist strategy differed from the wrist-arm (p = .002) and arm (p

< .001); wrist differed from arm (p = .005); wrist-arm differed from arm (p = .004); and arm differed from all limb segment

combinations (p < .005). A significant two-way interaction was revealed, joint-set by target,F(4, 36) = 14.586,p < .001,partial 2 = .618.

4. DISCUSSION

This experiment considered the impact of mouse movement scale and joint segment on the trajectories of rapid cursor-pointing movements made with a mouse. There was an interest in understanding how different aspects of control were

affected by these variables.

Mouse movement scale was found to have different effects on movement duration and kinematics depending ondisplay amplitude and target size. For targets located further from the start point on the display, movement times decreased

with a decrease in mouse movement scale. The decrease in the movement duration was localized primarily in the intitial

submovement, as illustrated in Figure 1. However, when the targets were located in near space, reducing the mouse

movement scale resulted in an increase in movement time, particularly for the small targets. The increase in movement

duration was associated with a significantly greater proportion of total movement time spent in the secondary submovementand a smaller proportion of total distance traveled in the primary submovement. This is illustrated in Figure 2. These data

indicate that participants had more difficulty making small-scale movements to precise targets. This may be attributable to

the use of the fingers to carry out the response.

We next examined the role of joint segment in controlling the mouse. Joint segment had a significant impact on mousemovement duration and kinematics. Movement time increased as larger joint sets were used to carry out the response.

Movements involving the arm and wrist were all affected in a similar manner by increasing precision requirements.

However, movements involving the fingers were affected to a greater extent. The increase in movement duration wasassociated with a significantly greater proportion of total movement time spent in the secondary submovement and a

smaller proportion of total distance traveled in the primary submovement. These results confirm that participants had more

difficulty making precise movements when controlling the mouse with their fingers.

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The primary limiting factor in mouse pointing appears to be the ability of the user to make small movements near the

targets. These types of movements heavily involve the fingers. This suggests a problem with the design of the mouse forclose or fine positioning. The fingers are naturally designed for fine-grain acts. However, the mouse affords a power grip

which limits the mobilization of the fingers during small-scale movements. Compare this, for example, to a stylus which

more fully exploits the high-resolution capacity of the fingers, resulting in better performance for small-scale movements

(Guiard, et al., 1999).

Contemporary computer interfaces make extensive use of the mouse for a variety of tasks such as selecting icons,navigating menus, or dragging guidelines. Mousing tasks that require cursor movements greater than two lateral screen

inches may result in extreme wrist deviations (i.e., exceeding 30% of maximum). Prolonged or repeated exposure to

deviated wrist postures can lead to musculoskeletal discomfort and injury (Bergqvtst, Wolgast, Nilsson, 1995; de Krom &

Kester, 1997; Matias, Salvendy & Kuckzek, 1998). Technically the size of the mouse movement can be scaled downrelative to the size of the cursor movement by increasing the gain, thus reducing the extent of left-and-right hand motion.

However, the results from this study suggest that this may not be an optimal solution as reduced movement scale could

result in a tradeoff between reduced motion and movement precision. Moreover, as static muscle work has also beenimplicated in the development of RSIs (Karlqvist, Hagberg & Selen, 1994), the longer periods spent in accurate cursor

placement at high gains may pose an additional health risk.

Increasing the gain to reduce the mouse movement scale presents an interesting and complex problem. It would

appear that for large-scale movements, users are able to take advantage of the shorter movement distance and reduced fine

Figure 1. Sample velocity profiles of large amplitude movements at a gain of2 (a) versus a gain of 8 (b). The primary and secondary submovements aredelineated by a solid vertical line. The horizontal dashed line shows the

maximum velocity in (b) for comparison purposes. Note the reduction in the

primary submovement time in (b).

(a) (b)

(a) (b)

Figure 2. Sample velocity profiles of small amplitude movements at a gainof 2 (a) versus a gain of 8 (b). Note the increase in the secondary

submovement time in (b).

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alignment time to acquire targets more quickly. However, for small-scale movements, the increased time required to

accurately place the cursor on a (small) target exceeds any potential time savings afforded by the shorter travel distance ofthe mouse. These results question the notion that there exists a single optimal gain for maximizing mouse performance.

Ultimately the solution may lie in the use of some type of non-linear gain system. Previous attempts to optimize the mouseusing non-linear gains were unsuccessful (e.g. Jellinek & Card, 1990; Tranke & Deutchmann, 1992). Perhaps a moresuccessful approach would enable the user to control the gain directly in real time (e.g. via a button on the mouse). This

requires further study.

5. REFERENCES

1. Bohan, M., Thompson, S.G., Scarlett, D.S., & Chaparro A. (in press). Gain and target size effects on cursor positioningwith a mouse. In Proceedings of the Human Factors and Ergonomics Society 47th Annual Meeting.

2. Balakrishnan, R., & MacKenzie, I.S. (1997). Performance differences in the fingers, wrist, and forearm in computerinput control. In Proceedings of ACM CHI97 Conference on Human Factors in Computing Systems, 303-310.

3. Bergqvtst, U., Wogast, E., & Nilsson, B. (1995). Musculoskeletal disorders among visual display terminal workers:individual ergonomic and work organziation factors. Ergonomics, 38: 763-776.

4. de Krom, M.C.T.F.M., Kester, A.D.M., Knipschild, P.G., & Spaans, F. (1997). Risk factors for caral tunnel syndrome.American Journal of Epidemiology, 132: 1102-1110.

5. Douglas, S.A., & Mithal, A.K. (1997). The ergonomics of computer pointing devices. London: Springer-Verlag.6. Fagarasanu, M., & Kumar, S. (2003). Carpal tunnel syndrome due to keyboarding and mouse tasks: a review.

International Journal of Industrial Ergonomcs, 31: 119-136.

7. Gibbs, C.B. (1962). Controller design: interactions of controlling limbs, time-lags, and gains in positional and velocitysystems. Ergonomics, 5: 385-402.

8. Guiard, Y., Beaudouin-Lafon, M., & Mottet, D. (1999). Navigation as multiscale pointing: Extending Fitts model tovery high precision tasks. In Proceedings of ACM CHI99 Conference on Human Factors in Computing Systems, 450-

457.

9. Jellinek, H.D., & Card, S.K. (1990). Powermice and user performance. In Proceedings of ACM CHI90 Conference onHuman Factors in Computing Systems, 213-220.

10. Karlqvist, L., Hagberg, M., & Sellen, K. (1994). Variation in upper limb posture and movement processing with andwithout mouse use. Ergonomics, 37: 1261-1267.

11. Lacquaniti, F., Ferrigno, G., Pedotti, A. Soechting, J.F., & Terzuolo, C. (1987). Changes in spatial scale in drawing andhandwriting: Kinematic contributions by proximal and distal joints. Journal of Neuroscience, 7: 819-828.

12. Langolf, G.D., & Chaffin, D.B. (1976). An investigation of Fitts law using a wide range of movement amplitudes.Journal of Motor Behavior, 8: 113-128.

13. Matias, A.C., Salvendy, G., & Kuczek, T. (1998). Predictive models of carpal tunnel syndrome causation among VDToperators. Ergonomics, 41, 213-226.

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Phillips, J.G., Triggs, T.J. (2001). Characteristics of cursor trajectories controlled by the computer mouse. Ergonomics,44: 527-536.

15. Trnkle, U. & Deutschmann, D. (1991). Factors influencing speed and precision of cursor positioning using a mouse.Ergonomics, 34: 161-174.

16. Walker, N. Meyer, D.E. & Smelcer, J.B. (1993). Spatial and temporal characteristics of rapid cursor-positioningmovements with electromechanical mice in human-computer interaction. Human Factors, 35: 431-458.

17. Zhai, S., Milgram, P., & Buxton, W. The influence of muscle groups on performance of multiple degree-of-freedominput. In Proceedings of CHI96 Conference on Human Factors in Computing Systems, 308-315.

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