Go to previous article
Go to next article
Return to 2000 Table of Contents
Edmund F. LoPresti, Jennifer Angelo, David M. Brienza,
Jonathan Sakai, and Lars Gilbertson
Departments of Bioengineering and Rehabilitation Science and
Technology,
University of Pittsburgh
Pittsburgh, PA 15260 USA
Email: edlopresti@juno.com
Many people are unable to operate a standard computer mouse
due to disabilities affecting their hands or arms. One
alternative is a head control interface, which translates head
movements into cursor movements. Head controls can allow many
people with disabilities to gain access to computers for
vocational, educational, or recreational purposes. Head
controls also have potential applications for people without
disabilities as a method for hands-free computing. Providing
alternatives to hand-operated controls can reduce the incidence
of repetitive strain injuries of the wrists, among other
benefits. Although the use of head controls by people with
disabilities is common, little research has been done on the
ergonomics of how people use head controls to operate
computers. Studies of human-computer interaction have focused
on the use of standard mice, keyboards, and other hand-operated
controls (Keates and Robinson, 1997). Research into the
ergonomics of head movements will contribute to the design of
more efficient head controls.
Research is particularly needed regarding the influence of
neck movement limitations. One such limitation is reduced
active neck range of motion. Active neck range of motion refers
to the number of degrees through which a person can move his or
her head (Figure 1). This neck range of motion may be reduced
as a result of a person's disability or a secondary condition
(Stanger et al. 1994). If neck range of motion is limited, a
person’s ability to operate a computer with head
movements may be reduced.
Figure 1: Range of motion for neck rotations: A. Flexion and
Extension B. Lateral Bending. C. Axial Rotation (supine
position). D. Axial Rotation (standing position). (Thibodeau
and Patton 1996 p. 298) This study investigates the ergonomics
of head control use by observing users with and without
disabilities, and with different ranges of neck movement.
Performance with head controls was evaluated for people without
disabilities, to obtain general information about how people
use these controls. Data was then collected for people with
cervical spinal cord injury, with different ranges of neck
movement. This data was compared to data for subjects without
disabilities, in order to study the effect of neck range of
motion limitations on the use of head controls.
Methods Fifteen subjects without physical disabilities and
three subjects with cervical spinal cord injury participated in
this study. Each subject attended two sessions, scheduled
between 3 and 14 days apart. During the first session, a
magnetic tracking/virtual reality based system (DeFrate et al.
1999) was used to measure neck movements in three dimensions.
Magnetic sensors (Flock of Birds, Ascension Technology,
Burlington, VT) tracked movements of the subject's head
relative to the torso. Subjects received visual feedback
concerning their movements through virtual reality glasses
(Virtual i-O (TM) Personal Viewing Glasses, Virtual i-O,
Seattle, WA). The glasses display a virtual environment in
which the subject is seated within a large wire-mesh sphere.
Cross-hairs in the foreground of the display move against the
wire-mesh background based on the movements of the subject's
head relative to his or her torso. As the subject performs head
movements, these movements are reflected by the motion of the
cross-hairs along the background. Each subject received a neck
range of motion evaluation using this system. The evaluation
included three trials each of flexion/extension, axial
rotation, and lateral bending.
During a second session, each subject performed two computer
exercises using head controls (HeadMaster (TM), Prentke Romich
Company, Wooster, OH). In a tracking task, the subject
attempted to keep the cursor on top of a circular target as the
target moved across the screen. Target movement patterns
included vertical, horizontal, and diagonal paths. The second
exercise was an icon selection task. At the beginning of the
exercise, a circle appears at the center of the screen. The
subject attempts to hold the cursor in this circle. A target
symbol then appears elsewhere on the computer screen. The
subject attempts to move the cursor to this target, and select
it by holding the cursor within the target (dwelling) for 0.5
seconds. The subject then returns the cursor to the circle at
the center of the screen. Targets appeared at one of three
distances from the center of the screen and in any of eight
directions from the center of the screen, for a total of 24
possible required movements.
Each subject performed four repetitions of the tracking task,
with eight targets presented in each repetition. All but one
subject performed 16 repetitions of the icon selection task,
with 24 targets presented in each repetition. One subject with
spinal cord injury only completed six repetitions of the icon
selection task due to fatigue.
Results Subjects without Disabilities Fitts' Law has been a
valuable tool in studying human-computer interaction (Olson and
Olson 1990). Fitts' Law describes a relationship between
movement time and target size and distance:
T = A + Blog2(2D/W) (1) where T = movement time, D = distance
from starting position to center of target, W = target width, A
is a constant term, and the coefficient B is the Fitts’
Law slope (Fitts and Peterson, 1964). Fitts' Law has been used
to describe the speed with which people are able to use
hand-operated controls such as the mouse or keyboard. Previous
research indicates a Fitts’ Law relationship may apply to
head movements as well (Jagacinski and Monk 1995, Lin et al.
1992). For this study, regression analysis of the icon
selection data for subjects without disabilities indicates a
Fitts' Law-type relationship between movement time and target
distance:
T = 0.369 + 0.300log2(2D/1.5) (2) Because only one icon size
was used, target width W=1.5 cm for all targets. Therefore the
data can only indicate a Fitts’ Law-type relationship
between movement time and distance, but not between movement
time and target size. For each subject, half the subject's data
was used to calculate this equation, and the remaining half of
the data was used to test the equation's accuracy. The mean
absolute-value error between the equation's predictions and the
actual data was between 19.90% and 32.79%, with a 27.24% mean
error across subjects.
One possible source of error for this model could be the
effect of movement direction on movement time. Paired t-tests
for the difference in mean movement times for each subject
indicated that vertical movements were faster than horizontal
(p=0.014) or diagonal movements (p=0.0009).
The effect of movement direction on cursor deviation was also
considered. For the tracking task, cursor deviation is used to
describe the difference between the path taken by the target
symbol and the actual path along which the subject moved the
cursor. Specifically, "cursor deviation" is used to refer to
the root-mean-square difference between the actual path taken
by the cursor and the straight-line path taken by the targets.
A paired t-test indicates that cursor deviation was higher for
diagonal targets compared to horizontal and vertical targets
(p=0.0031).
Subjects with Disabilities Data for three subjects with
cervical spinal cord injury was compared to data for the 15
subjects without disabilities. Active neck range of motion data
is summarized in Table 1. Subject E01 has neck ranges of motion
similar to the subjects without disabilities. Subject E03 has
somewhat reduced neck range of motion, while subject E02 has
severely reduced neck range of motion. Subjects' injury levels
are shown in Table 1. Also, subject E01 has a spinal fusion
between the C5 and C7 vertebrae and subject E03 has stabilizing
rods between the C3 and T2 vertebrae.
Subject Injury Level Flexion/ Extension ROM Total Axial
Rotation ROM Total Lateral Bending ROM C01-C15 None 118.42
± 15.00 137.87 ± 13.60 87.35 ± 10.65 E01
C7 103.31 ± 5.55 136.90 ± 2.57 90.06 ±
4.74 E02 C1 2.72 ± 0.28 7.64 ± 0.07 6.70 ±
0.61 E03 C5 71.48 ± 2.07 98.41 ± 1.00 37.06
± 2.68
Table 1: Range of Motion (ROM) in degrees. Mean (± st.
dev.) across subjects without disabilities (C01-C15). Mean
(± st. dev.) across trials for three subjects with
cervical spinal cord injury (E01-E03). Measures of these
subjects’ performance on the computer exercises are
summarized in Table 2. The measures shown are:
Icon Selection Time: time elapsed between when a target icon
appeared on the screen and when it was successfully selected by
the subject, not including the 500 ms dwell time.
Error for Fitts’ Law Model: mean absolute-value error
between observed icon selection times and the selection times
predicted by the Fitts Law equation derived from data for
able-bodied subjects (Equation 2). Icon Selection Accuracy: the
number of icons successfully selected, divided by the number of
icons presented as targets.
Percent Distance Subject Followed Moving Target: For the
tracking task, the straight-line distance the cursor moved
across the computer screen, as a percentage of the
straight-line distance traveled by the target. Used as a
measure of the portion of the screen accessible to the subject.
Data greater than 100% indicate that the subject moved the
cursor beyond the farthest position of the target.
Subject Icon Selection Time (seconds) Error for Fitts’
Law Model Icon Selection Accuracy Percent Distance Subject
Followed Moving Target C01-C15 1.18 ± 0.12 27.24%
± 3.59% 99.93% ± 0.14% 99.77% ± 3.14% E01
1.91 ± 0.98 34.98% 97.92% 100.10% ± 8.13% E02
5.01 ± 2.46 72.61% 16.07% 49.76% ± 31.30% E03
1.02 ± 0.40 36.06% 100% 98.16% ± 4.66% Table 2:
Results of Computer Exercise. Mean (± st. dev.) across
subjects for subjects without disabilities (C01-C15). Mean
(± st. dev.) across trials for three subjects with
cervical spinal cord injury (E01-E03).
Discussion The results of this study provide information about
the effects that movement distance and direction have on speed
and efficiency. Understanding these effects can contribute to
the design of better user interfaces for head control users, as
well as head controls themselves. First, the results indicate
that head movements can be described by a Fitts' Law equation.
This equation could then be used to predict how quickly head
control users could work with a particular computer software
product, based on the cursor movements required to by the
software. Different software, or different arrangements of
objects on the screen, could then be compared in order to
select the arrangement that will allow the user to work most
quickly. The results also indicate that vertical cursor
movements can be performed more quickly than horizontal or
diagonal movements, and that people have more difficulty
following a diagonal line using head movements compared to a
horizontal or vertical line. These effects of movement
direction could be taken into account in the design of software
for head-control users, and in the arrangement of icons on the
computer screen.
In addition to guiding the design and selection of computer
software, these results could be used in the design of head
control hardware. Information about the effects of movement
distance and direction on user's performance could allow
manufacturers to design head controls that accommodate a user's
movement patterns.
In addition to obtaining general ergonomic information about
head controls, it is important to consider the effect of
disabilities on the effectiveness of a head control system.
Subject E02, who has severely limited neck range of motion, had
low accuracy in the icon selection task. He also had difficulty
following the tracking targets across the screen, on average
only reaching halfway across the screen. Subject E01 also had
reduced performance with the head controls, despite having
normal neck ranges of motion. This subject could move the
cursor across the screen, as shown by his performance in the
tracking task and his high accuracy in the icon selection task.
However, his accuracy in the icon selection task was lower than
that achieved by the control subjects, and the times needed to
acquire icons were longer. This may be due to neck stiffness or
some other symptom not considered in this study. Subject E03,
on the other hand, despite somewhat reduced neck range of
motion, was able to perform the tracking and icon selection
exercises with performance comparable to the subjects without
disabilities.
The results from subjects with disabilities also shows the
limitations of human-computer models, such as the Fitts' Law
equation described above. This Fitts' Law model was developed
based on the data for subjects without disability. Based on the
results shown in Table 2, it does not seem to be as successful
at predicting the performance of subjects with disabilities
compared to subjects without disabilities. Ideally, models of
human-computer interaction would be developed to account for
disabilities, and thus be applicable for all computer
users.
Conclusion A study was conducted to analyze head movements in
the context of two computer exercises: an icon selection task
and a tracking task. The results indicate relationships between
movement distance and movement time; movement direction and
movement time; and movement direction and cursor deviation. The
results also show that disabilities such as cervical spinal
cord injury can have an impact on the ergonomics of head
controls. In further research, data will be collected for 12
more individuals with disabilities, allowing further analysis
of the effect of disabilities on the subjects' performance with
head controls. Based on these data, models of the
human-computer interface will be developed which address the
use of head controls by people with and without disabilities.
These results will also be used in the development of head
control software that can adapt to a user's available neck
range of motion.
Acknowledgments This research was funded by the Microsoft
Corporation and the Whitaker Foundation.
References DeFrate, L.E., Sakai, J.L., Gilbertson, L.G., Moon,
S-H., Nishida, K., Donaldson, W.F. III, Kang, J.D., and Woo, S.
L-Y. 1999. Magnetic Tracking/Virtual Reality Based System for
Comprehensive Kinematic Assessment of the Cervical Spine. ASME
Summer Bioengineering Conference. 1999.
Fitts, P.M. and Peterson, J.R. (1964). Information Capacity of
Discrete Motor Responses. Journal of Experimental Psychology.
67(2):103-112. Jagacinski, R.J. and Monk, D.L. 1985.
Fitts’ Law in Two Dimensions with Hand and Head
Movements. Journal of Motor Behavior. 17(1):77-95. Keates, S.
and Robinson, P. 1997. User Performance Modeling and Cognitive
Load. Proceedings of the RESNA 1997 Annual Conference. 342-344.
Lin, M., Radwin, R.G., and Vanderheiden, G.C. 1992. Gain
Effects on Performance Using a Head-Controlled Computer Input
Device. Ergonomics. 35(2):159-175.
Olson, J.R. and Olson, G.M. 1990. The Growth of Cognitive
Modeling in Human-Computer Interaction Since GOMS.
Human-Computer Interaction. 5:221-265.
Stanger, C., Phalangas, A., and Cawley, M. 1994. Range of Head
Motion and Force of High Cervical Spinal Cord Injured
Individuals for the Design of Test-bed Robotic System.
Proceedings of the 1994 ICORR Conference. 37-40. Thibodeau,
G.A. and Patton, K.T. 1996. Anatomy and Physiology Third
Edition. Mosby, St. Louis.
Go to previous article
Go to next article
Return to 2000 Table of Contents
Return to Table of
Proceedings