Mobility Metrics for Manual Wheelchair Use in Everyday Life
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Mobility Metrics for Manual Wheelchair Use in Everyday Life
Ricardo A. Lopez, M.S., Sharon E. Sonenblum, Ph.D. and Stephen H. Sprigle, Ph.D., P.T.
Rehabilitation Engineering and Applied Research Laboratory, Georgia Institute of Technology
Atlanta, GA 30332
ABSTRACT
In this study, a method for the robust detection of manual wheelchair movement with accelerometer-
based data logging is used to obtain metrics of wheelchair mobility that complement mean- and total-
distance-only based measurements. It is found that these metrics, that include distance, time and number
of activity bouts, provide better understanding of everyday use of manual wheelchairs by shedding more
light into how users operate their wheelchairs in their daily lives.
KEYWORDS
Wheelchair; mobility ; rehabilitation; activities of daily living.
BACKGROUND
In general literature on wheelchair usage is limited and mean distance is the main quantity of
interest that is reported(1,2). A description of wheelchair use in everyday activities that looks beyond total
and mean wheeled distances has been recently proposed and shown appropriate for power wheelchair
users(3). In (3), the need for a more thorough measure of power wheelchair use in the community was
established and focused on three metrics: distance, time and number of bouts of activity. A bout of
activity is defined as a measure of mobility that includes travel between intentional activities, as opposed
to a bout of movement, which includes any possible wheelchair movement, be it activity-oriented or not.
Together, these metrics can provide information about power wheelchair usage characteristics that can
impact the design, policy and prescription of these devices and that cannot otherwise be captured
accurately by distance-only based metrics. Therefore, this study aims to examine the application of these
additional metrics to manual wheelchairs, and assess their appropriateness to the manual wheelchair
scenario, especially when compared with more traditional total distance and duration measures.
METHOD
A convenience sample of 12 adults, ages 23 to 68 (median 39.5), with some affiliation to the local
spinal cord injury (SCI) rehabilitation center who used manual wheelchairs as their primary mobility
devices were recruited for this study with IRB approval. Subjects signed informed consent forms prior to
beginning their participation in the study. Each of the participant’s wheelchairs was instrumented with a
solid-state, triaxial, MEMS-based acceleration logger with a ±2g range, at a sampling rate of 10Hz. This
logger was mounted on one of the wheels –as seen in Figure 1– for periods between 1 to 2 weeks
(depending on subject availability). This method of measuring manual wheelchair movement has been
described previously(4) and offers a rate of accuracy better than 90% across manual wheelchair models
and indoor and outdoor surfaces.
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Figure 1 goes here: Instrumented wheel
Mobility Metrics for Manual Wheelchair Use in Everyday Life
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Only the two components of acceleration coplanar with the wheel were analyzed (X and Y
components as seen in Figure 1). The collected acceleration data was processed by the method in (4) to
produce detailed information about bouts of movement, i.e., detect when the wheelchair was moving, and
further processed to identify bouts of activity. The criteria for considering a movement bout a “bout of
activity” include a minimum stop time of 15 seconds and a start time of at least 5 seconds. These
thresholds were determined empirically as described in previous work (3).
Once bouts of activity were identified, statistics about their duration, distance and speed were also
gathered, including mean, standard deviation, range and median. These were later aggregated to generate
median and range day information. Total duration and distance wheeled were also computed in order to
correlate them with the median, range and number of bouts of activity figures.
RESULTS
Table 1 shows a list of median and range day mobility parameters for our subject population,
sorted by ascending wheeled distance. Two important observations can be made. First, an increase in
distance does not imply an increase in the number of activity bouts. Although not showed explicitly in
Table 2, the same disparity can be seen between wheeling time and activity bouts. Second, the ranges of
variation are very wide for all parameters. Therefore, the day-to-day spread in these parameters within
subjects is large.
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Table 1 goes here: Mobility measures
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Median day statistics have been compiled and are shown in Figure 2 as histograms. Of importance
is the examination of the total distance wheeled. As seen in the top left corner of Figure 2, this distribution
is skewed towards zero, though to a lesser degree than in the case of power wheelchairs (4). Regardless, a
mean distance figure alone would not be representative of typical behavior for our subject population.
This confirms the need for additional metrics.
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Figure 2 goes here: Mobility stats
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The top right corner of Figure 2 shows a histogram of number of activity bouts. This is the
distribution that is closer to a normal distribution of all the ones examined in this study. This is different
than in the power wheelchair case (3), where the number of bouts is skewed towards zero. What this
means is that in our subject population tends to engage in more bouts of activity daily than their power
wheelchair counterparts. The fact that the mean (n=103) and the median (n=106) are close, make the
number of bouts an attractive indicator for mobility in manual wheelchairs.
The bottom left section of Figure 2 shows the distribution of total wheeling time across
participants. Again, this histogram is skewed towards zero, resembling the trend observed with power
Mobility Metrics for Manual Wheelchair Use in Everyday Life
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wheelchairs in (3). Analogously, it can be concluded that wheeling time alone is not a good indicator of
wheelchair activity, as it clearly doesn’t correlate with the number of bouts distribution.
Having looked into distance and bouts, it seems appropriate to examine speeds. The research
question is if the speed of a bout depends on the purpose of such bout. Are bouts of activity, overall, faster
or slower than simple bouts of movement? The “Bout Speed” plot in Figure 2 shows that the speeds for
bouts of activity and bouts of movement are generally close, within 20% of one another. However, there
were two cases of subjects in which the difference was 21% and 28%. This finding makes it necessary to
carry further studies that look into demographic factors.
DISCUSSION
As reflected by the wide ranges in number of bouts, distances and durations of wheeling, it is clear
that there is no single typical behavior of full-time manual wheelchair use. Therefore, the conclusion that
distance or time alone are insufficient to describe mobility for power wheelchair users(3) can now be
extended to manual wheelchair users, as evidenced by the poor correlation between wheeled distance or
total time spent wheeling to number of bouts as seen in Figure 2. An interesting observation was the
relative normality of the number of activity bouts distribution, but this finding will have to be verified in a
larger population before embracing it as the dominant mobility measure. Future work should also study
the need for combining some of these additional metrics in order to enable clinicians, manufacturers and
policymakers to make more informed decisions –that take into account actual wheelchair usage–
regarding the design and prescription of manual wheelchairs. Even though we believe that the metrics
presented in this study apply to manual wheelchairs based on our measurements, further work is needed to
research possible deeper correlations to demographic factors. Also, possible differences between indoor
and outdoor manual wheelchair activity need to be studied in light of these additional measures of
mobility.
REFERENCES
1. Cooper R.A., Thorman T., Cooper R., et al., “Driving characteristics of electric-powered wheelchair
users: how far, fast, and often do people drive?,” Arch Phys Med Rehabil 2002;83:250-5.
2. Fitzgerald SG, Arva J, Cooper RA, Dvorznak MJ, Spaeth DM, Boninger ML. “A pilot study on
community usage of a push-rim activated, power-assisted wheelchair. Assist Technol 2003;15:113-9.
3. Sonenblum, S.E., Sprigle, S., Harris, F.H., Maurer, C.L., “Characterization of Power Wheelchair Use in
the Home and Community,” Archives of Physical Medicine and Rehabilitation 89(3), 486-91, 2008.
4. Sonenblum, S.E., Caspall, J., et al, “An Analysis Method for Detecting Manual Wheelchair
Movement.” Presented at the BMES Annual Meeting. 2009. Pittsburgh, PA.
ACKNOWLEDGMENTS
Funding was provided by NIDRR through the RERC on Wheeled Mobility (H133E080003). The opinions
contained in this article do not necessarily reflect those of the U.S. Department of Education.
Mobility Metrics for Manual Wheelchair Use in Everyday Life
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GRAPHICS PAGE
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Figure 1: Instrumented wheel
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LEGEND
Acceleration logger mounted on a manual wheelchair wheel. Two of the measuring axes (X’ and Y’) are
parallel to the wheel plane (X and Y). Only these two axes are analyzed, while acceleration along the third
axis, perpendicular to the wheel plane, is not used.
ALTERNATIVE TEXT
This figure depicts a spoked wheel with a data logger attached to a single spoke. The global coordinate
system includes the X-axis (horizontal) and Y-axis (vertical). The coordinate system of the wheel is
parallel to the global system in this figure. The axes of the data logger (X’’, Y’’) are oriented tangentially
and radially, respectively.