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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING , VOL. 11, NO. 3, SEPTEMBER 2003
Multidimensional EMG-Based Assessment of
Walking Dynamics
Ben H. Jansen, Senior Member, IEEE, Vonda H. Miller, Demetrios C. Mavrofrides, and Caroline W. Stegink Jansen
Abstract—The electromyogram (EMG) provides a measure of a
muscle’s involvement in the execution of a motor task. Successful
completion of an activity, such as walking, depends on the efficient
motor control of a group of muscles. In this paper, we present a
method to quantify the intricate phasing and activation levels of a
group of muscles during gait. At the core of our method is a multidimensional representation of the EMG activity observed during
a single stride. This representation is referred to as a “trajectory.”
A hierarchical clustering procedure is used to identify representative classes of muscle activity patterns. The relative frequencies
with which these motor patterns occur during a session (i.e., a series of consecutive strides) are expressed as histograms. Changes
in walking strategy will be reflected as changes in the relative frequency with which specific gait patterns occur. This method was
evaluated using EMG data obtained during walking on a level and
a moderately-inclined treadmill. It was found that the histogram
changes due to artificially altered gait are significantly larger than
the changes due to normal day-to-day variability.
Index Terms—Gait analysis, hierarchical clustering, multichannel electromyogram (EMG) analysis, trajectory analysis.
I. INTRODUCTION
E
LECTROMYOGRAPHY (EMG) provides a record of the
electrical activity generated by contracting muscles during
movement activities. Dynamic EMG analysis is routinely used
to assess muscle function during gait [1]. Of primary interest are
the onset, duration, and amplitude of the phases of activity of the
leg muscles during a stride. A variety of procedures have been
developed to measure these features, including threshold-based
methods [7] and functional approximation methods [2], [10].
Most approaches start from the linear envelope (LE), which is
obtained by full-wave rectification and lowpass filtering of the
EMG. LEs are often ensemble-averaged over multiple strides to
reduce the variability. Various methods have been used to analyze the LE, including Fourier series [15], Karhunen–Loeve expansion [14], factor analysis [9], and correlation measures [6].
Pattern recognition methods, operating on the aforementioned
features, have been used to quantify changes in gait patterns. Examples include the use of clustering techniques [15] and neural
networks [12].
Manuscript received May 4, 2001; revised April 16, 2002.
B. H. Jansen and V. H. Miller are with the Department of Electrical and
Computer Engineering, University of Houston, Houston, TX 77204-4005 USA
(e-mail: bjansen@uh.edu).
D. C. Mavrofrides was with the Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204-4005 USA. He is currently
with Sensorwise Inc., Houston,TX 77042-4119 USA.
C. W. Stegink Jansen is with the Department of Physical Therapy, School
of Allied Health Sciences, University of Texas Medical Branch, Galveston, TX
77555-1144 USA.
Digital Object Identifier 10.1109/TNSRE.2003.816865
Techniques for the objective, quantitative assessment of
changes in movement patterns leave much to be desired because
they do not include analysis of the coordinated activity of a
group of muscles. Even when multiple channels are recorded
simultaneously, each channel is usually analyzed one at a
time (see, for example, [5]). As a result, the complex and
intricate pattern with which the muscles contract and relax
during a motor task is not quantified. True multichannel EMG
analysis methods, in the sense that data from more than one
EMG channel are evaluated simultaneously, are rare. One
such approach was presented by Davis and Vaughan [4], who
plot the activity of one muscle against another, resulting in a
two-dimensional (2-D) graph. These graphs were evaluated
qualitatively only, and relationships between more than two
EMG channels at a time were not considered.
In this paper, we present a method that provides a quantitative assessment of the phasing and activation pattern of multiple
muscles at once, and on a stride-by-stride basis. This method, referred to as the Structural EMG Analysis (SEA) method, will be
useful to quantify the changes in gait patterns following surgery
or rehabilitation. The procedure is outlined in the next section,
and results obtained from gait data are presented in Section III.
A discussion concludes this paper.
II. SEA METHOD
The structural EMG analysis (SEA) method operates on multichannel EMG data obtained from ambulating subjects. We selected to study EMG during gait because it is quasi-periodic and
can easily be separated into individual gait cycles. The latter is a
prerequisite for the method. In addition, we operate on the linear
envelope of the EMG data. In the present implementation, the
method’s capability is limited to four simultaneously recorded
EMG channels, but expansion to a larger number of channels is
straightforward.
The first step in the SEA method is to transform multichannel
EMG recordings to trajectories for each single stride. Next,
representative gait cycles—templates—that account for most
of the variability in the data, are determined using a clustering
algorithm. In the third stage, histograms are formed expressing
the frequency of occurrence of each template in a session.
These histograms provide insight in the structure of EMG
activity during a repeated task, hence, the name of our method.
Each of the steps that comprise the SEA method is described in
detail, together with a procedure to compare histograms.
Trajectory Construction: The simultaneous activity of
contraction and relaxation between muscles is represented
by way of trajectories in a -dimensional Euclidean space
1534-4320/03$17.00 © 2003 IEEE
JANSEN et al.: MULTIDIMENSIONAL EMG-BASED ASSESSMENT OF WALKING DYNAMICS
Fig. 1. Three panels on the left show the normalized linear envelope of the
EMG recorded from the Biceps Femoris (BF), Tibialis Anterior (TA) and
Gastrocnemius (G) during one cycle of horizontal gait (black line) and inclined
gait (red line). The corresponding trajectories for BF versus TA, and TA versus
G are shown in the two panels on the left. All data are from one subject.
. Trajectories are constructed by placing different EMG
are identified by
channels along each axis so that points in
, where
represents sample
of the EMG of muscle .
Trajectories are formed from single stride data. An example
is presented in Fig. 1, which shows the linear envelope of the
EMG produced by the BF, G, and TA during one gait cycle,
and the (2-D) trajectories obtained from plotting BF versus TA,
and TA versus G. Striking differences between the BF versus
TA trajectories for horizontal and inclined gait can be observed,
while more subtle differences are apparent in the TA versus G
trajectories.
Template Formation: A clustering method was developed to
group strides with similar trajectories. Central to the clustering
procedure is a metric to measure the similarity between two
and . A point-by-point Euclidean distance is
trajectories
not appropriate for capturing overall curvature and flow similarity between two trajectories. Instead, we developed the metric
which involves a search along the two trajectories for
regions of similar curvature and flow. This similarity measure
is computed using
(1)
with
(2)
is computed by taking
points on traThe similarity
jectory that are samples apart, and finding the points on
points on (these points
trajectory that are closest to the
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are vectors in -dimensional space). Specifically, is determined
is minimized for
such that
. The variable
defines a window on
to which the search is confined. This window ensures that similar phases of two gait cycles are compared with each other. The
,
similarity measure is nonsymmetric, i.e.,
points on will have
because there is no guarantee that all
points on . Therefore, both
been compared to all
and
are computed and the larger of the two serves as
the measure of similarity.
depends on the value of the
The definition of
, the number of points skipped
, and
window length
, the number of (consecutive) points used. After extensive
experimentation, reported in [8], it was found that acceptable
,
, and
for
.
values are
The search for templates proceeds by first identifying that gait
cycle which, on average, is closest to all other gait cycles. Gait
cycles that are too far from this initial template are searched
again to find the next stride that is closest on average to all the
other strides that were not grouped with the first template. This
search method continues until all gait cycles are grouped with a
particular template.
The basic approach is implemented as follows. A distance
is obtained by computing the similarity
matrix
between each pair of strides among all the sessions used for
from cycle to all other
training. The average distance
gait cycles is computed using
(3)
,
where is the total number of gait cycles. The smallest
denoted as , is determined, and the gait cycle associated with
this distance is selected as the first potential template. Cycles
from the first potential template
that differ no more than
are removed, resulting in a smaller matrix. This process is repeated until only two gait cycles remain, and the one with the
is selected as the first template. Cycles that differ
largest
from this template by no more than a threshold—based on the a
priori mean distance of the least variable session—are removed.
Again, average distances are computed for this smaller-sized
distance matrix and the process is repeated until no cycles remain.
An example of this template finding procedure is shown in
Fig. 2. In step 1, a sample distance matrix is shown with three
trajectories from two different sessions A and B along with the
average distance of each trajectory to all other trajectories. In
, so A2
this example, the smallest average distance is
is selected along with all the trajectories that are less than 6.4
away from A2 to form the reduced distance matrix shown in step
2. This smallest average distance step is repeated on the reduced
matrix as shown in step 3 and continues until a 2 2 matrix is
found as shown in step 4. Of these two possible templates, the
is selected. Here,
one with the largest distance
and
, hence, A3 is selected as the first template.
Then a preset threshold—based on the a priori mean of the least
variable session—is used to select trajectories that are further
away from the template just found by this threshold value (we
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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING , VOL. 11, NO. 3, SEPTEMBER 2003
Fig. 2. Example to illustrate the procedure for template selection. A1 through
B3 indicate gait cycles, and the matrices show the similarity index between
templates. For example, the top-left matrix shows that the similarity between A1
and A2 is 13. Numbers to the right of the matrices are row averages. During each
successive step of the clustering procedure, similar gait cycles are combined,
and a new similarity matrix is produced. See text for detailed explanation.
used 7.0). In this example, B2 and B3 remain, from which B3
is selected by applying the aforementioned rules.
Histogram Formation: Once the templates for an individual
have been identified, a classification stage follows, where all the
gait cycles from a session are compared to the templates using
the previously described similarity (distance) measure. A count
is taken of how often each template is the most similar to the
gait cycles in question. In this way, we obtain a histogram for
each session, expressing how frequently gait cycles resemble
each template in that session. Since the number of strides may
vary from session to session, we normalize each histogram by
dividing by the number of strides in the session and multiplying
by hundred.
Histogram Comparison: Changes in gait between sessions
or between level- and inclined-walking will show up as differences in these histograms. These differences are quantified
using the sum of the squared differences, normalized to the
number of templates, defined as
(4)
and
are the two
where is the number of templates, and
histograms to be compared. A t-test can be used to determine
values when comparing histograms obtained under
if the
values when
similar gait conditions differ significantly from
comparing histograms obtained during different gait conditions.
III. RESULTS
A. Data Collection and Preprocessing
EMGs were obtained from two normal volunteers (male subject A and female subject B), who were asked to walk on a
treadmill at a self-selected, constant speed. Subject A selected a
speed of 2.0 miles/hour, and subject B walked at 3.2 miles/hour.
Surface electrodes were used to record the EMG from the
Vastus Lateralis (VL), BF, TA, and in the right leg. The electrodes were positioned according to standardized placements
[11], and an oscilloscope was used to verify that activity from
the intended muscle was obtained. Toe-off and heel-strike in-
formation was obtained using two foot switches, placed on the
outside of the heel and the head of the first metatarsal bone,
respectively. Data recorded on two different days (7/25/97 and
8/1/97) for subject A and three different days (7/23/97, 7/25/97,
and 8/1/97) for subject B were analyzed. Recordings were made
during morning and repeated afternoon sessions on each day.
During each morning and afternoon session, ten consecutive
data collection runs took place. In each run, data from about
20 contiguous steps were collected once the subject had gained
balance and a steady gate after stepping on the moving treadmill. The first five runs were done on a level treadmill, and the
next five runs with the treadmill inclined to a 5% grade. These
two conditions were chosen to induce two indisputably different
gait conditions.
All data were digitized at 752 Hz, and the foot switch data
were used to separate the gait cycles into single strides. The
four EMG channels were rectified and lowpass filtered (10-Hz
cutoff) to obtain estimates of the amplitude envelope. The linear
envelopes were normalized by subtracting the session mean and
dividing by the session standard deviation to allow for intersession comparisons.
B. Horizontal Versus Inclined Gait EMG Differences
We first present results showing that the EMG patterns obtained during horizontal gait differ consistently from the EMG
during inclined gait. Data from one horizontal and one inclined
session, collected on 7/25 from subject A were used to find the
templates. Five templates, three for horizontal and two for inclined gait, were obtained. These templates were used to classify
strides from another horizontal and inclined session obtained on
the same day (referred to as session 7/25-t), with identical electrode locations, and two horizontal and inclined sessions obtained on 8/01, referred to as 8/01-I and 8/01-II, respectively.
The exact electrode locations for the 8/01 data may differ from
the 7/25 session, but the electrode locations did not change between the 8/01-I and 8/01-II sessions.
Only four of 61 gait cycles obtained during level gait were
found to be more similar to a template derived from inclined
gait that to horizontal gait templates. Just two of 64 inclined
gait cycles were assigned to horizontal templates. This suggests
that the templates are indeed characteristic for the gait condition
from which they were derived.
Histograms for each of the sessions, showing how many gait
cycles within a session resembled a particular template, are presented in the top panel of Fig. 3. As one may see, the histograms
for horizontal gait sessions are very much different from the histograms for the inclined sessions. However, the differences between the histograms for the same walking condition are fairly
small. Specifically, the differences between the horizontal and
inclined histograms are very large, as one may see. The two
7/25 sessions produce virtually identical histograms. The histograms of the 8/01 sessions differ somewhat from the 7/25 sessions, especially for the horizontal sessions, but the differences
between the horizontal and inclined sessions remains large. The
differences between the histograms were quantified using the
measure of (4). The
values for the horizontal to inclined,
horizontal to horizontal, and inclined to inclined histogram comparisons are presented in Table I. A paired t-test indicated that
JANSEN et al.: MULTIDIMENSIONAL EMG-BASED ASSESSMENT OF WALKING DYNAMICS
297
Fig. 3. Histograms for subject A (left) and subject B (right). Dark bars: horizontal gait. Light bars: inclined gait. Each histogram shows the percentage of gait
cycles within a session that resembled a particular template. Templates are identified by a numeral followed by an “h” or an “i” (to indicate horizontal or inclined
gait template, respectively), followed by a number (e.g., h7 is a horizontal template). Sessions are indicated by month-date, followed by “h” or “i”, identifying
them as horizontal or inclined gait, respectively.
TABLE I
PRINCIPAL DIAGONAL (i.e., FROM UPPER LEFT TO LOWER RIGHT) OF THE
UPPER MATRIX SHOWS THE
VALUES OF SUBJECT A FOR THE COMPARISON
BETWEEN THE HISTOGRAMS OF THE HORIZONTAL AND INCLINED GAIT
VALUES
CONDITIONS FOR EACH OF THE THREE TEST SESSIONS.
FOR HORIZONTAL TO HORIZONTAL COMPARISONS BETWEEN SESSIONS
ARE SHOWN ABOVE THE PRINCIPAL DIAGONAL, AND THE NUMBERS
BELOW THE PRINCIPAL DIAGONAL SHOW THE INCLINED TO INCLINED
HISTOGRAM COMPARISONS. MEANS AND STANDARD DEVIATIONS OF
FOR HORIZONTAL VERSUS INCLINED (H VS. I), HORIZONTAL VERSUS
HORIZONTAL (H VS. H) AND INCLINED VERSUS INCLINED (I VS. I) ACROSS ALL
SESSIONS ARE SHOWN IN THE LOWER MATRIX
D
D
D
Fig. 4. EMG from VL, BF, TA, and G of the dominant templates of subject A,
during horizontal gait (top panel) and inclined gait (bottom panel).
the differences between level and inclined gait are significantly
0.01 .
larger than between identical conditions
The linear envelopes of the EMG associated with the most
prevalent horizontal and inclined template are presented in
Fig. 4. As one may see, the BF activity is somewhat reduced
toward the end of the stride for inclined as compared to
horizontal gait. The primary differences between horizontal
and inclined EMG patterns are changes in the timing of the
onset of EMG activation. The most striking difference can be
observed in the TA, which reaches maximal activity around
90% of the horizontal stride length, but during inclined gait this
maximum is reached much earlier, namely around 70% of the
stride length. Smaller shifts can also be seen for the G (25%
for horizontal to 35% for inclined), BF (95% horizontal, 88%
inclined), and VL (95% horizontal, 100% inclined).
A similar experiment was conducted for subject B. One horizontal and one inclined session collected on 8/01 were used
to derive templates. Five templates were found—four for horizontal strides and one for inclined gait. Data from another session collected on 8/01 (with identical electrode locations, and
referred to as 8/01-t), and one pair of horizontal and inclined sessions each from 7/23 and 7/25, were classified with these templates and the resulting histograms are presented in the bottom
panel of Fig. 3. Again, as one can see, the differences between
the horizontal and inclined histograms are large, and there are
only minor differences between the histograms of 8/01, 7/23 and
7/25. Also, only very few horizontal strides were assigned to
values for the horinclined templates, and vice versa. The
izontal to inclined, horizontal to horizontal, and inclined to inclined histogram comparisons are presented in Table II. A paired
t-test indicated that the differences between level and inclined
gait are significantly larger than between identical conditions
0.01 .
The linear envelopes of the EMG of the most prevalent horizontal and inclined template are presented in Fig. 5. A small
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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING , VOL. 11, NO. 3, SEPTEMBER 2003
TABLE II
INTER- AND INTRAGAIT COMPARISONS FOR SUBJECT B. SEE TABLE I FOR AN
EXPLANATION
TABLE III
NUMBER OF TEMPLATES FOR HORIZONTAL (H) AND INCLINED (I) GAIT FOR
EACH OF THE THREE TEMPLATE SETS FOR SUBJECT A AND B
TABLE IV
MEANS
D
AND STANDARD DEVIATIONS OF THE
VALUES FOR THE
HORIZONTAL TO INCLINED (HI), HORIZONTAL TO HORIZONTAL (HH), AND
INCLINED TO INCLINED (II) HISTOGRAMS, FOR EACH OF THE THREE
TEMPLATE SETS FOR SUBJECT A AND B
Fig. 5. EMG from VL, BF, TA, and G of the dominant templates of subject B,
during horizontal gait (top panel) and inclined gait (bottom panel).
decrease in the activity of the G for inclined gait may be observed, but the primary differences between the two conditions,
again, appear to be in the relative timing of the EMG activation
patterns. The TA, and to a somewhat lesser degree the VL, activate earlier during inclined gait than during level gait.
C. Sensitivity to Template Selection
The ability to distinguish between gait conditions is critically
affected by the template set. The choice of the template set obviously depends on the data used for template extraction. In
the present case, data from a single session are used to derive
the templates. Conceivably, the gait patterns observed during
a single session may not accurately reflect the variety of patterns that could be produced by a subject, resulting in a decreased ability to differentiate between gait conditions. Therefore, experiments were conducted to assess how sensitive the
SEA method is to the choice of the template set. Two additional
template sets were created for each subject. For subject A, new
template sets were extracted from another 7/25 session and from
a randomly selected 8/01 session. These sets are referred to as
AT2 and AT3, respectively. The original template set will be denoted by AT1. For subject B, the two new template sets came
from a 7/23 (BT2) and a 7/25 session (BT3), and again, the
original set is denoted by BT1. All template sets had different
number of templates, as shown in Table III. Subject A shows
relatively little variability in the number of templates extracted,
in contrast to subject B.
Each of the template sets was used to classify individual
values for the
strides from three other data sets, and the
horizontal to inclined, horizontal to horizontal, and inclined
to inclined histogram comparisons were computed. Mean
values and standard deviations are presented in Table IV. The
values when comparing horizontal to inclined gait are
substantially larger than the corresponding values for horizontal-to-horizontal or inclined-to-inclined gait comparisons,
suggesting that all template sets can differentiate between
level and inclined gait. Template set 3 for subject B shows
the least difference between the horizontal-to-inclined and the
values, and in fact represents the only
inclined-to-inclined
case where the difference is not significant. Closer inspection
of the data revealed that this template set classified 15 of the
18 gait cycles during the 8/01 inclined session as horizontal
gait. This suggests that one session may not be sufficient to
adequately capture the variety of gait patterns manifested
during one gait condition.
IV. DISCUSSION AND CONCLUSION
Our results show that an induced change in walking condition can be recognized reliably and consistently with the SEA
method, but future research will be needed to determine the
smallest possible change in gait patterns that the method can
detect.
The within-session and day-to-day variability is generally
much smaller than the variability between conditions, independent of the choice of the template set. This suggests that our
approach is robust with regard to template selection and daily
variations that could result from slight differences in electrode
placement. However, if this technique is to be used on data
collected over a longer period of time, template selection might
be more critical in the histogram distributions. Specifically,
some of our results suggest that the relatively brief sessions,
JANSEN et al.: MULTIDIMENSIONAL EMG-BASED ASSESSMENT OF WALKING DYNAMICS
containing about twenty gait cycles, may be insufficient to capture the complete variety of gait patterns that may be produced
by a subject, even when the gait condition is tightly controlled.
EMG activation patterns characteristic for level and inclined
gait have been discovered by the SEA method. Although it is
tantalizing to associate these patterns with central pattern generator activity, verification of this link will require more investigation. Further research is also needed to determine if a library
of gait templates can be developed. Ideally, each movement type
and condition (normal or abnormal) would be associated with
one (or more) templates in the library. Once such a library is
constructed, it would not be necessary to extract templates for
each individual.
The SEA method presented in this paper could be used to
track changes in movement patterns that may occur as a result of
therapeutic intervention. A pretherapy session would be used to
define an initial set of templates. Once the therapy starts having
an effect on the way the patient executes the movement, changes
in the histograms will occur. At that time, the template set will
need to be expanded with templates extracted from the most recent session, to account for the new ambulation patterns. If no
further changes in the histograms are observed, one may conclude that the treatment effect has reached a stable plateau.
Important information may also be contained in the sequence
with which movement patterns occur. For example, pattern
may always be followed by pattern , but never by pattern .
Such information can be quantified using Markov modeling,
where each pattern represents a “state.” The set of states and
the state transition probabilities would completely define the interactive motor programming of an ensemble of muscles, and
could be used to arrive at a truly dynamic assessment of movement.
It should be made clear that EMG is just one of many measures routinely used to describe gait. Alternatives include clinical observation and measurement of joint kinematics. The latter
can provide position and acceleration of body segments, and the
forces acting on them during gait. Which gait parameter to select depends to a great extent on the question to be answered [3].
For instance, when designing an orthosis to support the ankle
in patients with a footdrop, it is important to determine the effects of wearing the orthosis on the position of ankle, knee, and
hip joints during the entire gait cycle. Thus, isokinematic measures are to be preferred. On the other hand, EMG may be preferred if muscle activity is of primary interest. For example, gait
impairments in children with cerebral palsy may be treated by
surgically transferring a muscle-tendon unit to a different location. Prior to this intervention, it is essential to asses activity of
all pertinent musculature during the child’s gait and determine
which muscle-tendon transfer can most successfully affect the
interactions of all involved muscles to improve the gait [13].
Post-surgically, EMG assessment is of value to determine if the
transferred muscle indeed assumes its new role within the context of the other muscle activity. Such information could be obtained with the EMG-based method presented here. However,
multiple measurement modalities will be required in virtually
all cases to obtain a complete characterization of the dynamic
processes underlying gait. The SEA method introduced here is
one component of such a suite of measurement tools to characterize gait and movement in general.
299
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Ben H. Jansen (S’74–M’76–SM’90) received his
B.t.w. and Ir. degrees in electrical engineering from
Twente University, Enschede, The Netherlands, in
1973 and 1975, respectively, and the Ph.D. degree
in medical informatics from the Free University,
Amsterdam, The Netherlands, in 1979.
He was a Research Associate and a Research
Assistant Professor with the Department of Electrical
and Biomedical Engineering, Vanderbilt University,
Nashville, TN, through the summer of 1982, at
which point he joined the Department of Electrical
and Computer Engineering, the University of Houston, Houston, TX. He was
promoted to Full Professor in 1992. His research interests include biomedical
signal analysis, with an emphasis on electroencephalograms and evoked
potentials. He serves on the Editorial Board of Clinical Neurophysiology and is
a frequent reviewer for several major biomedical engineering journals.
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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING , VOL. 11, NO. 3, SEPTEMBER 2003
Vonda H. Miller received the B.S. degree in computer science from Lamar University, Beaumont, TX,
and the M.S. degree in electrical engineering from the
University of Houston, Houston, TX. She is currently
working toward the Ph.D. degree at the Department
of Electrical Engineering, University of Houston.
She joined Texas Instruments, Dallas, TX, after
the completion of the B.S. degree to develop image
recognition and flight control algorithms for cruise
missiles in the Defense Suppression Electronics
Group. After completion of the M.S. degreed, she
joined Boeing, Houston, as a Systems Engineer responsible for avionics
hardware and software for the International Space Station. Her research
interests include biomedical signal analysis, pattern recognition, and oscillatory
neural networks.
Ms. Miller was recognized as a Texas Space Grant Fellow from 1996 to 1999.
Demetrios C. Mavrofrides received his B.S. degree
in electrical engineering from the University of
Florida, Gainesville, in 1989 and the M.S. degree
in electrical engineering from the University of
Houston, Houston, TX, in 1997.
He was an Engineer with Martin Marietta Missile
Systems, Orlando, FL, from 1989 to 1992. He is currently with SensorWise Inc., Houston.
Caroline W. Stegink Jansen received the degree
in physical therapy in Utrecht, The Netherlands, in
1973, and the M.S. and Ph.D. degrees in physical
therapy from the Texas Woman’s University,
Houston, TX, in 1988 and 1995, respectively.
She was a Visiting Assistant Professor with the
Texas Woman’s University prior to becoming an
Assistant Professor at the Physical Therapy Department, the School of Allied Health Sciences, the
University of Texas Medical Branch, Galveston, TX,
in 1996. Her research interest is in the assessment of
impairments, functional use and disability of patients with pathologies affecting
primarily the upper extremity. She is interested in the completion of movement
patterns in association with repetitive motion injuries. She is currently a Guest
Editor for the IEEE TRANSACTIONS ON NEURAL SYSTEMS REHABILITATION
ENGINEERING for the topic of splinting of the hand. She serves as member of
the Editorial Board and as a reviewer for the Journal of Hand Therapy, as well
as reviewer for Physiotherapy: Theory and Practice.