RESEARCH
Open Access
Contributions of lower extremity kinematics to
trunk accelerations during moderate treadmill
running
Timothy R Lindsay1*, James A Yaggie2 and Stephen J McGregor1
Abstract
Background: Trunk accelerations during running provide useful information about movement economy and
injury risk. However, there is a lack of data regarding the key biomechanical contributors to these accelerations.
The purpose was to establish the biomechanical variables associated with root mean square (RMS) accelerations
of the trunk.
Methods: Eighteen healthy males (24.0 ± 4.2 yr; 1.78 ± 0.07 m; 79.7 ± 14.8 kg) performed treadmill running with
high resolution accelerometer measurement at the lumbar spine and full-body optical motion capture. We collected
60 sec of data at three speeds (2.22, 2.78, 3.33 m∙s−1). RMS was calculated for medio-lateral (ML), anterio-posterior (AP),
vertical (VT), and the resultant Euclidean scalar (RES) acceleration. From motion capture, we calculated 14 kinematic
variables, including mean sagittal plane joint angles at foot contact, mid-stance, and toe-off. Principal components
analysis (PCA) was used to form independent components comprised of combinations of the original variables.
Stepwise regressions were performed on the original variables and the components to determine contributions
to RMS acceleration in each axis.
Results: Significant speed effects were found for RMS-accelerations in all axes (p < 0.05). Regressions of the original
variables indicated from 4 to 5 variables associated with accelerations in each axis (R2 = 0.71 to 0.82, p < 0.001). The
most prominent contributing variables were associated with the late flight and early stance phase. PCA reduced
the data into four components. Component 1 included all hip angles before mid-stance and component 2 was
primarily associated with propulsion. Regressions indicated key contributions from components 1 and 2 to ML,
VT, and RES acceleration (p < 0.05).
Conclusions: The variables with highest contribution were prior to mid-stance and mechanically relate to shock
absorption and attenuation of peak forces. Trunk acceleration magnitude is associated with global running
variables, ranging from energy expenditure to forces lending to the mechanics of injury. These data begin to
delineate running gait events and offer relationships of running mechanics to those structures more proximal in
the kinetic chain. These relationships may provide insight for technique modification to maximize running
economy or prevent injury.
Keywords: High resolution accelerometers, Root mean square, Principal components analysis, Running, Economy,
Injury, Stiffness
* Correspondence: tlindsa2@emich.edu
1School of Health Promotion & Human Performance, Eastern Michigan
University, Ypsilanti, MI, USA
Full list of author information is available at the end of the article
JNER
JOURNAL OF NEUROENGINEERING
AND REHABILITATION
© 2014 Lindsay et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly credited.
Lindsay et al. Journal of NeuroEngineering and Rehabilitation 2014, 11:162
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Background
Running is an increasingly popular sport that provides
substantial health benefits at minimal expense. Estimates
from 2011 are that 38.7 million Americans participate in
running or jogging 6 or more days per year (up from 24.5
million in 2001), with 9.2 million doing so 110 or more
days per year (up from 6.8 million in 2001) [1]. Offsetting
the numerous health benefits of exercise is the relatively
high incidence of injury, which according to one system-
atic review, ranges from 19-79% [2]. Even at the lower end
of this range, the high participation rate means that injury
is a substantial concern. Since most running injuries are
chronic rather than acute [3,4], the tolerable level of accu-
mulated stress is an important consideration. This stress
depends on multiple factors including the training dose,
anatomical structure, and movement mechanics [4-7]. We
focus on mechanics in this paper.
Mechanically speaking, running involves the application
of force to the ground to generate the resultant ground re-
action force (GRF) necessary for forward propulsion and
support against gravity. This places stress on soft tissue
and bone via force transmission through the kinetic chain,
which may lead to future injury if the exercise dosage ex-
ceeds regenerative capacity. A comprehensive description
of forces requires a complicated model, but the acceler-
ation of the center of mass (COM) can provide a simple
quantification of net force. Continuous COM data may
then be expressed as a root mean square (RMS) value to
represent the overall magnitude of acceleration over many
strides [8]. RMS provides a measure of dispersion similar
to standard deviation, only relative to zero rather than the
mean [9]. The presence of more extreme values in the sig-
nal (i.e., high acceleration or deceleration) increases the
RMS value. Acceleration at any anatomical location de-
pends on the level of attenuation through tissue de-
formation and joint excursion at all points distal. The
attenuation of force and acceleration can be modified
with lower limb stiffness and may alter the likelihood of
running-related injuries [10,11]. High stiffness may aid
performance and economy but also may increase the
risk of injury to structural components. In contrast,
stiffness that is too low may be metabolically costly and
increase the risk of soft tissue injury [8,10-12]. Stiffness
depends on the intrinsic properties of bone and soft tis-
sue (muscle, tendon, ligament, and cartilage) [13], but
also may be modified via kinematic changes. For ex-
ample, in subjects instructed to perform a soft drop
landing, there was greater knee joint excursion [14,15].
As well, Derrick [11] has argued that runners generally
run with extended knees prior to impact, but are able
to increase knee flexion in order to reduce vertical ac-
celerations. Similarly, subjects who were instructed to
adopt a “Groucho running” style had longer strides
(believed to be associated with decreased stiffness) and
decreased stiffness, as directly measured [16]. Inter-
ventions such as gait retraining to pursue this objective
are promising and demonstrate that kinematics are
modifiable [17,18].
There has not yet been a direct investigation into the
relationship between running mechanics and RMS accel-
eration. The measurement of acceleration requires little
equipment, can be done in the field, and real-time feed-
back is possible. Since the major movements of running
are in the sagittal plane, we focused on the flexion/ex-
tension behavior of the hip, knee, and ankle joints during
various gait events, as well as some other key variables
that are readily modifiable. The purpose of this study was
to determine the biomechanical factors contributing to
global axial accelerations in active healthy males. In previ-
ous work [8], we observed greater accelerations in healthy
untrained runners compared to trained collegiate runners.
In the current study we selected a sample that was rela-
tively heterogeneous with regard to chosen mode of phys-
ical activity and indicative of those from the general
population who might take up running as a recreational
activity for health benefits. These individuals would be
more likely to exhibit mechanics that would make them
more susceptible to injury due to relatively high accelera-
tions [8]. To accomplish our objectives, we used a multiple
regression approach to determine the variables that best
fit a least squares model generated for RMS acceleration
in each axis. Additionally, principal components analysis
(PCA) was used to establish potentially hidden interac-
tions between individual variables that can be combined
to form separate components. These components may be
then assessed for their contribution to axial accelerations.
Thus, with a view to performance and injury management,
this study will provide a description of modifiable bio-
mechanical factors and their relationship with RMS trunk
accelerations.
Methods
Subjects and experimental procedure
Eighteen healthy, active, college-age males volunteered to
participate. Subjects participated 2–7 times per week in
various forms of physical activity such as individual endur-
ance sports (including running for 6 subjects), strength
training, team sports, and/or combat sports. The proce-
dures of this study were approved by the Human Subjects
Review Committee of Eastern Michigan University College
of Health and Human Services. All subjects provided writ-
ten informed consent.
We analyzed 60 sec of data from three randomly-ordered
treadmill run trials run at 2.22, 2.78, and 3.33 m∙s−1. Sub-
jects were given as much rest between trials as they desired
(typically 60–180 s). Subject characteristics are presented in
Table 1.
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Instrumentation
We placed one triaxial high resolution accelerometer
(G-Link ADXL 210, Microstrain, Inc., Williston, VT) on
the dorsal mid-line, at the level of the iliac crest (ap-
proximately at the L4/L5 spinous process). Accelerome-
ters mounted at this anatomical location can provide
valid estimates of oxygen consumption during running
and can distinguish mechanics between trained and un-
trained individuals [8]. Although the legs primarily move
in the sagittal plane during running, this is not the case
for the spine and pelvis. Because the accelerometer is
mounted in that region, there is significant non-sagittal
movement requiring measurement in three and not just
two dimensions. The accelerometer (mass = 47 g) con-
sists of internal circuitry enclosed in a 58 × 43 × 21 mm
casing, plus an antenna extending a bit outside the di-
mensions and adding 18 mm to the thickness. The ac-
celerometer was mounted to a semi-rigid strap, and
secured with elastic wrap to minimize extraneous move-
ment of the device.
Kinematic data was collected with a 3-D optical mo-
tion capture system (Vicon MX, Vicon, Centennial, CO).
We employed a 39-marker full body gait model (Plug-
In-Gait, Vicon, Los Angeles, CA) consisting of 15 seg-
ments including the head, thorax, pelvis, upper arm,
forearm, hand, thigh, shank, and foot. Seven cameras
(Vicon T40 and T40 S) were placed roughly equidistant
to the subject on the treadmill. Mean values for fourteen
kinematic variables were calculated (mean value for left
and right leg). Foot contact was defined as the point of
lowest vertical displacement of the heel marker [19].
Mid-stance was defined as the lowest point of the
software-estimated COM. Toe-off was defined as the
point of maximum knee extension [19]. Lower limb joint
angles were calculated according to the parameters of
the software and model. Variables are listed and defined
in Table 2.
Data capture and analysis
Data were collected in the medio-lateral (ML), anterio-
posterior (AP), and vertical (VT) axes. Trajectories
were sampled at 200 Hz and then filtered with a 4th
order Butterworth filter with a low pass cutoff at
10 Hz. Accelerometer data were streamed wirelessly at
617 Hz to Agilelink software (Microstrain, Williston,
VT), subsequently re-sampled at 200 Hz, and filtered
similarly to correspond with motion capture data. During
running, the device is not perfectly aligned relative to the
room (i.e., the global coordinate system, as opposed to
the body coordinate system). Corrections were made
for the tilt of the accelerometer, based on the method
of Moe-Nilssen [9]. We provide a brief description of
the calculations, but we encourage the reader to study
the details provided in that paper [9]. Correction is
possible because the mean vector angles in the ML and
AP sensing axes may be estimated while the participant
is running (see Appendix for calculations). The RMS of
the
vertical
(VTRMS),
medio-lateral
(MLRMS),
and
anterior-posterior (APRMS) axes was then calculated for
the epochs in each trial:
xRMS ¼
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
1
N
X
N
i
xi2
v
u
u
t
ð1Þ
where x is the given plane and N is the total number of
samples in 60 sec (at 200 Hz, N = 12,000). The result-
ant Euclidian scalar variable (RES) was also calculated
Table 1 Subject characteristics
Mean
SD
Min
Max
Age (yr)
24.0
4.2
19
32
Height (m)
1.78
0.07
1.66
1.89
Mass (kg)
79.7
14.8
59.1
107.3
BMI (kg∙m−2)
25.2
3.6
20.8
31.7
Table 2 Biomechanical variable definitions
Abbreviation
Explanation
Measurement
convention
Hip-max
Maximum hip angle
(before foot-strike).
Positive = flexion
Hip-FS
Hip angle at foot-strike.
Hip-MS
Hip angle at mid-stance.
Hip-TO
Hip angle at toe-off.
Knee-FS
Knee angle at foot-strike.
Positive = flexion
Knee-MS
Knee angle at mid-stance.
Knee-TO
Knee angle at toe-off.
Ankle-FS
Ankle angle at foot-strike.
Positive = dorsiflexion
Ankle-TO
Ankle angle at toe-off.
PR
Mean range of pelvis rotation
in the transverse plane for
each gait cycle.
Scalar
FA
Foot advance; sagittal plane
distance between the heel
and COM at foot contact,
relative to mean leg length.
Scalar
DROP
Vertical displacement of COM
from foot contact to mid-stance,
relative to mean leg length.
Scalar
RISE
Vertical displacement of COM
from mid-stance to toe-off,
relative to mean leg length.
Scalar
SR
Step rate.
Steps per min
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for the determination of the magnitude of the overall
body acceleration:
RESRMS ¼
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
VT RMS2 þ MLRMS2 þ APRMS2
p
ð2Þ
The above processing and analysis of data was done
using custom designed code in a Matlab environment
(Matlab R2013b, Mathworks, Natick, MA).
Statistical tests
Correlations were first performed to assess the relation-
ship between anthropometric variables and acceleration.
Analysis of variance (ANOVA) was used to determine the
effect of speed on the four acceleration and fourteen bio-
mechanical variables. A stepwise regression was then used
to determine the significant kinematic contributions to ac-
celeration in each axis. We also performed principal com-
ponent analysis (PCA) to reduce the dimensionality of the
data into significant components using a varimax rotation
and Kaiser normalization. A stepwise regression was then
performed using these components as predictors of acceler-
ation in each axis. Post hoc power analyses were conducted
for all ANOVA and regression analyses. A Bonferroni test
was used for multiple comparisons, where appropriate.
Statistical significance was set at p < 0.05. Statistical analysis
was done using SPSS software (version 21, IBM Corpor-
ation, Armonk, NY).
Results
Significant speed effects were found for RMS-accelerations
for ML, AP, and RES (p < 0.05, Table 3). Of the biomechan-
ical variables, only maximum hip angle showed a significant
speed effect (p < 0.05, Table 3). Height, mass, and BMI were
not significantly correlated with acceleration in any axis
(p < 0.05).
Regression indicated 4 to 5 significant variables associ-
ated with acceleration, depending on the axis (Table 4).
We encourage the reader to take notice of the sign of
the beta coefficients (Table 4) and the angle definitions
(Table 2) to understand the direction of change that is
associated with an increase in acceleration. The combin-
ation of significant variables was different for each axis.
Explained variance (R2) ranged from 0.71 to 0.82. A plot
of predicted versus measured RMS acceleration for each
axis is provided in Figure 1.
PCA indicated 4 significant kinematic components
(Table 5), explaining 79.1% of total variance. Component
1 (λ = 4.9, 37.4% of variance) was comprised of variables
predominantly associated with hip flexion in late flight
and early stance phase (hip-MS, hip-FS, knee-MS, hip-
max). Component 2 (λ = 2.8, 21.2% of variance) was as-
sociated with the propulsive phase of the gait cycle
(ankle-TO, knee-TO, RISE, PR). Component 3 (λ = 1.6,
12.5% of variance) included variables associated with
cushioning during the early stance phase (knee-FS,
DROP, ankle-FS). Regressions (Table 6) indicated that
components 1 and 2 significantly predicted ML, VT, and
RES acceleration (R2 from 0.32 to 0.40, p < 0.001). Com-
ponent 3 significantly predicted AP acceleration (R2 =
0.041, p = 0.041).
Discussion
The purpose of this study was to determine the bio-
mechanical contributors to global axial RMS accelera-
tions during running. We found significant relationships
where explained variance using regressions on the ori-
ginal variables was 0.71 for ML, 0.53 for AP, 0.74 for VT,
and 0.43 for RES. PCA did identify hidden relationships
that explained 79% of the variance of the original vari-
ables and that were not evident using only multiple re-
gression. When regressions were performed using the
PCA component variables, though, explained variance
was lower than with the original biomechanical variables
alone. Reducing the numerous variables into a few prin-
cipal components therefore does explain much of the
variance in a simplified manner, but the predictive value
of this simplified relationship is not as strong as using a
traditional regression with a non-reduced variable set.
Table 3 Mean (SD) acceleration and biomechanical
variables for each speed
Variable
Speed (m/s)
Observed
power
2.22
2.78
3.33
MLRMS (g)*
0.35 (0.05)
0.41 (0.06)†
0.46 (0.07)†
1.00
APRMS (g)*
0.36 (0.06)
0.43 (0.10)†
0.50 (0.10)†
0.99
VTRMS (g)
1.09 (0.13)
1.18 (0.11)
1.19 (0.10)
0.72
RESRMS (g)*
1.21 (0.12)
1.33 (0.12)†
1.38 (0.11)†
0.99
Hip-max (deg)*
36.2 (6.8)
42.1 (7.2)†
48.3 (7.4)†
1.00
Hip-FS (deg)
28.9 (6.5)
31.5 (5.8)
34.6 (6.2)
0.68
Hip-MS (deg)
22.6 (7.6)
24.9 (6.9)
27.6 (7.4)
0.41
Hip-TO (deg)
−5.4 (5.6)
−8.4 (5.6)
−10.5 (5.6)
0.68
Knee-FS (deg)
13.0 (8.2)
12.3 (6.6)
13.7 (6.6)
0.08
Knee-MS (deg)
37.9 (7.1)
39.3 (6.8)
40.6 (6.7)
0.16
Knee-TO (deg)
10.5 (6.5)
8.6 (5.8)
8.0 (6.1)
0.19
Ankle-FS (deg)
9.4 (5.0)
8.9 (4.8)
9.2 (4.8)
0.06
Ankle-TO (deg)
−11.5 (7.5)
−16.0 (5.4)
−17.6 (5.7)
0.76
PR (deg)
3.9 (1.6)
4.7 (2.5)
5.6 (3.7)
0.34
FA (% mean
leg length)
5.3 (2.9)
6.9 (3.0)
9.2 (3.8)
0.90
DROP (% mean
leg length)
6.2 (1.7)
6.3 (1.5)
5.9 (1.4)
0.10
RISE (% mean
leg length)
8.6 (1.7)
8.9 (1.5)
8.7 (1.5)
0.09
SR (steps/min)
155.5 (9.5)
158.0 (9.0)
163.1 (10.5)
0.54
*Significant speed effect at p < 0.05, †significantly different from 2.22 m∙s−1.
Lindsay et al. Journal of NeuroEngineering and Rehabilitation 2014, 11:162
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Accelerations measured at the lumbar spine originate
from the GRF, which is transmitted through the foot,
shank, thigh, and pelvis. GRF at the shank is typically bi-
phasic and is significantly attenuated at proximal body
segments [20,21]. The two GRF peaks are associated
with impact and propulsion [22,23], with resultant body
segment acceleration depending on GRF magnitude and
damping effects [24]. The magnitude of force applied to
the ground depends, in part, on the stiffness of the
lower extremities, as does the acceleration resulting
from the GRF.
According to the regressions, increased RMS accelera-
tions were associated with different combinations of the
following kinematic characteristics during early stance
phase, depending on the axis: increased hip flexion, de-
creased knee flexion, and decreased foot advance. Most
studies demonstrate that a combination of increased hip
flexion and decreased ankle dorsiflexion at foot contact
is associated with alterations in GRF during foot contact
in various settings, providing evidence for the role of
both the quadriceps and the ankle dorsiflexor muscles in
shock absorption [25-30]. Our data did not demonstrate
the importance of ankle dynamics in shock attenuation,
but did highlight the role of hip angle in positioning the
quadriceps for shock attenuation during running [21,31].
Indeed, decreased FA was associated with increased AP,
VT, and RES acceleration. In contrast, a greater FA leads
to a flatter angle of attack (i.e., angle between segment
and the ground), which results in high lengthening rates
and decreased rate and magnitude of loading [32].
During the late stance and propulsion phase, a greater
hip extension and ankle plantar flexion at toe off were as-
sociated with greater RMS acceleration. According to
modeling by Hamner et al. [30], the soleus and gastrocne-
mius provide the biggest contributions to the propulsion
phase. Data from the present study supports the import-
ant role of the ankle plantar flexors in propulsion.
Kinematic observations in the current study are similar
to changes observed by McMahon et al. [16] when per-
forming a “Groucho running” intervention. In that study,
reductions in leg stiffness were associated with reduced
GRF and increased metabolic cost and are accomplished
by increased knee flexion. In contrast, in the present study
this appears to be facilitated by increased hip joint excur-
sion and a decreased foot advance. We note that Groucho
running is an exaggerated style for the purpose of estab-
lishing a relationship, and not intended for exercise and
performance purposes. The subjects in the present study
used a freely-chosen technique and were not given any in-
struction to modify their form. Still, the kinematic descrip-
tions we provide would seem to be subject to modification
with skill training [17,18].
There is also evidence that the level of acceleration
may be modified with training. We have previously
shown that the vertical accelerations of trained collegiate
runners are lower than untrained individuals but greater
than triathletes with similar fitness and training volume
[33]. This may represent an optimization of the different
performance requirements and injury risk between the
different groups because the optimal magnitude of verti-
cal accelerations for performance may be different than
what is optimal for minimizing risk of injury, and both
may be different from sport to sport. Acceleration mag-
nitude and stiffness may reflect several aspects of phys-
ical function during running such as energy expenditure,
impact forces relating to stress and injury, and perform-
ance [10,11]. Often, one aspect must be compromised if
another is to be maximized. For example, high impact
forces accompanying high limb stiffness may increase
energy return and performance according to the spring-
mass model but may require more energy and occur at
the expense of an overuse injury [32].
In the current study, we employ a simple approach to
modeling, including kinematic descriptions and a single
acceleration quantity for each axis (representing accelera-
tions over the entire gait cycle); this work represents an
easily accessible method with the potential for real-time
output. Although it is not possible to fully account for the
myriad of interactions between force, acceleration, stiff-
ness, effective segmental mass, performance, and injury,
Table 4 Regression results for original variables
Dependent Adjusted R2 Independent
Beta
p
Observed
power
MLRMS
0.714a
Hip-max
0.009
< 0.001
1.00
Hip-TO
−0.005 < 0.001
Knee-FS
−0.003
0.002
Knee-MS
−0.003
0.020
APRMS
0.718b
Hip-max
0.016
< 0.001
1.00
Hip-MS
−0.010 < 0.001
Hip-TO
−0.008 < 0.001
FA
−0.010 < 0.001
RISE
−0.020
0.001
VTRMS
0.795c
Hip-FS
0.034
< 0.001
1.00
FA
−0.027 < 0.001
Hip-MS
−0.030 < 0.001
Hip-max
0.010
< 0.001
Ankle-TO
−0.014 < 0.001
RESRMS
0.822d
Hip-Max
0.017
< 0.001
1.00
FA
−0.028 < 0.001
Hip-FS
0.034
< 0.001
Hip-MS
−0.036 < 0.001
Ankle-TO
−0.014 < 0.001
aF(4,51) = 35.362, p < 0.001; bF(5,50) = 28.960, p < 0.001; cF(5,50) = 45.542,
p < 0.001. dRES: F(5,50) = 51.967, p < 0.001.
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there have been several reports of the benefits of interven-
tions using acceleration as an outcome variable [17,18,34].
Data reduction via PCA facilitates the tracking of such
characteristics because the number of features becomes
relatively smaller [35]. Indeed, satisfactory descriptions of
walking gait using PCA (~80-90% explained variance)
Table 5 Rotated component matrix from principal
component analysis
Component
1
2
3
4
Hip-MS
0.910*
−0.058
0.081
−0.127
Hip-FS
0.906*
0.022
−0.016
0.134
Knee-MS
0.897*
0.034
0.239
0.213
Hip-max
0.852*
−0.146
0.157
0.105
Ankle-TO
−0.114
0.888*
−0.033
−0.295
Knee-TO
0.345
0.812*
0.076
0.069
RISE
0.599
−0.658*
−0.063
−0.009
PR
0.172
−0.517*
−0.054
−0.122
Knee-FS
0.342
−0.004
0.902*
−0.074
DROP
0.600
−0.006
−0.667*
0.243
Ankle-FS
0.505
0.305
0.514*
0.117
FA
0.352
0.062
−0.172
0.810*
Hip-TO
0.401
0.588
−0.112
−0.591*
Bold font and *indicates grouping for each component.
Figure 1 Predicted RMS acceleration versus measured RMS acceleration values. Graphs indicate: (a) ML, b) AP, c) VT, and d) RES.
Each graph includes data from all three speeds. Most biomechanical variables did not show a speed effect.
Table 6 Regression results for principal components
Dependent Adjusted R2
Predictors
Beta
p
Observed
power
MLRMS
0.322a
Component 1
0.418
< 0.001
1.00
Component 2 −0.415 < 0.001
APRMS
0.058b
Component 3
0.273
0.041
1.00
VTRMS
0.401c
Component 2 −0.529 < 0.001
1.00
Component 1
0.378
0.001
RESRMS
0.380d
Component 2 −0.513 < 0.001
1.00
Component 1
0.374
0.001
aF(2,53) = 14.066, p < 0.001; bF(1,54) = 4.364, p = 0.041; cF(2,53) = 19.412, p < 0.001.
dF(2,53) = 17.875, p < 0.001.
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applied to continuous waveforms of joint markers or joint
ankles have been obtained with only the first three or four
principal components [35-39]. However, sometimes only
one of many principal components is significantly differ-
ent between subject groups (fallers vs. non-fallers, over-
weight vs. normal weight) or experimental conditions
(loaded vs. non-loaded) in walking tasks [40,41]. The re-
duction of the kinematic variables into four principal
components may aid conceptualization of the key gait
characteristics that contribute to the magnitude of ac-
celerations. That it was possible to form components
from the different biomechanical variables is likely indi-
cative of movement synergies employed by the individ-
ual as a motor strategy [37]. To the extent that this
strategy can be altered, this presents an opportunity to
modify force production and impact absorption.
The discrete values used in the present study represent
an a priori reduction from continuous waveform data,
and may be seen as a limitation, but the maximum and
minimum values found in a waveform can often be the
regions of most significant difference [40], and would
thus likely be captured at points during the gait cycle
that we examined. As well, the complete dataset of bio-
mechanical variables displayed greater explained vari-
ance than the principal components. This may indicate
that the reduction of the complete dataset results in the
loss of important information that is explanatory with
regard to gait dynamics. However, this does not neces-
sarily diminish the value in identifying otherwise hidden
synergistic relationships perhaps indicative of a neuro-
muscular strategy. Another limitation is the small num-
ber of biomechanical variables chosen for analysis.
While the selection of a few readily modifiable variables
provides a simple preliminary analysis, there are other
variables that have not been included that potentially
affect RMS trunk acceleration. Indeed, our measure-
ments focused on movement in the sagittal plane, but
this neglects frontal plane dynamics that may influence
medio-lateral acceleration. Because the accelerometer only
approximates COM movement, the findings are limited if
an explanation of COM per se is desired. However, if the
goal is to investigate what contributes to measured accel-
erations, and explain previous findings (c.f. McGregor [8])
then the factors highlighted in this paper provide a basis
for future investigations.
Conclusions
This study helps to establish the use of lumbar-
mounted accelerometers to demonstrate effects related
to stiffness, impact, and the attenuation of acceleration.
Previous work has demonstrated the connection be-
tween RMS accelerations and energy expenditure [8].
Our present data provides a more mechanistic explan-
ation of how various kinematic configurations may
influence the multi-segmental force cascade from the
foot-ground interface to the lumbar vertebrae where ac-
celerations are measured. Specifically, we have identi-
fied the role of hip and knee angles in shock absorption
and the role of the hip and ankle in propulsion. In
addition to establishing these key biomechanical con-
tributors to acceleration, we showed how many of these
variables change in concert. Wherever these variables
are modifiable, the acceleration signal may be a useful
way to monitor movement with a view to performance
and injury management. Our findings pertain to young,
healthy, and active men and women, but the relation-
ships found here can form the basis from which more
specific subject groups may be studied in the future.
Appendix
Statistical tests
The following steps and equations are based on Moe-
Nilssen [9]. For a dataset of large N, the acceleration
vector approaches the sine of the angle of that vector:
limaML ¼ sinθML
ð3Þ
limaAP ¼ sinθAP
ð4Þ
The coordinate system definitions must be strictly
maintained so that the positive/negative signs are correct
and the relationships hold true. The following correc-
tions were applied to each of the axes:
aAPcorr ¼ aAPmeas⋅ cosθAP−aVTmeas⋅ sinθAP
ð5Þ
aVTprov ¼ aAPmeas⋅ sinθAP þ aVTmeas⋅ cosθAP
ð6Þ
aMLcorr ¼ aMLmeas⋅ cosθML−aVTprov⋅ sinθML
ð7Þ
aVTcorr ¼ aMLmeas⋅ sinθML þ aVTprov⋅ cosθML þ 1
ð8Þ
where corr, meas, and prov refer to corrected, measured,
and provisional terms, respectively. The static compo-
nent of gravity was also corrected for the VT axis, leav-
ing only the dynamic acceleration component.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
TL participated in study design, carried out data collection, performed data
processing, and drafted the manuscript. JY participated in interpretation of
data and drafting the manuscript. SM conceived of the study, performed
statistical analysis, participated in interpretation of data and drafting of the
manuscript. All authors read and approved the final manuscript.
Acknowledgements
The authors are thankful to the subjects and for data collection assistance
from Aaron Stickel, Lucas Wall, Zach Maino, Ken Hayes, and Ann Brennan.
Author details
1School of Health Promotion & Human Performance, Eastern Michigan
University, Ypsilanti, MI, USA. 2School of Health Sciences & Human
Performance, Ithaca College, Ithaca, NY, USA.
Lindsay et al. Journal of NeuroEngineering and Rehabilitation 2014, 11:162
Page 7 of 8
http://www.jneuroengrehab.com/content/11/1/162
Received: 2 May 2013 Accepted: 20 November 2014
Published: 12 December 2014
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doi:10.1186/1743-0003-11-162
Cite this article as: Lindsay et al.: Contributions of lower extremity
kinematics to trunk accelerations during moderate treadmill running.
Journal of NeuroEngineering and Rehabilitation 2014 11:162.
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Lindsay et al. Journal of NeuroEngineering and Rehabilitation 2014, 11:162
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Contributions of lower extremity kinematics to trunk accelerations during moderate treadmill running.
Lindsay, Timothy R,Yaggie, James A,McGregor, Stephen J