JRRD

Volume 52, Number 7, 2015
Pages 775–784

Neuromuscular control of curved walking in people with stroke: Case
report
Amanda E. Chisholm, PhD;* Taha Qaiser; Tania Lam, PT, PhD
School of Kinesiology, University of British Columbia, Vancouver, Canada; and International Collaboration on Repair
Discoveries, Vancouver Coastal Health Research Institute, Vancouver, Canada

walking as opposed to skilled tasks that require the ability to adapt the basic walking pattern. Recent studies
have postulated that impairments in gait that persist after
stroke, such as slow speed and spatial-temporal asymmetry [1–2], may limit the ability to adapt the gait pattern as
required to mobilize safely in the community [3]. Turning and curved walking create a unique set of demands
on the neural processes involved in the control of mediallateral (ML) stability and forward progression that are
greater than the requirements for straight walking [4–8].
Given the significance of turning in everyday mobility
[9–10], as well as the higher risk of falls and injuries
while turning [11–15], it is surprising that there has been
very little research to date on the capacity to adapt
straight walking in ambulatory individuals with stroke.
Some clinical assessments of mobility, the Timed
“Up and Go” test (TUG) and Emory Functional Ambulation Profile, include a turning task to examine adaptation
to straight walking. Both measures have been correlated

Abstract—People are required to adapt their basic walking
pattern to turn and change directions safely for activities of
daily living. This case study describes the changes in neuromuscular control among individuals with stroke on walking
paths of different curvatures. Two men with hemiparetic stroke
and one control subject walked along a straight, wide curved,
and tight curved pathway while muscle activation of the medial
and lateral gastrocnemius was recorded, along with the trajectory of the center of pressure (COP) during the single support
phase. Balance, sensorimotor control, and functional ambulation were also evaluated. The subject with greater lower-limb
sensorimotor impairment displayed a larger difference in the
anterior-posterior COP displacement between limbs, which
exacerbated as the path curvature increased. In addition, while
the control subject demonstrated a lateral shift in the mediallateral COP position, this person was unable to adapt the COP
position on the nonparetic side. The second participant with a
stroke demonstrated better balance and lateral shifting of the
COP position. Modulation of the COP trajectory is required to
safely perform curved walking. Further study is required to
confirm the role of stroke-related gait deficits in the ability to
adapt to curved walking.

Abbreviations: AP = anterior-posterior, CMSA = ChedokeMcMaster Stroke Assessment, COP = center of pressure, EMG =
electromyography, FSR = force-sensitive resistor, LG = lateral
gastrocnemius, MG = medial gastrocnemius, Mini-BESTest =
Mini-Balance Evaluations Systems Test, ML = medial-lateral,
TUG = Timed “Up and Go” test.
*
Address all correspondence to Amanda E. Chisholm,
PhD; International Collaboration on Repair Discoveries,
818 West 10th Ave, Vancouver, British Columbia, Canada,
V5Z 1M9; 604-675-8815. Email: achisholm@icord.org
http://dx.doi.org/10.1682/JRRD.2014.08.0189

Key words: biomechanics, center of pressure, curved walking,
electromyography, gait, hemiparesis, kinetics, neuromuscular
control, stroke, turning.

INTRODUCTION
Current approaches to gait assessment following
stroke primarily measure impairments during straight

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with balance function [16–17], walking speed [17–18],
and lower-limb motor impairment [19–20] in the stroke
population. Previous findings have demonstrated that
individuals with chronic stroke require a greater number
of steps and more time to complete a turn to both the
paretic and nonparetic sides than controls [21]. Furthermore, turning capacity at 180° was correlated with single-support time asymmetry, suggesting greater difficulty
for individuals with more severe gait impairments [21].
Similarly, performance on the Figure-8 Walk Test shows
that individuals with chronic stroke take twice as long to
complete a curved walking task as nondisabled controls
[22]. Although timed tests of functional ambulation offer
an easy and efficient measure of gait performance in the
clinical setting, they do not expose underlying challenges
in the motor control of changing directions during walking that can be determined by examining the center of
pressure (COP) trajectory.
Curved walking imposes greater ML instability than
straight walking [4–7] because the COP needs to shift
toward the lateral edge of the inner foot [7,23]. People with
stroke have impairments in the regulation of frontal plane
stability, as indicated by a reduction in weight-shifting
capacity while standing [24–26]. However, the extent to
which ML control is affected during curved walking
remains unknown. Examining the COP trajectory may provide information on the capacity to turn or walk along
curved paths. Individuals with stroke demonstrate less ML
variability and shorter anterior-posterior (AP) displacement in the COP trajectory under the paretic foot than the
nonparetic foot during straight walking [27]. These findings suggest poor control of ML stability and forward progression, which coincides with a reduced single support
time on the paretic side [27]. In addition, this may lead to
difficulty adapting the gait pattern to match the greater
demands required of curved walking.
Modulation of muscle activation patterns has been
identified with walking along curved pathways [28–29].
The inner leg of the turn shows reduced amplitude of
activity in muscles located in the medial compartments of
the lower limb, whereas those in the lateral compartments
show increased amplitude with increasing path curvature;
however, the opposite effect is observed for the outer leg
[28]. These effects are consistent with a shift in foot pressures toward the lateral aspect of the inner foot [29]. One
study investigating neuromuscular strategies for curved
walking in people with chronic stroke found reduced
adaptation in the gastrocnemii and hamstring muscles

and a shift in foot pressures to increased curved walking
paths compared with age-matched controls [29]. In addition, physiological evidence shows that the gastrocnemii
muscles contribute to active control over ML ankle
motion to stabilize the body in the frontal plane and may
be related to the degree of challenge to stability [30].
While difficulties in curved walking are apparent among
people with chronic stroke, examining the COP excursion can reveal aspects about the neuromuscular response
to control ML stability and forward progression.
The purpose of this case report was to better understand neuromuscular control in walking along paths with
increasing curvatures by examining changes in the COP
trajectory and lower-limb muscle activity among people
with stroke. We hypothesized that individuals with stroke
would show impaired control over the ML stability and
reduced forward progression as path curvature increased
compared with reference data provided by an agematched control.

METHODS
Case Description
Two men who had an ischemic stroke that resulted in
hemiparesis participated in this study after giving written
informed consent. Subject 1 (S1) was 59 yr old and had a
stroke 7 yr ago affecting his left side, while subject 2 (S2)
was 61 yr old with the right side affected from a stroke 1 yr
ago. Both participants were able to walk a minimum of 10
m without an assistive device. However, S1 uses a single
point cane and ankle-foot orthotic for daily mobility. Neither of the subjects had experienced a fall since their
stroke. Additionally, a 65 yr old man, free of any other
musculoskeletal or neurological disease affecting balance
or mobility, completed the study as a control participant.
All demographic and clinical data are outlined in Table 1.
The University of British Columbia Clinical Research Ethics Board approved the experimental procedures.
Measurements
We evaluated the degree of sensorimotor impairment
due to stroke with the Chedoke-McMaster Stroke Assessment (CMSA) impairment inventory for the foot and leg
administered by a trained research assistant. Each item was
measured on a 7-point scale, with lower scores indicating
greater impairment. The CMSA has high interrater and
intrarater reliability [31]. The Mini-Balance Evaluations

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Table 1.
Subject demographic and clinical data.

Demographic
Age (yr)
Weight (kg)
Height (cm)
Stroke Type
Time Poststroke (yr)
Affected Side
Falls
TUG + Dual Task (s)
Mini-BESTest
CMSA Leg
CMSA Foot

Subject 1
59
81.6
175
Ischemic
7
Left
0
36.1
13
5
2

Subject 2
61
70.3
172
Ischemic
1
Right
0
11.1
27
6
5

Control
65
79.3
177
—
—
—
0
8.9
28
—
—

CMSA = Chedoke-McMaster Stroke Assessment (measure of sensorimotor
impairment), Mini-BESTest = Mini-Balance Evaluations Systems Test (measure of balance function), TUG = Timed “Up and Go” test (measure of functional mobility).

Systems Test (Mini-BESTest) was used to assess balance
control. The 14 items are scored from 0 (severe impairment) to 2 (normal function) to represent anticipatory balance, reactive postural control, sensory orientation, and
dynamic gait functions. Interrater reliability is above 0.96
for the total score on the Mini-BESTest [32]. The TUG is
an item of the Mini-BESTest and was used to assess functional mobility [32].
Two force-sensitive resistors (FSRs) (Interlink Electronics Inc; Camarillo, California) were positioned under
the heel and medial forefoot of each foot to determine
stance and swing phases of the gait cycle. Surface electromyography (EMG) (Delsys Inc; Boston, Massachusetts) was used to record activity of the medial
gastrocnemius (MG) and lateral gastrocnemius (LG)
bilaterally during walking. Subjects walked over a force
plate positioned in the middle of the walkway (Bertec;
Columbus, Ohio). FSR, EMG, and force plate data were
collected at 1,000 Hz in a custom data acquisition program made in LabVIEW (National Instruments; Austin,
Texas). Infrared-emitting markers (Optotrak, NDI;
Waterloo, Canada) were used to track the motion of each
foot, which were defined with the following landmarks:
head of the fifth metatarsal, head of the second metatarsal, and heel. Kinematic data were synchronized with a
trigger to the acquisition system and sampled at 100 Hz.
The kinematic landmarks were used to define the foot’s
position with respect to the force plate coordinate system.

Experimental Protocol
Data were recorded while participants traversed each
of the following paths, which were outlined on the laboratory floor with tape (Figure 1):
1. Straight path: 6 m straight path, curvature = 0.
2. Large semicircle: 10 m circular path with a radius of
2 m, curvature = 0.5 m.
3. Small semicircle: 3 m circular path with a radius of
0.5 m, curvature = 2 m.
Subjects walked clockwise and counterclockwise
along each path at their usual comfortable walking speed
without any assistive device until four to five acceptable
foot contacts on the force plate were achieved per direction. Participants were monitored to ensure that they were
following the path, such that the line outlining the path
curvature was maintained between the two feet. The time
required to complete each of these paths was recorded
with a stopwatch to calculate walking speed. Sufficient
rest was allowed between walking trials to minimize
fatigue.
Data Analysis
Data were analyzed offline using custom-written
routines in MATLAB (MathWorks; Natick, Massachusetts). FSR and force plate signals were low-pass filtered
at 25 Hz with a fourth-order Butterworth filter. The EMG
data were full-wave rectified and low-pass filtered at
25 Hz with a fourth-order Butterworth filter. FSRs data
were used to define the times of foot contact and toe-off
and identify the single support phase. The step cycle was
defined as the period between successive foot contact

Figure 1.
Laboratory setup for straight (black line), wide curve (light gray
line), and tight curve (dark gray line) walking paths were outlined with tape on floor. Force plate was positioned in middle of
each path.

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times, and data were normalized to 100 percent of the
gait cycle. Walking trials with insufficient foot contact on
the force plate were excluded from the analysis. Data
were divided to examine COP excursions and muscle
activity during the single support phase for the inner leg
when walking along the curve. Kinematic landmarks on
the foot were used to normalize calculations for the AP
COP displacement from the first to last COP position in
the single support phase with respect to foot length and
ML mean position and variability (standard deviation).
These measures provided information on forward progression and ML stability during the single support
phase. We also calculated the ML-COP minimum and
maximum perpendicular distance relative to the lateral
border of the foot (i.e., defined as a line from the heel to
fifth metatarsal) to examine stability relative to the base
of support limits. COP outcome measures were calculated for each trial and presented as the mean for each
condition. The EMG data were normalized as a percentage of the gait cycle for each step over the force plate,
and then the average amplitude was calculated over the

gait cycle for each condition. Muscle activity from the
single support phase of the step cycle was inspected to
evaluate changes across conditions.

RESULTS
Table 2 presents a summary of the COP outcome
measures. All subjects reduced their comfortable walking
speed as the path curvature increased. The control participant demonstrated a reduction in AP-COP displacement
as the path curvature increased. The ML-COP shows a
trend toward greater variability and more lateral mean
position during the tight curve compared with the wide
curve and straight path (Figure 2, Table 2). As the path
curvature increased, the ML minimum and maximum
distance to the foot’s lateral border increased. The MG
activity decreased during single support as the path curvature increased, while LG activity shows the opposite
trend (Figure 3).

Table 2.
Center of pressure outcome measures for straight and curved walking.

Test Path
Subject 1
Paretic
Straight
Wide
Tight
Nonparetic
Straight
Wide
Tight
Subject 2
Paretic
Straight
Wide
Tight
Nonparetic
Straight
Wide
Tight
Control
Straight
Wide
Tight

Mean

Variability

Minimum

Maximum

AP
Displacement

0.48 ± 0.05
0.36 ± 0.08
0.30 ± 0.01

5.4 ± 3.7
12.6 ± 2.3
10.0 ± 0.5

1.2 ± 0.3
1.5 ± 0.2
1.5 ± 0.5

59.9 ± 5.8
67.4 ± 5.8
63.1 ± 3.1

67.5 ± 5.1
76.8 ± 3.8
70.3 ± 3.4

11.5 ± 1.7
8.7 ± 3.3
6.0 ± 5.1

0.47 ± 0.06
0.40 ± 0.05
0.32 ± 0.04

26.8 ± 4.5
25.9 ± 1.4
28.2 ± 3.5

2.8 ± 2.3
2.8 ± 1.2
3.2 ± 1.7

68.7 ± 10.8
75.5 ± 9.0
72.1 ± 4.2

108.9 ± 11.3
101.7 ± 4.1
101.0 ± 7.2

38.4 ± 20.3
19.8 ± 7.5
19.3 ± 4.2

0.92 ± 0.14
0.78 ± 0.10
0.65 ± 0.06

8.0 ± 2.8
3.2 ± 5.1
13.1 ± 7.3

4.5 ± 2.7
7.5 ± 2.6
6.0 ± 2.5

35.4 ± 10.2
35.2 ± 9.3
40.9 ± 8.4

56.4 ± 4.8
55.1 ± 4.1
67.1 ± 3.3

49.7 ± 21.8
47.8 ± 16.4
27.0 ± 6.9

0.99 ± 0.07
0.89 ± 0.17
0.69 ± 0.06

4.4 ± 9.4
5.2 ± 8.2
7.1 ± 4.0

3.7 ± 1.4
5.9 ± 2.3
5.9 ± 5.6

31.8 ± 5.0
26.2 ± 3.9
48.5 ± 7.6

63.0 ± 9.8
67.9 ± 7.8
67.6 ± 2.5

52.6 ± 4.9
47.6 ± 6.4
29.6 ± 12.7

1.25 ± 0.10
0.99 ± 0.04
0.97 ± 0.02

7.1 ± 3.9
7.7 ± 3.5
13.4 ± 4.7

4.4 ± 1.7
4.3 ± 1.4
4.8 ± 1.9

29.2 ± 7.4
38.2 ± 5.0
41.1 ± 6.9

83.3 ± 3.2
89.2 ± 1.2
94.6 ± 6.0

58.0 ± 1.8
49.9 ± 3.5
44.9 ± 7.0

Speed (m/s)

ML

Note: Data are presented as mean ± standard deviation. Anterior-posterior (AP) displacement is reported as percentage of foot length. Medial-lateral (ML) outcome
measures are reported in millimeters. ML mean and variability are measured relative to midline (2nd metatarsal and heel), and minimum and maximum measured
from lateral border (5th metatarsal and heel).

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Figure 3.
Average electromyography activities for medial gastrocnemius
(MG) and lateral gastrocnemius (LG) muscles are plotted for
straight (black line), wide curve (light gray line), and tight curve
(dark gray line) walking paths for (a) S1, (b) S2, and (c) control
participant. Data are normalized to 100% of step cycle. Shaded
gray box represents average single support phase. Note y-axis
has been adjusted for subjects based on their maximum activity
during step cycle. S = subject.
Figure 2.
Each panel shows representative data of center of pressure trajectory, measured in millimeters, during straight (black line),
wide curve (light gray line), and tight curve (dark gray line)
walking paths for (a) S1, (b) S2, and (c) control participant.
Positions of heel (black circle), second metatarsal (black triangle), and fifth metatarsal (black square) are plotted. S = subject.

S1 presented with severe lower-limb sensorimotor
impairment and poor balance function. COP analysis

revealed a negative AP displacement for the paretic limb
across all conditions for S1, indicating that the first COP
point was anterior to the last COP point on the foot during the single support phase. The difference between the
minimum and maximum point in the AP direction reveals
mean displacements of 12.9, 6.5, and 4.4 percent (relative to foot length) for straight, wide curve, and tight
curve paths, respectively. The paretic and nonparetic
limbs show no change in ML variability between conditions. The nonparetic limb’s AP displacement reduced as

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the path curvature increased; however, the ML variability
was consistently larger for all conditions compared with
the paretic limb (Table 2). The minimum and maximum
ML-COP distance does not show the increasing trend
across condition as observed in the control participant.
MG and LG muscle activity show no change for both
limbs as the path curvature increases (Figure 3).
In comparison to S1, S2 had mild lower-limb sensorimotor impairment, better balance function, and a faster
walking speed. The AP-COP displacement for both limbs
reduced as the path curvature increased. ML variability
was greater during the curved conditions than in the
straight condition for both limbs (Table 2). The ML minimum distance increased from the straight to tight path
curvature for the nonparetic limb. The nonparetic limb
showed no change in LG activity and a slight decrease in
MG activity, while the paretic showed a slight increase in
LG activity and no change in MG activity from straight
to tight curved walking paths (Figure 3).

DISCUSSION
In this case report, we examined modulation of the
COP trajectory and lower-limb muscle activity during the
single support phase of gait in response to walking paths
with increasing curvature among individuals with
chronic stroke and an age-matched control participant.
Muscular adaptations to curved walking, but not the COP
trajectory, have been examined in nondisabled adults and
people with stroke [28–29]. We observed reduction in the
AP-COP displacement and an increase in ML variability
as the walking path curvature increased, possibly reflecting the degree of difficulty in controlling frontal plane
stability. However, the individual with stroke presenting
with greater lower-limb sensorimotor impairment displayed poor forward progression on the paretic side and
did not demonstrate modulation in ML variability with
increasing curvatures on the nonparetic side. Other
changes we detected include reduced walking speed,
larger minimum ML-COP distance to the foot’s lateral
border, and a more lateral mean COP position.
Research on measuring the COP trajectory to understand neuromuscular control during walking in the stroke
population has mainly focused on straight walking and
gait initiation [27,33]. In comparison to straight paths, the
COP trajectory is modulated during curved walking, as
seen by the reduction in AP displacement, lateral shift in

position, and greater ML variability. Similar to a previous
study, S1 demonstrated poor forward progression on the
paretic side and a greater difference between limbs for APCOP displacement than S2, likely due to a greater severity
in lower-limb sensorimotor control [27]. The difference
between limbs for AP-COP displacement may be related
to challenges in controlling stability during forward progression over the paretic limb. Factors contributing to a
smaller AP-COP displacement on the paretic side
observed for S1 include a flatfoot or forefoot initial contact, reduced stance time, and poor stability [1,34]. Furthermore, the difference in AP-COP displacement
between limbs appears to be further exacerbated as the
path curvature increases in both subjects. Given that
between-limb asymmetry is required to maintain curved
walking [7], perhaps the lateral weight shift to the paretic
side becomes more difficult as the curvature increases. In
a previous study, individuals with greater temporal asymmetry took a longer time and more steps to complete a
180° turn to both sides [21]. Individuals with gait asymmetry may require more specific training on curved walking and turning to improve lateral weight shifting capacity.
Challenges to frontal plane stability during curved
walking are observed by changes in COP trajectory and
ML mean position and variability. A more lateral ML
position under the foot as the path curvature increased
may reflect a strategy to maintain balance as the body
adjusts to change directions. These findings are consistent
with observations that people lean toward the inside leg as
the pelvis shifts over to the inside of the curve [5,28] and
plantar pressures redistribute under that lateral aspect of
the foot [29]. Greater ML variability may reflect an
increased demand on lateral stability when challenged to
adapt the basic locomotor pattern. In particular for S1,
increased time on the nonparetic limb to compensate for
poor swing phase mechanics of the paretic limb may contribute to a larger ML-COP variability. Individuals with
greater sensorimotor impairment may engage compensatory movements, such as hip hiking and lower-limb circumduction, to facilitate swinging the paretic limb
forward [35]. Therefore, these compensations may also
contribute to increased ML-COP variability.
In agreement with previous studies, our control participant demonstrated a reduction in MG activity and an
increase in LG activity to adapt to increases in the path
curvature [28–29]. Modulation in muscle activity is
thought to be necessary for maintaining ML stability during curved walking [4,7]. A previous study linked reduced

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CHISHOLM et al. Curved walking biomechanics poststroke

modulation of the MG to increases in path curvature when
the paretic limb was on the inside of the curve [29]. Our
new observations on the muscular response show reduced
MG activity and no change in LG activity on the nonparetic side as the path curvature increased. It has been suggested that cutaneous afferents may play a role in
modulating EMG activity through the sural nerve reflex
[36]. The lack of change in EMG activity on the nonparetic side for S1 may reflect no changes in ML-COP
position as the curvature increased, thus leading to
reduced activity of cutaneous afferents on the lateral
aspect of the foot. Further study is required to confirm a
relationship between modulation of cutaneous afferents,
EMG activity, and COP trajectory.
Although we have described important changes in
the neuromuscular control response to curved walking,
there are several limitations to be considered. Our participants walked at their comfortable pace for each condition, which reduced as the path curvature increased.
Slower walking speeds have been demonstrated to produce a shorter AP-COP displacement and smaller MLCOP variability in the stroke population [27]. Future controlled studies will require a matched speed across conditions to discern the effects of walking speed and curve
difficulty. While we examined adaptation to curved walking without the influence of an assistive device (e.g., cane
or walker), these devices are commonly prescribed to
maintain walking capacity to compensate for underlying
impairments following a stroke [37]. Although previous
studies have suggested that assistive devices can alter the
gait pattern and the degree of symmetry between limbs
[38–39], the effect of assistive devices on curved walking
has yet to be investigated. This will be important to provide better application of research findings to the walking
conditions encountered in daily activities.
Our findings provide an initial report showing difficulty in adapting the neuromuscular response to curved
walking among people with stroke. Although we initially
collected data from eight individuals poststroke, only
data from two individuals were included in the analysis
because of an insufficient number of acceptable foot contacts on the force plate during curved walking (<4 per
condition). For future studies conducting this type of
assessment, we recommend limiting the number of walking conditions collected to avoid fatigue and using multiple force plates to capture more foot contacts per walking
bout. In addition, we only focused on the inner leg of the
turn because the body shifts toward the inner leg during

curved walking [7]. However, future studies should also
explore contributions of the outer leg to the motor control
of curved walking. In addition, it will be important to
establish the optimal amount of change in the outcome
measures between walking conditions. Our findings also
highlight characteristics of individuals who may be suitable to complete this assessment, such as ability to walk
10 m without an assistive device, mild sensorimotor
impairment at the leg (CMSA 5+), and a normal rating on
the walking items of the Mini-BESTest. This will guide
future studies with a larger sample to confirm the effect
of walking curvature on neuromuscular control.

CONCLUSIONS
This case report describes our initial findings on
modulation of the COP trajectory and muscle activity in
response to curved walking. Our results add to research
showing that after stroke individuals have difficulty
adapting walking conditions that involve curves or turns
[21,29,40]. We found a reduction in the AP-COP displacement and an increase in ML variability as the walking path curvature increased, which may indicate the
degree of difficulty in maintaining stability. Developing
new approaches to evaluate walking capacity is critical to
determining the effect of interventions on the underlying
neuromuscular control for walking tasks performed in
everyday activities. Further studies are required to understand changes in COP trajectory and muscle activity with
respect to stroke-related impairments.

ACKNOWLEDGMENTS
Author Contributions:
Study concept and design: T. Lam.
Acquisition of data: A. E. Chisholm, T. Qaiser.
Analysis and interpretation of data: A. E. Chisholm, T. Qaiser,
T. Lam.
Drafting of manuscript: A. E. Chisholm.
Critical revision of manuscript for important intellectual content:
A. E. Chisholm, T. Qaiser, T. Lam.
Final approval for manuscript to be published: A. E. Chisholm,
T. Qaiser, T. Lam.
Financial Disclosures: The authors have declared that no competing
interests exist.
Funding/Support: This material was based on work supported by the
Canadian Institutes of Health Research (reference number 93375).
Institutional Review: The University of British Columbia Clinical
Research Ethics Board approved the experimental procedures.

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Participant Follow-Up: The authors do not plan to inform participants of the publication of this study. However, participants have been
encouraged to check the Web site for updated publications.

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Submitted for publication August 14, 2014. Accepted in
revised form June 1, 2015.
This article and any supplementary material should be
cited as follows:
Chisholm AE, Qaiser T, Lam T. Neuromuscular control
of curved walking in people with stroke: Case report.
J Rehabil Res Dev. 2015;52(7):775–84.
http://dx.doi.org/10.1682/JRRD.2014.08.0189
ResearcherID: Tania Lam, PT, PhD: H-3730-2015