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Measuring Effects of Two-Handed Side and Anterior Load Carriage on Thoracic-Pelvic Coordination Using Wearable Gyroscopes

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Manual carrying of heavy weight poses a major risk for work-related low back injury. Body-worn inertial sensors present opportunities to study the effects of ambulatory work tasks such as load carriage in more realistic conditions. An immediate effect of load carriage is reflected in altered gait kinematics. To determine the effects of load carriage mode and magnitude on gait parameters using body-worn angular rate gyroscopes, two laboratory experiments (n = 9 and n = 10, respectively) were conducted. Participants performed walk trials at self-selected speeds while carrying hand loads in two modes (two-handed side vs. anterior) at four load levels (empty-handed, 4.5 kg, 9.1 kg, and 13.6 kg). Six measures of postural sway and three measures of thoracic-pelvic coordination were calculated from data recorded by four body-worn gyroscopes for 1517 gait cycles. Results demonstrated that, after adjusting for relative walking speed, thoracic-pelvic sway, and movement coordination particularly in the coronal and transverse planes, characterized by gyroscope-based kinematic gait parameters, are systematically altered by the mode of load carriage and load magnitude. Similar trends were obtained for an anthropometrically homogenous (Expt-1) and diverse (Expt-2) sample after adjusting for individual differences in relative walking speed. Measures of thoracic-pelvic coordination and sway showed trends of significant practical relevance and may provide sufficient information to typify alterations in gait across two-handed side vs. anterior load carriage of different load magnitudes. This study contributes to understanding the effects of manual load carriage on thoracic-pelvic movement and the potential application of body-worn gyroscopes to measuring these gait adaptations in naturalistic work settings.
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sensors
Article
Measuring Effects of Two-Handed Side and Anterior
Load Carriage on Thoracic-Pelvic Coordination Using
Wearable Gyroscopes
Sol Lim 1,* and Clive D’Souza 2
1Department of Systems and Industrial Engineering, The University of Arizona, Tucson, AZ 85721, USA
2
Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109, USA;
crdsouza@umich.edu
*Correspondence: lims@arizona.edu; Tel.: +1-520-626-0728
Received: 31 July 2020; Accepted: 7 September 2020; Published: 12 September 2020


Abstract:
Manual carrying of heavy weight poses a major risk for work-related low back injury.
Body-worn inertial sensors present opportunities to study the effects of ambulatory work tasks
such as load carriage in more realistic conditions. An immediate effect of load carriage is reflected
in altered gait kinematics. To determine the effects of load carriage mode and magnitude on gait
parameters using body-worn angular rate gyroscopes, two laboratory experiments (n= 9 and n= 10,
respectively) were conducted. Participants performed walk trials at self-selected speeds while
carrying hand loads in two modes (two-handed side vs. anterior) at four load levels (empty-handed,
4.5 kg, 9.1 kg, and 13.6 kg). Six measures of postural sway and three measures of thoracic-pelvic
coordination were calculated from data recorded by four body-worn gyroscopes for 1517 gait
cycles. Results demonstrated that, after adjusting for relative walking speed, thoracic-pelvic sway,
and movement coordination particularly in the coronal and transverse planes, characterized by
gyroscope-based kinematic gait parameters, are systematically altered by the mode of load carriage
and load magnitude. Similar trends were obtained for an anthropometrically homogenous (Expt-1)
and diverse (Expt-2) sample after adjusting for individual differences in relative walking speed.
Measures of thoracic-pelvic coordination and sway showed trends of significant practical relevance
and may provide sufficient information to typify alterations in gait across two-handed side vs. anterior
load carriage of different load magnitudes. This study contributes to understanding the effects of
manual load carriage on thoracic-pelvic movement and the potential application of body-worn
gyroscopes to measuring these gait adaptations in naturalistic work settings.
Keywords:
load carriage; gyroscope; gait kinematics; gait detection; thoracic-pelvic coordination;
wearable sensor
1. Introduction
Load carriage is common in daily activities. People often choose to carry loads in their hands in an
anterior or lateral location for convenience over short distances or for intermittent periods. Manual load
carriage is also prevalent and inevitable in many occupations such as military [
1
], firefighting [
2
],
and construction work [
3
], and for Manual Material Handling (MMH) work in manufacturing [
4
],
warehousing [
5
], packaging, and distribution [
6
,
7
]. In a comprehensive study of MMH conducted
at 2442 industrial locations, load carriage represented 15.7% of all MMH tasks recorded and was
superseded only by lifting-lowering [
8
]. Routine load carriage is associated with an increased risk of
work-related Musculoskeletal Disorders (MSDs) in the back [
9
,
10
] and upper and lower limbs [
11
,
12
].
A study investigating the relationship between load carriage and low back MSD prevalence among
steel-workers found that frequent load carriage of objects weighing 5 to 15 kgs was highly associated
Sensors 2020,20, 5206; doi:10.3390/s20185206 www.mdpi.com/journal/sensors
Sensors 2020,20, 5206 2 of 28
with low back pain and injury with an odds ratio of 7.2 (95% CI: 1.60–32.4) [
13
]. Prior studies associate
the increase of hand loads to increased torso and hip moments [
14
16
], increased back and abdominal
muscle activity for improved spinal stability [
17
19
], and increased compressive and shear loads on
the lumbar spine [3,2022].
Beyond temporal aspects of duration and repetition, the risk from load carriage to the musculoskeletal
system also depends on the load magnitude and the mode or manner by which the load is carried [
22
,
23
].
The latter alters the position of the load relative to the spine thus affecting compressive and shear
stresses on the spine. Prior studies have quantified the increased risk of spinal injury from load carriage
by using analytical models to estimate spinal loads. McGill et al.
[20]
estimated spinal compressive loads
to be significantly higher when carrying weights in one hand compared to both hands. Rose et al.
[22]
investigated compressive and shear loads in the lumbar spine in various modes of load carriage
including one-handed side carry, shoulder carry, two-handed anterior carry, and posterior carry.
They concluded that, for the same load level, two-handed anterior carry resulted in the largest increase
in anterior-posterior shear loading and thereby posed the greatest relative risk of low back injury [
22
].
Rohlmann et al.
[24]
estimated that lumbar compression force with respect to standing was nearly
twice as high for carrying a weight in front of the body compared to carrying it laterally.
Human gait encapsulates movement with the arms, head, legs, and torso. The movements
of each segment may seem variable and arbitrary, but, in fact, are repeatable and predictable.
This is particularly true of kinematic adjustments for maintaining postural stability in response
to external hand loads while walking. Effects of posterior loads such as from carrying a backpack
or rucksack on gait patterns [
25
28
] has received the most attention due to its relevance to certain
vulnerable populations, e.g., military soldiers [
29
,
30
], firefighters [
2
], and school children [
31
,
32
].
Increasing backpack load was associated with a decrease in swing duration [
33
], single support
duration [
28
], and stride length [
25
], and an increase in double support duration [
28
] and stride
frequency [
25
]. Increasing posterior loads also produced changes in postural kinematics such as
increased mean torso inclination [
27
,
28
,
30
], decreased torso angular acceleration [
14
], and decreased
transverse torso and pelvis rotation [25].
Biomechanical loading from load carriage also affects the way people move their torso and pelvis
when walking. Relative phase angle, described by the phase difference or relative phase between
two oscillating segments, provides a measure of coordination between multiple body segments
or joints during complex and repetitive multi-joint movements [
34
]. In conventional gait analysis,
relative phase angles are used as a measure of rotational thoracic-pelvic coordination [
16
,
25
,
35
37
].
Graham et al. [38] used
variability in thoracic-pelvic relative phase angles as a measure of postural
stability to compare posterior vs. anterior load carriage. Phase angle relationships between torso and
pelvis may provide sufficiently sensitive measurements to characterize changes in intersegment
coordination induced by alterations in load moment associated with task variables, specifically
object mass and carrying mode.
A limitation of prior studies using intersegment relative phase angles is that they relied on
optical motion tracking with participants walking at discrete and precise speeds on a treadmill.
These studies do not directly extend to naturalistic gait speeds adopted by workers in applied
work settings. Wearable inertial sensors, which include accelerometers, inclinometers, gyroscopes,
and inertial measurement units (IMUs), have gained considerable attention in biomechanics research
as an inexpensive and less obtrusive form of bioinstrumentation [
39
]. The portability and low
power consumption of inertial sensors make them suitable for monitoring ambulation under
naturalistic conditions, potentially outdoors and over long durations [
40
,
41
]. Multiple studies have
demonstrated the validity and reliability of commercial, body-worn inertial sensors and related data
processing algorithms using data from angular rate gyroscopes and/or linear accelerometers to analyze
able-bodied and pathological gait [
42
46
]. In comparison, the application of body-worn inertial sensors
to analyze ambulatory occupational tasks such as MMH and load carriage is still emerging [47].
Sensors 2020,20, 5206 3 of 28
The objective of this study was to quantify and compare the effects of a two-handed side and
anterior load carriage performed at self-selected walking speeds on the amplitude and coordination of
thoracic and pelvic rotations obtained from body-worn gyroscopes. The magnitude and mode of load
carriage were hypothesized to influence torso and pelvic sway and thoracic-pelvic coordination during
load carriage relative to unloaded gait after accounting for individual walking speed. To demonstrate
generalizability of the findings, two separate experiments with similar methods were conducted.
The experiments differed in the homogeneity in the sample composition and the make/brand of
commercial wearable sensor used, and are presented separately. Consistent results from the two
experiments were considered to indicate converging evidence, while inconsistent results could
reflect effects specific to a particular experiment design. Previously validated algorithms from the
biomechanics literature were adapted for extracting gait parameters from body-worn gyroscopes and
are summarized in the appendices.
2. Experiment 1
2.1. Materials and Methods
2.1.1. Participants
The study sample in Experiment-1 comprised nine healthy male individuals aged between 18 to
35 years old. Prior to participation, participants provided written informed consent and were screened
for pre-existing back injuries or chronic pain with a body discomfort questionnaire adapted from the
body mapping exercise developed by NIOSH [
48
]. Gender and health restrictions were applied in
order to minimize potential inter-subject variability in gait patterns from these sources in the interest
of obtaining a homogeneous sample. Participants’ stature and mass were measured to calculate Body
Mass Index (BMI; kg/m
2
). The study was approved by the University of Michigan’s institutional
review board.
2.1.2. Experiment Procedure
A laboratory experiment was conducted that required participants to carry a weighted box on
a straight, marked path (26.2 m length
×
1.6 m width) with a levelled-floor for a distance of 24 m
in two carrying modes, namely, a two-handed side carry and two-handed anterior carry (Figure 1).
Four levels of box weights were evaluated in each mode: no-load (i.e., unloaded, empty-handed
reference condition), 4.5 kg, 9.1 kg, and 13.6 kg. The load levels were 20%, 40%, and 60%, respectively,
of the maximum permissible lifting load of 23 kg specified by the NIOSH Lifting Equation [
49
].
Magnitudes and distances were informed by previous field-based studies on MMH in industrial
settings [
7
,
8
] and laboratory studies on manual load carriage, e.g., [
3
,
22
,
50
]. The two-handed side
load carriage was performed using a box in each hand with a handle on the top (152.4 mm width
×
177.8 mm depth
×
127 mm height, Figure 1a). The two-handed anterior carry was performed using one
box with two handles on the sides (177.8 mm width
×
228.6 mm depth
×
203.3 mm height, Figure 1b).
To maintain symmetry in hand-loads, the combined weight in the anterior carry was equally divided
between the right and left hand in the two-handed side carry.
Two walk trials in the no-load condition were performed at the beginning of the experiment.
Subsequently, each participant performed two consecutive walk trials with hand loads in each
combination of carrying mode and load level, presented in random order to minimize any potential
influence of cumulative fatigue on gait kinematics. Participants were instructed to self-select a walking
speed for each condition that could be comfortably maintained over the two consecutive walk trials.
In the two-handed anterior condition, participants were instructed to hold the box close to their
torso with their elbow flexed 90. In order to minimize carryover effects of fatigue, participants were
provided a two-minute rest break between each condition and were allowed additional rest breaks
anytime if they requested.
Sensors 2020,20, 5206 4 of 28
Thigh (R)
Shank (R)
T6
S1
(a) (b) (c)
Figure 1.
Images showing the two carrying modes evaluated in this study: (
a
) two-handed side carry,
(
b
) two-handed anterior carry, along with the location of (
c
) four inertial sensors attached on the body
at T6 (Posterior), S1 (Posterior), thigh (Right), and shank (Right).
2.1.3. Instrumentation
During the walk trials, kinematic data were continuously recorded using four commercial inertial
sensors, namely, Opal (APDM Inc., Portland, OR, USA; dimension = 43.7
×
39.7
×
13.7 mm (L
×
W
×
H);
weight = 25 g; internal storage = 8 GB; https://www.apdm.com/). Each sensor is comprised of a 3-axis
accelerometer (±16 g), a 3-axis gyroscope (±2000 /s), and a 3-axis magnetometer (±8 Gauss).
Two sensors were attached using elastic Velcro straps over the sixth thoracic vertebra (T6) and the
first sacral vertebra (S1), respectively (Figure 1). One sensor was positioned along the superior aspect
of the right thigh midway between the hip and lateral femoral epicondyles, and the fourth sensor on
the superior aspect of the right shank midway between the lateral femoral and malleolar epicondyles,
and attached using double-sided hypoallergenic tape and medical tape wrap. Sensors were attached
with one of the sensor axes (i.e.,
x
-axis) aligned with the proximal-distal axis of the body segment and
pointing inferiorly.
2.1.4. Data Processing and Dependent Measures
Only gyroscope data were utilized in this study. The four inertial sensors synchronously recorded
triaxial gyroscope data at a sampling frequency of 80 Hz. Each walk trial lasted no more than
30 s. Raw gyroscope data for each walk trial were filtered using a second-order low-pass zero-lag
Butterworth filter with a cut-off frequency of 2 Hz.
Next, a custom software algorithm implemented in MATLAB (MATLAB R2016b, The MathWorks Inc.,
Natick, MA, USA) was used for computing particular spatio-temporal gait parameters. Specifically,
gait cycle duration and stride length were computed in order to segment the continuous sensor data into
discrete gait cycles. Filtered data were integrated to obtain angular displacement, and subsequently filtered
using a second-order high-pass filter with a cut-off frequency of 0.75 Hz to reduce the effect of drift [
51
].
Torso (T6) and pelvic (S1) sway and their corresponding thoracic-pelvic coordination in the form of relative
phase angles in the coronal, transverse, and sagittal planes respectively were computed from the gyroscope
data for each segmented gait cycle. Table 1provides definitions and sensor locations used for computing
these 11 parameters with the computational procedure briefly described below.
Spatio-temporal parameters. First, heel-strike (i.e., when the foot first touches the floor) and toe-off
events were detected using the filtered angular velocity data (sagittal plane) obtained from the
gyroscope on the right shank using an algorithm adapted from Aminian et al. [
42
] and validated
in multiple prior studies [
52
,
53
]. Time duration between two consecutive right heel-strikes were
Sensors 2020,20, 5206 5 of 28
denoted as one gait cycle, which is summarized in Appendix A. In a study to validate the algorithm by
comparing gyroscope-derived gait events to corresponding measurement from foot pressure sensors,
Aminian et al.
[42]
reported no statistically significant error for toe-off detection, and an average delay
of 10 ms for detecting heel-strike events.
Stride length was estimated using the double segment gait model by Aminian et al.
[42]
and
summarized in Appendix B. The double segment gait model estimates stride length by considering
the thigh and shank as a double pendulum during the swing phase, and likewise as an inverted
double pendulum during the stance phase with the assumption of symmetry between both legs [
42
].
A previous validation of this algorithm reported RMSE’s of 23 ms for gait cycle duration, and 7 cm
(7.2%) for stride length compared to reference gait data obtained from foot pressure sensors [42].
Torso and Pelvis Postural Sway. For each gait cycle, six measures of torso and pelvic sway were
calculated as the peak-to-peak range of motion (ROM) angles in the coronal, transverse, and sagittal
planes at the thoracic (sensor at T6) and pelvic (sensor at S1) segment location, respectively. The angular
displacements were calculated by integrating the filtered gyroscope data within each gait cycle (~1.0 s
average duration).
Thoracic-Pelvic Coordination. Thoracic-pelvic coordination was measured in each of the three anatomical
planes using the mean relative phase angle [
34
] computed at each data-frame and averaged over
each gait cycle (refer Appendix C). A higher mean relative phase angle implied an out-of-phase
or uncoordinated movement between the thorax and pelvis segments compared to more in-phase
(i.e., synchronized and coordinated movements) for lower mean relative phase angles.
2.1.5. Statistical Analyses
Five gait cycles were extracted from each walk trial for analysis. The 2nd to 6th gait cycle were
used in order to minimize the effects of acceleration and deceleration near the start and end of the
walk trials. Eleven gait parameters were computed for a total of 720 gait cycles (i.e., 9 participants
×
8
conditions
×
2 repetitions per condition
×
5 gait cycles per walk trial). Computed gait parameters
were averaged over five gait cycles within each walk trial for subsequent statistical analyses.
Statistical analyses were conducted in two stages. First, descriptive statistics and Pearson
correlation coefficients for the nine thoracic-pelvic sway and coordination parameters over two walk
trials each averaged over five gait cycles per condition were calculated. Second, separate mixed effects
models [
54
] implemented in SPSS v.26 (IBM Inc., New York, NY, USA) were used to quantify the
effects of within-subject variables, viz., carrying mode (two-handed side carry vs. two-handed anterior
carry) and load level (no-load, 4.5 kg, 9.1 kg, and 13.6 kg) and their two-way interaction effect on
each of the nine gait parameters. Self-selected walking speed was a potential confounding variable
since it influences gait posture, and is known to be influenced by leg length (or stature) [
55
] as well
as external load conditions [
56
]. Relative speed (walking speed/leg length; m/s) is commonly used
in gait studies as a measure of walking speed (stride length/gait cycle duration, m/s) normalized to
individual anthropometry, typically leg length or stature (e.g., [
57
59
]). Thus, the centered value of
relative walking speed (labeled ’centered relative speed’) was included in each mixed effects model
as a covariate (i.e., set as a fixed effect and random effect parameter) to adjust for differences in
anthropometry and walking speed. Two walk trials in each condition were included as repeated
measures. Residual errors from the mixed model estimates were examined graphically and confirmed
that model assumptions of normality (Q-Q plots) and homogeneity of variances (box-plots of residuals
across carrying mode and load levels) were satisfied. Significant main and interaction effects (
p<
0.05)
were examined using Bonferroni post-hoc tests which adjusts for Type-I error rates to analyze paired
comparisons between carrying mode at each level of load level and vice versa. Estimated marginal
means and standard errors (
±
SE) for the 9 gait parameters obtained from the mixed effects models were
tabulated, stratified by carrying mode and load level, at the average values of centered relative speed.
Sensors 2020,20, 5206 6 of 28
Table 1.
List and definitions of gait parameters calculated from inertial sensor data. In each row, location of sensors used for calculating the parameters are indicated
by ’’ with relevant source reference. Connected dots indicate pairs of sensors used together.
Parameter Definition Inertial Sensor Location Source
T6 S1 R. Thigh R. Shank
Spatio-temporal parameters
1 Gait cycle duration (sec)
The duration of one gait cycle (one right plus left step
duration calculated as the time between two consecutive
right heel-strikes)
[42]
2 Stride length (cm)
The length moved from right heel-strike to the next right
heel-strike during one gait cycle
[42]
Torso and pelvis postural sway
3, 4 Coronal ROM at T6 & S1 ()
Range of rotation angle in coronal plane: Max (integrated
angular velocity,
z
-axis) - min (integrated angular velocity,
z-axis)
• • [25]
5, 6 Transverse ROM at T6 & S1 () Same calculation as above in transverse plane: x-axis • •
7, 8 Sagittal ROM at T6 & S1 () Same calculation as above in sagittal plane: y-axis • •
Thoracic-pelvic coordination
9
Coronal mean relative phase
angle between T6 and S1 ()
Average (pelvic phase angle - thoracic phase angle). Phase
angle (t) = arctan (normalized angular velocity,
z
-axis
(t)/normalized integrated angular velocity, z-axis (t))
——[25,34,60]
10
Transverse mean relative phase
angle between T6 and S1 ()
Same calculation as above in transverse plane: x-axis ——
11
Sagittal mean relative phase
angle between T6 and S1 ()
Same calculation as above in sagittal plane: y-axis ——
Sensors 2020,20, 5206 7 of 28
2.2. Results
The nine participants had an average (
±
standard deviation, SD) age of 21.7
±
2.7 years
(range: 18–27 years), stature 1.76
±
0.06 m (range: 1.64–1.84 m), leg length 0.94
±
0.06 m (range:
0.84–1.04 m), body mass 77.47
±
10.64 kg (range: 64.95–93.26 kg), and BMI 24.87
±
2.84 kg/m
2
(range: 20.99–30.36 kg/m
2
). Average relative speed across all gait cycles were 1.09
±
0.08 m/s (range:
0.96–1.24 m/s). Correlation coefficients between pairs of sway and coordination parameters were
below 0.51.
Table 2summarizes the results from the mixed effects analyses of carrying mode and load level
on thoracic and pelvic sway and coordination. Table 3provides the estimated marginal means (
±
SE)
for these parameters with statistically significant (
p<
0.05) effects of carrying mode and load level
relative to the no-load condition, at average values of the covariate, centered relative speed. Significant
trends across carrying mode and load level are discussed in the subsequent section.
2.2.1. Torso and Pelvis Sway and Coordination in the Coronal Plane
In the coronal plane, thoracic sway was significantly more in the no-load and low load levels vs.
medium, and high load conditions (Table 2), while pelvic sway did not indicate any significant
differences between carrying mode and load level. A significant interaction effect of carrying mode and
load on mean coronal relative phase angle was obtained. Coronal relative phase angle was significantly
less in the anterior carry vs. side carry mode for the low, medium and high load conditions separately,
suggesting more in-phase thoracic-pelvic movement in the anterior vs. side carry condition (Figure 2).
In the side carry condition, the mean relative phase angle was significantly more in the high load
condition vs. the medium load, low load, and no-load conditions. Conversely, in the anterior carry
condition, the mean coronal relative phase angle was significantly higher in the no-load condition
compared to each of the loaded conditions.
2.2.2. Torso and Pelvis Sway and Coordination in the Transverse Plane
In the transverse plane, thoracic rotation was not significantly different across carrying mode
and load level. However, transverse rotation at the pelvis was significantly more in the side carry
compared to the anterior carry. The mean relative phase angle in the transverse plane was significantly
less in the side carry vs. anterior carry. The mean relative phase angle in the transverse plane was
significantly less in all of the loaded conditions compared to the unloaded condition.
2.2.3. Torso and Pelvis Sway and Coordination in the Sagittal Plane
A significant interaction effect of carrying mode and load level on thoracic rotation in the sagittal
plane was obtained. Thoracic rotation was significantly more in the side carry vs. anterior carry mode
for the low, medium, and high load conditions separately. Pelvic sagittal rotation was not significantly
different among carrying modes and load levels. Mean sagittal relative phase angle was significantly
more in the side carry vs. anterior carry mode.
Sensors 2020,20, 5206 8 of 28
Table 2.
Experiment-1 (n= 9) summary results from the mixed effects analyses for the main and interaction effects of carrying mode and load level on torso and pelvis
sway and thoracic-pelvic coordination measures in the coronal, transverse, and sagittal planes. Significant pair-wise Bonferroni comparisons (
p
< 0.05) are provided
for main and interaction effects that were significant at
p
< 0.05 indicated by *. NL = No-load (empty-handed), L = Low load at 4.5 kg, M = Medium load at 9.1 kg,
H = High load at 13.6 kg.
Carrying Mode Load Level Carrying Mode
×Load Level
Relative Speed,
Centered (s)
Coronal plane
ROM at T6 ()F(1, 114) = 0.24, p= 0.623 F(3, 53) = 11.89, p< 0.001 * F(3,53) = 0.15, p= 0.930 F(1, 106) = 112.67, p< 0.001 *
(NL, L) > (M, H)
ROM at S1 ()F(1, 130) = 0.02, p= 0.891 F(3, 66) = 2.52, p= 0.066 F(3, 66) = 0.11, p= 0.952 F(1, 113) = 8.05, p= 0.005 *
Mean relative phase angle ()F(1, 101) = 96.96, p< 0.001 * F(3, 71) = 1.72, p= 0.170 F(3, 72) = 15.35, p< 0.001 * F(1, 95) = 39.03, p< 0.001 *
Side > Anterior Side: H > (M, L, NL)
Anterior: NL > (L, M, H)
H, M, L: Side > Anterior
Transverse plane
ROM at T6 ()F(1, 118) = 1.65, p= 0.202 F(3, 55) = 0.61, p= 0.612 F(3, 55) = 1.17, p= 0.330 F(1, 1) = 18.46, p= 1.0
ROM at S1 ()F(1, 101) = 7.41, p= 0.008 * F(3, 63) = 1.33, p= 0.272 F(3, 63) = 1.47, p= 0.231 F(1, 95) = 7.03, p= 0.009 *
Side > Anterior
Mean relative phase angle ()F(1, 128) = 8.59, p= 0.004 * F(3, 62) = 4.98, p= 0.004 * F(3, 62) = 1.07, p= 0.371 F(1, 110) = 0.04, p= 0.842
Anterior > Side (NL, L) > (M, H)
Sagittal plane
ROM at T6 ()F(1, 113) = 28.55, p< 0.001 * F(3, 60) = 0.58, p= 0.630 F(3, 60) = 3.11, p= 0.033 * F(1, 91) = 0.08, p= 0.775
Side > Anterior H, M, L: Side > Anterior
ROM at S1 ()F(1, 128) = 2.95, p= 0.088 F(3, 65) = 2.07, p= 0.113 F(3, 65) = 0.69, p= 0.565 F(1, 1) = 16.35, p= 1.0
Mean relative phase angle ()F(1, 122) = 7.14, p= 0.009 * F(3, 57) = 0.53, p= 0.664 F(3, 57) = 1.30, p= 0.284 F(1, 103) = 7.24, p= 0.008 *
Side > Anterior
Sensors 2020,20, 5206 9 of 28
Table 3.
Experiment-1 (n= 9) estimated marginal mean
±
SE for the No-load condition, and statistically significant mean differences
±
SE relative to the no-load
condition by carrying mode and load level, at average values of centered relative speed. Significant mean differences relative to the no-load condition by carrying
mode across different load levels are within the square brackets (i.e., [- - mean ±SE - -]).
Side Carry Anterior Carry
No-Load Low (4.5 kg) Medium (9.1 kg) High (13.6 kg) Low (4.5 kg) Medium (9.1 kg) High (13.6 kg)
Coronal plane
ROM at T6 () 5.6 ±0.3 - 1.7 ±0.3 1.8 ±0.4 - 1.7 ±0.3 1.8 ±0.4
ROM at S1 () 7.5 ±0.4 - - - - - -
Mean relative phase angle () 109.5 ±5.2 +5.0 ±7.0 +8.4 ±6.0 +23.6 ±5.9 22.2 ±6.0 22.9 ±6.5 30.2 ±6.1
Transverse plane
ROM at T6 () 5.6 ±0.4 - - - - - -
ROM at S1 () 7.4 ±0.4 [------------ +1.7±0.6 --------------] - - -
Mean relative phase angle () 102.2 ±7.6 30.5 ±10.7 30.4 ±10.5 33.8 ±9.1 - 11.6 ±10.3 18.8 ±10.6
Sagittal plane
ROM at T6 () 2.9 ±0.2 +0.5 ±0.3 +0.5 ±0.3 +0.6 ±0.3 0.2 ±0.2 0.5 ±0.2 0.3 ±0.2
ROM at S1 () 3.8 ±0.7 - - - - - -
Mean relative phase angle () 81.7 ±3.5 [------------ +1.8±4.5 --------------] [------------- 9.0 ±4.5 --------------]
Sensors 2020,20, 5206 10 of 28
Torso
Pelvis
Torso
Pelvis
Front-view
Top-view
Figure 2.
Mean (
±
standard error) thoracic-pelvic relative phase angle in the coronal plane (
top
panel)
and transverse plane (
bottom
panel) obtained from the average gait cycle data of n= 9 participants
and normalized to individual gait cycle duration. The figure compares two-handed side carry and
two-handed anterior carry (colored blue and red, respectively) in high load level at 13.6 kg with respect
to the no-load (empty-handed; grey). Higher values of mean relative phase angles indicate out-of-phase
or less synchronized rotational movements between the torso and pelvis.
3. Experiment 2
3.1. Materials and Methods
3.1.1. Participants
Based on promising results obtained from Experiment-1, a second experiment was conducted.
Participants for Experiment-2 consisted of ten healthy male individuals aged between 18 to 55 years
old different from Experiment-1. Compared to the previous experiment, the age range for participant
recruitment was expanded to diversify the sample population. Participants were screened for
pre-existing back injuries or chronic pain and provided informed consent as in Experiment 1
(Section 2.1.1).
Sensors 2020,20, 5206 11 of 28
3.1.2. Experiment Procedure
Experiment conditions and data collections procedures were the same as in Experiment 1
(described in Section 2.1.2). The experiment had participants carry a weighted box in a straight,
marked path (12 m length ×1.6 m width) on a levelled floor for a distance of 10 m.
3.1.3. Instrumentation
Experiment 2 used a different commercial wearable inertial sensor (Biostamp RC; mc10 Inc.,
Lexington, MA, USA; dimension = 66
×
34
×
4.5 mm (L
×
W
×
H); weight = 7 g; internal storage = 32 MB;
https://www.mc10inc.com/), which consists of a flexible and conformal skin-adhesive patch with an
embedded 3-axis accelerometer (
±
16 g) and 3-axis gyroscope (
±
2000
/s) sensor. The Biostamp RC
was considered because of its comparable accuracy (e.g., [
52
,
61
]) and improved wearability relative to
the other commercial IMUs that have a hard enclosure and elaborate attachment process. The device is
also 510(k) cleared by FDA for medical use.
Prior to Experiment 2, a simple empirical validation of the two sensors, namely APDM Opal
(Expt-1) and BioStampRC (Expt-2) was performed to ensure the accuracy of their gyroscope-based
angular displacement estimates relative to an optical motion capture (Qualisys AB, Gotenburg, Sweden)
based estimate as reference. A simple pendulum consisting of a non-ferrous rigid arm (arm length:
460 mm; mass: 0.275 kg) was used since our study modelled the torso and pelvis as segments of an
oscillating inverted pendulum.
For validation, two passive optical markers (used for computing the reference angular displacement)
along wth an APDM Opal and Biostamp RC sensor were attached to the pendulum arm. Starting from
an initial angular displacement of approximately 75
, continuous oscillations were recorded for 30 s
durations (i.e., considered representative of a walk trial duration). For time synchronization between
different measurement systems, the pendulum arm was moved at a fast speed to create identifiable
peaks before the start of the pendulum movement. Time-synchronized 2D angular displacement
computed from the two gyroscope systems and passive optical markers from three trials of 30 s
duration each were compared. The same filtering methods were used for both gyroscope and optical
motion marker data. The average
±
SD RMSE between both gyroscopes was 0.22
±
0.09
, while the
RMSE between the optical motion capture and Opal and Biostamp RC was 0.53
±
0.06
and 0.31
±
0.03
,
respectively. The relatively low RMSE (
<
1
) between instrumentation types was deemed acceptable
for the purposes of comparing 2D angular displacement data from the respective sensors used in
Experiments 1 and 2. For the instrumented walk trials, four sensors were attached on the participant
at identical anatomical locations to Experiment-1 using the manufacturer-provided adhesive tape in
order to continuously record kinematic data during the walk trials. Sensor setup was the same as
Experiment 1 as described in Figure 1.
3.1.4. Data Processing, Dependent Measures, and Statistical Analysis
Data obtained from the experiment walk trials were processed in a manner identical to
Experiment 1
(Section 2.1.4)
. Eleven gait parameters were computed (Table 1) for a total of 797 gait
cycles (i.e., 10 participants
×
8 conditions
×
10 gait cycles per condition minus the 6th gait cycle
from three different walk trials discarded due to measurement issues). Statistical analyses identical to
Experiment-1 (Section 2.1.5) were performed.
3.2. Results
The ten participants had an average
±
SD age of 38.6
±
10.8 years (range: 22–55 years),
stature
1.81 ±0.05 m
(range: 1.72–1.88 m), leg length 0.96
±
0.05 m (range: 0.88–1.03 m), body mass
80.75
±
12.20 kg (range: 57.60–96.60 kg), and BMI 24.70
±
3.17 kg/m
2
(range: 19.47–28.85 kg/m
2
).
Average relative speed across all gait cycles were 1.01
±
0.15 m/s (range: 0.83–1.25 m/s). Correlation
coefficients between pairs of sway and coordination parameters were below 0.75.
Sensors 2020,20, 5206 12 of 28
Table 4summarizes results from the mixed effects analyses of carrying mode and load level on
torso and pelvic sway and coordination parameters. Table 5provides the estimated marginal means
(
±
SE) for these nine parameters with statistically significant (
p<
0.05) effects of carrying mode
and load level relative to the no-load condition, estimated at the average value of centered relative
speed (covariate).
3.2.1. Torso and Pelvis Sway and Coordination in the Coronal Plane
In the coronal plane, thoracic sway was significantly more in the no-load vs. the medium and
high load conditions, and also significantly more in the low vs. medium load condition. Pelvic sway
was significantly more in the side vs. anterior carry mode. A signficant interaction effect of carrying
mode and load level on mean coronal relative phase angle was obtained. Coronal relative phase angle
was significantly less in the anterior carry vs. side carry mode for the low, medium, and high load
conditions separately, suggesting more in-phase thoracic-pelvic movement in the anterior vs. side
carry condition akin to Experiment 1 (Figure 2). In the anterior carry mode, the mean relative phase
angle was significantly more in the no-load condition compared to the high load condition.
3.2.2. Torso and Pelvis Sway and Coordination in the Transverse Plane
In the transverse plane, thoracic sway was significantly more in the side carry compared to the
anterior carry mode. Pelvic sway was significantly more in the side carry vs. anterior carry mode in
each of the three loaded conditions separately. The mean relative phase angle in the transverse plane
was significantly less in medium and high load vs. unloaded conditions, regardless of carrying mode.
3.2.3. Torso and Pelvis Sway and Coordination in the Sagittal Plane
In the sagittal plane, a significant interaction effect of carrying mode and load level on thoracic
sway was noted. Thoracic sway was significantly more in the side carry vs. anterior carry mode in
the low, medium and high load conditions separately. Furthermore, in the side carry, the high and
medium load conditions showed significantly more thoracic sway compared to the no-load condition.
Pelvic sagittal rotation and mean sagittal relative phase angle were not significantly different between
different carrying modes and load levels.
Sensors 2020,20, 5206 13 of 28
Table 4.
Experiment-2 (n= 10) summary results from the mixed effects analyses for the main and interaction effects of carrying mode and load level on torso and
pelvis sway and thoracic-pelvic coordination in the coronal, transverse, and sagittal planes. Significant pairwise Bonferroni comparisons (
p
< 0.05) are provided
for main and interaction effects that were significant at
p
< 0.05 indicated by *. NL = No-load (empty-handed), L = Low load at 4.5 kg, M = Medium load at 9.1 kg,
H = High load at 13.6 kg.
Carrying Mode Load Level Carrying Mode
×Load Level
Relative Speed,
Centered (s)
Coronal plane
ROM at T6 ()F(1, 123) = 0.02, p= 0.896 F(3, 79) = 6.48, p= 0.001 * F(3, 78) = 0.69, p= 0.558 F(1, 93) = 48.81, p< 0.001 *
NL > (M, H), L > M
ROM at S1 ()F(1, 149) = 5.16, p= 0.025 * F(3, 72) = 0.91, p= 0.442 F(3, 71) = 0.71, p= 0.548 F(1, 124) = 24.91, p< 0.001 *
Side > Anterior
Mean relative phase angle ()F(1, 125) = 28.52, p< 0.001 * F(3, 76) = 1.12, p= 0.346 F(3, 75) = 3.91, p= 0.012 * F(1, 122) = 5.01, p= 0.027 *
Side > Anterior Anterior: NL > H
H, M, L: Side > Anterior
Transverse plane
ROM at T6 ()F(1, 86) = 5.96, p= 0.017 * F(3, 46) = 2.24, p= 0.096 F(3, 46) = 1.44, p= 0.242 F(1, 68) = 13.88, p< 0.001 *
Side > Anterior
ROM at S1 ()F(1, 84) = 20.12, p< 0.001 * F(3, 41) = 0.64, p= 0.595 F(3, 41) = 3.12, p= 0.036 * F(1, 55) = 47.64, p< 0.001 *
Side > Anterior H, M, L: Side > Anterior
Mean relative phase angle ()F(1, 122) = 0.07, p= 0.794 F(3, 60) = 5.23, p= 0.003 * F(3, 59) = 0.40, p= 0.752 F(1, 86) = 2.17, p= 0.145
NL > (L, M, H)
Sagittal plane
ROM at T6 ()F(1, 130) = 47.34, p< 0.001 * F(3, 64) = 1.01, p= 0.393 F(3, 62) = 6.67, p= 0.001 * F(1, 100) = 3.34, p= 0.071
Side > Anterior Side: (H, M) > NL
H, M, L: Side > Anterior
ROM at S1 ()F(1, 120) = 0.287, p= 0.593 F(3, 58) = 0.12, p= 0.947 F(3, 57) = 0.36, p= 0.779 F(1, 112) = 0.79, p= 0.378
Mean relative phase angle ()F(1, 103) = 3.59, p= 0.061 F(3, 49) = 0.81, p= 0.493 F(3, 48) = 0.76, p= 0.523 F(1, 1) = 0.69, p= 1.0
Sensors 2020,20, 5206 14 of 28
Table 5.
Experiment-2 (n= 10) estimated marginal mean
±
SE for the No-load condition, and statistically significant mean differences
±
SE relative to the no-load
condition by carrying mode and load level, at average values of centered relative speed. Significant mean differences relative to the no-load condition by carrying
mode across different load levels are within the square brackets (i.e., [- - mean ±SE - -]).
Side Carry Anterior Carry
No-Load Low (4.5 kg) Medium (9.1 kg) High (13.6 kg) Low (4.5 kg) Medium (9.1 kg) High (13.6 kg)
Coronal plane
ROM at T6 () 4.3 ±0.2 0.4 ±0.3 1.1 ±0.3 1.0 ±0.3 0.4 ±0.3 1.1 ±0.3 1.0 ±0.3
ROM at S1 () 8.3 ±6.6 [------------ +0.3±0.4 --------------] [------------ 0.6 ±0.4 --------------]
Mean relative phase angle () 118.7 ±6.2 +1.8 ±7.1 +7.4 ±7.1 +5.9 ±7.1 11.4 ±7.7 21.3 ±8.0 24.7 ±7.8
Transverse plane
ROM at T6 () 5.2 ±0.3 [------------ +1.0±0.3 --------------] [------------ +0.2±0.3 --------------]
ROM at S1 () 7.1 ±0.6 +1.3 ±1.0 +2.5 ±1.1 +2.3 ±1.2 0.7 ±0.7 0.9 ±0.6 0.9 ±0.6
Mean relative phase angle () 66.1 ±4.4 18.1 ±5.8 20.7 ±5.4 17.8 ±6.4 18.1 ±5.8 20.7 ±5.4 17.8 ±6.4
Sagittal plane
ROM at T6 () 2.7 ±0.1 +0.3 ±0.2 +0.5 ±0.2 +0.5 ±0.2 0.4 ±0.2 0.5 ±0.2 0.2 ±0.1
ROM at S1 () 3.5 ±0.3 - - - - - -
Mean relative phase angle () 52.9 ±3.3 - - - - - -
Sensors 2020,20, 5206 15 of 28
4. General Discussion
The purpose of this study was to quantify the effects of hand loads during two-handed side and
anterior load carriage on thoracic-pelvic sway and coordination obtained from body-worn gyroscopes
while walking at a self-selected speed. By including relative speed as a covariate, the study estimated
the marginal effects of carrying mode and load level after adjusting for individual anthropometry
(leg length) and preferred walking speed, which are two known variables that affect gait kinematics.
Results from two separate experiments demonstrate that two-handed side and anterior load carriage
relative to unloaded walking are associated with systematic influences on thoracic-pelvic sway
and coordination. The two experiments differed in terms of their anthropometric diversity, namely,
relatively homogenous (Expt-1) and age-diverse (Expt-2) male cohorts, and the choice of commercial
wearable sensor, yet produced convergent findings suggesting consistency in findings.
Prior to discussing the study’s key findings, it is worth noting that studies have previously
investigated biomechanical adaptations in gait due to the magnitude and position of hand
loads
[2,26,27,29,30,33]
. However, these studies primarily involved either walking on treadmills at
controlled speeds or on ground without accounting for walking speed. Moreover, these studies
examined diverse carrying modes and magnitudes of load including a fixed amount, or loads
normalized to percentage of body weight. None of the reviewed studies characterized the specific
measures of thoracic-pelvic coordination (i.e., relative phase angles) in two-handed anterior and/or
side carry. Studies that investigated thoracic-pelvic or lumbar-sacral coordination used other modes
of carrying (e.g., backpacks [
25
], overhead firearm carry [
35
]), while studies on anterior and/or side
carry examined other types of kinematic and biomechanical measures [
3
,
20
,
38
,
50
]. As a result, direct
comparisons with the present study are not possible. However, the direction of change in gait and
postural kinematics observed in the present study are supported by findings from prior studies and
are briefly discussed here.
In general, the peak-to-peak magnitude changes in thoracic and pelvic sway were small and
on their own were of potentially less practical value. More notable was the combining of thoracic
and pelvic sway over the entire gait cycle into a normalized measure of thoracic-pelvic coordination,
i.e., relative phase angle, which yielded distinct and meaningful trends primarily in the coronal and
transverse planes. In both experiments, side load carriage was characterized by an increase in pelvic
sway in the transverse plane and an increase in thoracic sway in the sagittal plane compared to anterior
load carriage. Regarding thoracic-pelvic coordination in the coronal plane, both experiments indicated
a significant interaction effect of carrying mode and load. Thoracic-pelvic coordination in the coronal
plane was more asynchronous or out-of-phase in the side vs. anterior carry, and these differences were
more pronounced at higher load levels, i.e., coronal movement was more asynchronous in side carry and
more coordinated in anterior carry at the high vs. low load levels. In general, thoracic-pelvic movement
in the transverse plane was significantly more coordinated in the loaded vs. unloaded walk conditions,
which is a trend also reported in other modes of load carriage [
25
,
35
]. Comparing between modes,
thoracic-pelvic movement was more coordinated in the side vs. anterior carry, though statistically
significant only in Experiment-1. Thoracic-pelvic movement in the sagittal plane was slightly more
coordinated in the anterior carry vs. side carry and significant only in Experiment-1, but these
differences were smaller compared to the effects on thoracic-pelvic coordination in the coronal and
transverse planes. It is possible that the relatively small sample size relative to the sample diversity
may have contributed to reduced statistical power to detect certain differences in Experiment-2.
These trends in postural sway and thoracic-pelvic coordination across carrying modes and load
magnitudes can be explained as follows by changes in body dynamics [
25
,
29
], specifically, angular
momentum and location of the combined center of mass (COM) in response to the body’s need for
maintaining dynamic balance while walking/carrying.
Sensors 2020,20, 5206 16 of 28
4.1. Analysis of Two-Handed Side Carry
During the swing phase of walking, the upper torso including the arms counter-rotate relative
to the pelvis in the transverse plane to reduce net angular momentum [
62
]. In a side carry with
the external load equally divided between the right and left hands, there is a net increase in the
moment of inertia and the angular momentum in the transverse plane from loads located laterally [
50
].
Consequently, the angular momentum of the rest of the body needs to increase in the opposite direction
to reduce the net angular momentum. This increase is accomplished by synchronizing the transverse
rotation of the torso and pelvic segments and evinced as increased pelvic sway and more in-phase
coordination between thoracic-pelvic segments in the transverse plane relative to unloaded gait.
Hand loads in a side carry lower the location of the COM of the upper body in the coronal and
the sagittal planes. Consequently, the moment of inertia and angular momentum of the torso in the
coronal plane increases. With the increasing hand loads, angular momentum of the upper body in
the coronal plane is controlled by limiting thoracic coronal sway. Pelvic sway in the coronal plane
was found to increase with increasing load levels to compensate for this reduced rotation of the torso.
Furthermore, the torso and pelvis tend to counter-rotate in the coronal plane in order to decrease the
net angular momentum between the torso and pelvis, which was evident from the more out-of-phase
coordination between thoracic-pelvic segments with increasing load levels.
4.2. Analysis of Two-Handed Anterior Carry
Rotation of the arms is restricted in a two-handed anterior carry since the torso, arms, and hand
load are coupled and move together [
3
,
35
,
63
]. In order to reduce the net angular momentum in the
transverse plane while walking, the upper body including the arms and the trunk counter-rotate
relative to the lower body. The need for increased postural stability due the anterior load is achieved
by co-contracting the trunk muscles and limiting rotational movement in the upper body [
19
] and
by a concomitant decrease in pelvic sway in the transverse plane with increasing anterior loads.
This decreased transverse pelvic rotation during load carriage is compensated by a simultaneous
increase in hip excursion [
25
] and reflected in the increased pelvic sway recorded in the sagittal plane.
During anterior load carriage, coordination between the thoracic-pelvic segments was more
in-phase or synchronized in the loaded conditions vs. no-load in the coronal and transverse planes
indicating the close coupling between the torso and pelvis. Since the load is located medially in the
transverse and coronal planes during anterior load carriage in contrast to the laterally positioned
loads in a side carry, the moment of inertia and angular momentum in the coronal plane at the torso
is less in the anterior carry [
50
]. Thus, the need for counter-rotating the upper vs. lower body to
reduce the net angular momentum between the segments is relatively lower compared to the side carry.
This difference in angular momentum and trunk movements between modes produced diverging
trends in thoracic-pelvic coordination in the anterior carry (i.e., more in-phase) compared to the side
carry (i.e., more out-of-phase) with increasing load magnitude.
4.3. Study Contributions and Limitations
Collectively, the findings from this study help typify naturalistic postural adaptations to load
carriage under conditions simulating manual material handling that commonly occur in the workplace.
The application of wearable inertial sensors, including gyroscopes, accelerometers, and IMUs enables
the study of such ambulatory work tasks. The present study specifically focused on a minimal set of
body-worn gyroscope sensors (i.e., two for thoracic-pelvic coordination, and two for spatiotemporal
gait paramaters) as a prelude to subsequent studies in applied work settings. Prior studies on posture
and gait during load carriage have typically used optical motion tracking in controlled laboratory
environments, often while walking on a treadmill at a precise speed. The present study had walk
trials performed in more naturalistic conditions with participants walking at a self-selected speed in a
sufficiently long and levelled corridor. This was done in order to record natural adaptations in gait
Sensors 2020,20, 5206 17 of 28
patterns associated with the carrying mode and load level without imposing any external constraint
on gait speed. However, this implied having to adjust for potential confounding variables related
to individual anthropometry and gait. Earlier studies support the notion that walking speeds and
stride length are slightly modified depending on the loads carried [
25
,
30
,
64
]. The present study used a
normalized measured of walking speed, namely relative speed, obtained from gyroscope-based
estimates of gait cycle duration and stride length as a covariate to account for between and
within-participant differences in walking speed and stature. The covariate was statistically significant
in nearly all of the dependent measures tested in coronal and transverse planes, justifying its inclusion.
Despite this statistical treatment, it is possible that some of the postural parameters were influenced by
differences in anthropometry and/or strength (e.g., hip abduction strength) factors not measured in
this study besides load intensity and carrying mode.
Only two carrying modes were considered in this study. A shift in the center of mass posteriorly
such as while wearing a backpack, e.g., [
14
,
22
,
25
,
28
,
33
], or laterally with a load in one hand or on one
shoulder, e.g., [
17
,
19
,
20
,
22
,
33
], may result in gait adaptations different from those produced in this
study. However, our findings on thoracic-pelvic sway and coordination during the anterior carry were
similar to findings on the effects of posterior load carriage reported by [
25
] wherein a loaded backpack
carry decreased transverse thoracic and pelvic rotation, and the mean relative phase angle between
thoracic and pelvic segments, suggesting similar effects on gait kinematics. The load magnitudes
used in the present study were lower than some previous studies (e.g., 40% of the body weight [
25
])
and focused on multiple relatively short bouts of walking (<30 s per trial) to reflect load magnitudes
and carrying distances found in the workplace (e.g., between workstations, [
7
,
8
]). With heavier loads,
longer walking durations, and onset of whole-body fatigue, the metabolic cost of load carriage is
expected to increase causing even further alterations in muscle activation patterns, posture, and gait
kinematics [
65
,
66
]. The present study enforced two-minute rest breaks between walk trials to minimize
the cumulative effects of fatigue. Based on walking speeds obtained in Experiment-1, the path length
in Experiment-2 was reduced from 26.2 m to 12 m to further minimize possible effects of cumulative
fatigue, while ensuring that five cycles of steady-state gait per trial (i.e., 2nd to 6th gait cycle) could be
obtained in a consistent manner.
As an initial investigation, the present study was limited to an age-diverse cohort of male
participants with multiple gait cycles analyzed per condition and participant, resulting in 1517 gait
cycles analyzed. Subsequent studies would need to consider diversity in functional strength and gender
among other known sources of variability in gait. In both experiments (Expt-1 with a homogeneous
sample & Expt-2 with a diverse sample), pelvic-thoracic coordination in coronal and transverse planes
indicated significant and plausible differences in carrying mode and magnitude of load suggesting
consistent kinematic adaptations. However, the relatively small sample size causes both experiments
to have low statistical power. Hence, certain statistical differences may possibly go undetected,
for example, the absence of a significant effect of carrying mode on sagittal thoracic-pelvic coordination
in Experiment-2 (compared to Experiment-1). Nevertheless, the relatively healthy, all-male cohort
in these initial studies help establish baseline expectations for thoracic-pelvic coordination patterns
during load carriage, and facilitates reference comparisons in future studies investigating effects
and/or onset of physical fatigue, or presence of work-related disorders such as low-back pain e.g., [
67
],
on dynamic gait posture during load carriage.
The findings from the present study suggest that information about both carrying mode and
load magnitude can be obtained from a combination of thoracic-pelvic sway and coordination.
These findings have potential for informing approaches to assessing exposures from load carriage [
68
]
in work settings where the intensity, duration, frequency, and mode of load carriage varies over
time, such as in construction work, warehousing, packaging, and distribution. Field-based studies
of biomechanical exposure assessment typically require instrumentation for force measurement such
as surface electromyography for muscle activation levels and force platform for measuring ground
reaction forces at the foot that is conspicuous, cumbersome, and potentially interferes with workers
Sensors 2020,20, 5206 18 of 28
movements and task performance. Instead, wearable sensing technologies such as gyroscopes and
IMUs could provide a low-cost and less obtrusive alternative for use in field-based studies. However,
certain algorithmic limitations such as related to sensor drift have yet to be overcome. The present
study circumvents issues of drift by integrating angular velocity data only within each gait cycle for
computing stride length, thoracic-pelvic sway, and coordination (i.e., typically less than 1 s duration).
The present study was limited to short duration walk trials, and used a previously validated
algorithm that used gyroscope data for gait detection. While algorithmic approaches to detecting gait
events from body-worn gyroscopes and IMUs have received considerable attention in the biomechanics
and clinical literature, e.g., [
42
,
69
72
], further studies are needed to identify and validate those
sensor configurations and algorithms in real-world environment (e.g., [
73
]) and occupational settings
(e.g., uneven or different terrain types in construction sites, e.g., [
74
]). In addition, the growing interest
of applying machine learning algorithms to extract contextual information beyond postural angles
such as the type of work tasks and/or load intensities in the context of MMH using body-worn inertial
sensor data can broaden the use of wearable sensors for field-based assessment of biomechanical
exposures and associated injury risk [
68
,
75
,
76
]. An important step when constructing such machine
learning algorithms is the selection of relevant and plausible features that can accurately and
reliably discriminate among task conditions. Findings from the present study suggest thoracic-pelvic
coordination as candidate features for such machine learning algorithms.
5. Conclusions
Thoracic-pelvic coordination when walking is challenged by the addition of external hand loads.
This study identified distinct patterns in thoracic-pelvic coordination from different hand loads in
two-handed side vs. anterior load carriage at self-selected walking speeds using body-worn gyroscopes,
after adjusting for relative speed. These differences reflect postural adjustments to maintain dynamic
postural stability while walking under the external load conditions. Rotational movement coordination
between the torso and pelvis measured as relative phase angles in the coronal and transverse planes
were particularly insightful to quantify the effects of hand-load magnitude and carrying mode on
walking. Study findings suggest that relative phase angles measured in the coronal and transverse
planes may provide sufficiently sensitive information to characterize changes in gait posture induced
by carrying mode and the magnitude of hand-load. These findings serve as a foundation for future
studies using wearable sensing in applied work settings to measure posture adaptations and effects of
exposure to biomechanical stress from manual load carriage.
Author Contributions:
Conceptualization, S.L. and C.D.; methodology, S.L. and C.D.; analysis, S.L. and C.D.;
writing—original draft preparation, S.L.; writing—review and editing, S.L. and C.D.; funding acquisition, C.D.
All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by National Institute on Disability, Independent Living, and Rehabilitation
Research (NIDILRR), Grant No. 90IF0094. NIDILRR is a Center within the Administration for Community Living
(ACL), Department of Health and Human Services (HHS). The contents of this publication do not necessarily
reflect the official policies of NIDILRR, ACL, or HHS, nor imply endorsement by the U.S. Government.
Conflicts of Interest:
The authors declare no conflict of interest. The funder had no role in the design of the study;
in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish
the results.
Ethical Statement:
The study was conducted in accordance with the Declaration of Helsinki, and the protocol
was approved by the Ethics Committee of the University of Michigan, Ann Arbor (Protocol #HUM00098607).
Written consent from each participant was obtained before they participated in the study.
Sensors 2020,20, 5206 19 of 28
Abbreviations
The following abbreviations are used in this manuscript:
IMU Inertial Measurement Units
MMH Manual Material Handling
MSD Musculoskeletal Disorders
COM Center of Mass
Appendix A. Computing Temporal Gait Parameters Using Angular Velocity from a
Shank-Mounted Gyroscope
Gait cycle duration was calculated as the time duration between right heel-strike to the consecutive
right heel-strike (Figure A1). Detection of heel-strike and toe-off gait events were performed as the
first step in a custom algorithm (modified from Aminian et al.
[42]
). One gait cycle was composed of
the sequence of the following events: (1) right heel-strike, (2) left toe-off, (3) left heel-strike, (4) right
toe-off, and (5) next right heel-strike. In this study, events (1), (4), and (5) were detected using sagittal
angular velocity (rad/s) from inertial sensors on the right shank. The procedure is outlined below:
Figure A1.
Graphical overview of the algorithm for detecting temporal gait events. Sample profile of
the sagittal angular velocity (rad/s) from the right shank sensor is depicted for one complete gait cycle
(100%) recorded during empty-handed condition. A = Right heel-strikes, B and C = minimum peaks
within the right leg stance, D = Right toe-off, E = Mid-swing.
Step 1: Detect mid-swing
Mid-swings were detected by finding the global minimum peaks (Points E in Figure A1) in the
sagittal angular velocity (rad/s) data. To detect the valid global minima representing the mid-swing,
Aminian et al. [
42
] applied a multi-resolution wavelet decomposition. In this paper, we applied
two search criteria in terms of the amplitude of the peak and time duration between peaks. First,
an amplitude threshold of
1.75 rad/s determined from preliminary testing was applied to identify
the global minimum peaks of interest (i.e., Points E in Figure A1) while avoiding Points B after the
heel-strike and the minimum peaks during the stance phase (i.e., Points C in Figure A1). Second, a time
threshold of 0.63 s was used for the minimum time difference between consecutive global minima to
ensure rejection of points B and C in Figure A1.
Sensors 2020,20, 5206 20 of 28
Step 2: Estimating average gait cycle duration
Time duration between consecutive mid-swings were calculated to estimate the average gait cycle
duration (T0) using Equation (A1).
T0=N
0(T(E)i+1T(E)i)
N(A1)
where, T(E)
i
is an array containing the timeframes for the ith instance of a mid-swing Point E, Nis the
number of gait cycles detected, and i= 0 to N.
Step 3: Detect heel-strike
Heel-strikes were detected by finding the local maxima (Points A in Figure A1) within the range
of [T(E)
i
+ 0.25 s, T(E)
i
+
T0
]. Multiple peaks may be detected within this time range so the search was
refined in Step 5.
Step 4: Detect Toe-off
Toe-offs were detected by finding the local maxima (Points D in Figure A1) within the range of
[T(E)iT0,T(E)i0.05 s].
Step 5: Refine heel-strike
Heel-strikes were refined amongst the detected heel-strikes from Step 3 by setting the search
range of [T(E)
i
,T(D)
i+1
], where T(D)
i+1
is the timeframe for i+1 instance of Point D. The first local peak
after the mid-swing was selected as the heel-strike.
Step 6: Pair heel-strike and toe-off events
Heel-strikes and toe-offs detected were paired if the time duration between heel-strike (T(E)i) to
toe-off (T(D)
i
) was within [0.1 s, 2.5 s]. This pairing starts from the first heel-strike and toe-off in the
time series and continued until the last heel-strike. Any heel-strike that does not meet this criterion
was discarded.
Step 7: Compute temporal duration between detected gait events
Time difference between consecutive heel-strikes (Figure A1, time duration combining each stance
and swing in the bottom panel) were used to calculate gait cycle duration. Start and end of each stance
and swing phase were used in estimating stride length in Appendix B.
Appendix B. Estimating Stride Length from Angular Velocity of the Right Shank and Thigh
Recorded by Gyroscopes
Stride lengths were estimated by algorithm adapted from Aminian et al.
[42]
. Sagittal angular
velocity (rad/s) data from the right thigh (
ωThigh
) and right shank (
ωShank
) sensors and right
thigh length (
l1
, trochanter height
knee height) and right shank length (
l2
, knee height) were
used. The calculation was performed in a custom algorithm implemented in MATLAB R2016b
(The MathWorks Inc., Natick, MA, USA). Leg movement during stance and swing phases were
modeled as an inverted double pendulum and double pendulum [
42
], respectively, as depicted in
Figure A2. During the right stance phase, the right shank and thigh rotate forward by pivoting the
right foot on the ground. Although the foot has not moved forward, the body has moved forward by
distance
d1
. During the right swing phase the right foot steps forward by distance
d2
. Total stride
length can be estimated as the sum of
d1
and
d2
assuming bilateral symmetry, i.e., that right and left
step lengths are equal. Stride lengths were calculated for each gait cycle kfollowing the steps:
Sensors 2020,20, 5206 21 of 28
R
R. Heel-strike R. Toe-off R. Heel-strike
Stance (R)
Inverted double pendulum model
Swing (R)
Do ubl e p endul um mo del
𝒅𝟏
𝑙$
𝑙$
𝑙%
𝑑′
𝑑′
𝑀$
𝑀%
𝛿𝛾
𝛽
𝑙%𝑙%
𝑙$
𝛼
𝒅𝟐
𝑀$
𝑀%
𝛿𝛾
𝛽
𝛼
𝑙%
𝑙$
𝒅𝟏
Figure A2.
Graphical summary of the double pendulum model (adapted from Aminian et al. [
42
])
used for estimating the stride lengths from sagittal angular velocity (rad/s) data of the right shank and
right thigh during right stance and swing phases separately.
Step 1: Calculate the distance d1moved during the stance phase
The distance moved during the right stance phase (
d1
) was estimated from the trigonometric
relations. The angular rotation of the right thigh (
α
) and right shank (
β
) were estimated by integrating
ωThigh
and
ωShank
, within each gait cycle, respectively. For each gait cycle k, we calculated
d1(k)
as follows:
d1(k) = q(l1+M2(k))2+ (l1+M1(k))2(l1+M2(k))(l1+M1(k)) cos α(k), (A2)
where:
α(k) = ZR.Toeo f f (k)
R.Heelstrike(k)ωThigh (t)dt, (A3)
β(k) = ZR.Toeo f f (k)
R.Heelstrike(k)ωShank(t)dt, (A4)
M1(k) = sinδ(k)
sinα(k)d0(k), (A5)
M2(k) = sinγ(k)
sinα(k)d0(k), (A6)
Sensors 2020,20, 5206 22 of 28
with:
γ(k) = πα(k)
2
δ(k) = π+α(k)2β(k)
2
d0(k) = l2(k)q2(1cosβ(k))
(A7)
Step 2: Calculate the distance (d2) moved during the swing phase
The distance moved during the right swing phase (d2) was estimated as follows:
d2(k) = q(l2+M1(k))2+ (l2+M2(k))2(l2+M1(k))(l2+M2(k)) cos β(k), (A8)
where:
α(k) = ZR.Heelstrike(k)
R.Toeo f f (k)ωThigh (t)dt, (A9)
β(k) = ZR.Heelstrike(k)
R.Toeo f f (k)ωShank(t)dt, (A10)
M1(k) = sinδ(k)
sinβ(k)d0(k), (A11)
M2(k) = sinγ(k)
sinβ(k)d0(k), (A12)
with:
γ(k) = πβ(k)
2
δ(k) = π2α(k) + β(k)
2
d0(k) = l2(k)q2(1cosα(k))
(A13)
Step 3: Combining d1and d2
Stride length is the total distance moved during one gait cycle k:
Stride length(k) = d1(k) + d2(k)(A14)
Appendix C. Algorithm for Calculating the Mean Thoracic-Pelvic Relative Phase Angle
Mean relative phase angle [
34
] was calculated using the angular velocity (rad/s) data obtained
from the torso (
ωT6
) and pelvis (
ωS1
) sensors. Additionally, angular displacement (rad,
θ
) was
calculated by integrating the angular velocity data across time within each gait cycle. The algorithm is
outlined below along with an example obtained during no-load condition depicted in Figure A3.
Step 1: Calculate normalized angular velocity and angular displacement
Normalized angular velocity and angular displacement were obtained by rescaling the amplitudes
of the data to the range [1, 1] within each normalized (%) gait cycle separately for each sensor.
Sensors 2020,20, 5206 23 of 28
Step 2: Calculating phase angle
Phase angles of the torso and pelvis segments (
φ
) were calculated separately by following
Equation (A15):
φ(t) = arctan(ω(t)/θ(t)) (A15)
where
ω
(t) represents the normalized angular velocity time series,
θ
(t) the normalized angular
displacement, at t= 0, ..., 100%.
Step 3: Calculate relative phase angle
Relative phase angle between torso and pelvis was obtained following Equation (A16):
φ(t) = φT6(t)φS1 (t)(A16)
Step 4: Calculate mean relative phase angle
Mean relative phase angle, which was the dependent measure used in the present study,
was computed as the average of the relative phase angle over one gait cycle:
φ=N
t=0 φ(t)
N(A17)
Sensors 2020,20, 5206 24 of 28
Figure A3.
Overview of the algorithm for computing mean thoracic-pelvic relative phase angle using one exemplar participant data from the no-load condition in the
coronal plane.
Sensors 2020,20, 5206 25 of 28
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... EMG can be used to monitor and record myoelectric signals generated by skeletal muscle activity [79]. Gyroscopes can monitor angular velocity changes that occur during motion for motion posture analysis [80]. A force sensor, magnetometer, and pressure sensor are less frequently cited in the literature and were only mentioned in three papers. ...
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Balance impairment is common in individuals with multiple sclerosis (MS). However, objective assessment of balance usually requires clinical expertise and/or the use of expensive and obtrusive measuring equipment. These barriers to the objective assessment of balance may be overcome with the development of a lightweight inertial sensor system. In this study, we examined the concurrent validity of a novel wireless, skin-mounted inertial sensor system (BioStamp®, MC10 Inc.) to measure postural sway in individuals with MS by comparing measurement agreement between this novel sensor and gold standard measurement tools (force plate and externally validated inertial sensor). A total of 39 individuals with MS and 15 healthy controls participated in the study. Participants with MS were divided into groups based on the amount of impairment (MSMild: EDSS 2–4, n = 19; MSSevere: EDSS ≥6, n = 20). The balance assessment consisted of two 30-s quiet standing trials in each of three conditions: eyes open/firm surface, eyes closed/firm surface, and eyes open/foam surface. For each trial, postural sway was recorded with a force plate (Bertec) and simultaneously using two accelerometers (BioStamp and Xsens) mounted on the participant’s posterior trunk at L5. Sway metrics (sway area, sway path length, root mean square amplitude, mean velocity, JERK, and total power) were derived to compare the measurement agreement among the measurement devices. Excellent agreement (intraclass correlation coefficients >0.9) between sway metrics derived from the BioStamp and the MTx sensors were observed across all conditions and groups. Good to excellent correlations (r >0.7) between devices were observed in all sway metrics and conditions. Additionally, the acceleration sway metrics were nearly as effective as the force plate sway metrics in differentiating individuals with poor balance from healthy controls. Overall, the BioStamp sensor is a valid and objective measurement tool for postural sway assessment. This novel, lightweight and portable sensor may offer unique advantages in tracking patient’s postural performance.
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Background Mobility impairment is common in people with multiple sclerosis (PwMS) and there is a need to assess mobility in remote settings. Here, we apply a novel wireless, skin-mounted, and conformal inertial sensor (BioStampRC, MC10 Inc.) to examine gait characteristics of PwMS under controlled conditions. We determine the accuracy and precision of BioStampRC in measuring gait kinematics by comparing to contemporary research-grade measurement devices. Methods A total of 45 PwMS, who presented with diverse walking impairment (Mild MS = 15, Moderate MS = 15, Severe MS = 15), and 15 healthy control subjects participated in the study. Participants completed a series of clinical walking tests. During the tests participants were instrumented with BioStampRC and MTx (Xsens, Inc.) sensors on their shanks, as well as an activity monitor GT3X (Actigraph, Inc.) on their non-dominant hip. Shank angular velocity was simultaneously measured with the inertial sensors. Step number and temporal gait parameters were calculated from the data recorded by each sensor. Visual inspection and the MTx served as the reference standards for computing the step number and temporal parameters, respectively. Accuracy (error) and precision (variance of error) was assessed based on absolute and relative metrics. Temporal parameters were compared across groups using ANOVA. Results Mean accuracy±precision for the BioStampRC was 2±2 steps error for step number, 6±9ms error for stride time and 6±7ms error for step time (0.6–2.6% relative error). Swing time had the least accuracy±precision (25±19ms error, 5±4% relative error) among the parameters. GT3X had the least accuracy±precision (8±14% relative error) in step number estimate among the devices. Both MTx and BioStampRC detected significantly distinct gait characteristics between PwMS with different disability levels (p<0.01). Conclusion BioStampRC sensors accurately and precisely measure gait parameters in PwMS across diverse walking impairment levels and detected differences in gait characteristics by disability level in PwMS. This technology has the potential to provide granular monitoring of gait both inside and outside the clinic.
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The performance of manual material handling tasks is one major cause of lower back injuries. In the current study, we investigated the influence of the weight configuration of hand loads on trunk muscle activities and the associated spinal stability. Thirteen volunteers each performed static weight holding tasks using two different 9 kg weight bars (with medial and lateral weight configurations) at two levels of heights (low and high) and one fixed horizontal distance (which resulted in constant spinal joint moment across conditions). Results of the current study demonstrated that holding the laterally-distributed load significantly reduced activation levels of lumbar and abdominal muscles by 9 to 13% as compared with holding the medially-distributed load. We believe such an effect is due to an elevated rotational moment of inertia when the weight of the load is laterally distributed. These findings suggest that during the design and assessment of manual material handling tasks, such as lifting and carrying, the weight configuration of the hand load should be considered. Practitioner summary Elevated trunk muscle activities were found when holding a medially-distributed load vs. a laterally-distributed load (with an equivalent external moment to the spine), indicating a reduced spinal stability due to the reduced rotational moment of inertia. The configuration of the hand load should be considered when evaluating manual material handling tasks.
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The purpose of this study was to determine the effect of different methods of backpack carrying on gait kinetics in children, using a new treadmill that allowed three-dimensional measurement of right and left leg ground reaction forces (GRFs). Forty-one healthy children, with a mean age of 12 years, participated in this study. The mean height was 152 cm and the mean weight 40 kg. The three trials consisted of walking on the treadmill at the speed of 3.5 km/h, first without a backpack and then carrying a 10 kg school bag on the right shoulder or on both shoulders. For each carrying condition GRFs were recorded, averaged, and analyzed for 30 steps. Stride, stance, double stance, thirteen specific GRF parameters and the symmetry index were measured. The right leg produced higher propulsive fore-aft forces than the left one, whatever the walking conditions. For the two maximum peaks and the average vertical force during stance, a statistical difference was found between walking without a backpack and carrying a backpack on one or two shoulders (one or two shoulder carrying > no backpack) but never between one-shoulder and two-shoulder carrying. The children increased their stance and double stance when walking with a backpack compared with walking without a pack. The symmetry index increased with one-strap carrying (compared with no backpack and two-strap carrying) for the maximum force during the breaking phase (Fy1) when it decreased for the maximum propulsive horizontal force before taking-off (Fy2). Children should be advised to carry their backpack on two shoulders rather than use a one-strap backpack.
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Numerous gait event detection (GED) algorithms have been developed using accelerometers as they allow the possibility of long-term gait analysis in everyday life. However, almost all such existing algorithms have been developed and assessed using data collected in controlled indoor experiments with pre-defined paths and walking speeds. On the contrary, human gait is quite dynamic in the real-world, often involving varying gait speeds, changing surfaces and varying surface inclinations. Though portable wearable systems can be used to conduct experiments directly in the real-world, there is a lack of publicly available gait datasets or studies evaluating the performance of existing GED algorithms in various real-world settings. This paper presents a new gait database called MAREA (n=20 healthy subjects) that consists of walking and running in indoor and outdoor environments with accelerometers positioned on waist, wrist and both ankles. The study also evaluates the performance of six state-of-the-art accelerometer-based GED algorithms in different real-world scenarios, using the MAREA gait database. The results reveal that the performance of these algorithms is inconsistent and varies with changing environments and gait speeds. All algorithms demonstrated good performance for the scenario of steady walking in a controlled indoor environment with a combined median F1score of 0.98 for Heel-Strikes and 0.94 for Toe-Offs. However, they exhibited significantly decreased performance when evaluated in other lesser controlled scenarios such as walking and running in an outdoor street, with a combined median F1score of 0.82 for Heel-Strikes and 0.53 for Toe-Offs. Moreover, all GED algorithms displayed better performance for detecting Heel-Strikes as compared to Toe-Offs, when evaluated in different scenarios.
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Kinetic and kinematic responses during walking vary by footwear condition. Load carriage also influences gait patterns, but it is unclear how an external load influences barefoot walking. Twelve healthy adults (5 women, 7 men) with no known gait abnormalities participated in this study (age = 23 ± 3 years, height = 1.73 ± 0.11 m, and mass = 70.90 ± 12.67 kg). Ground reaction forces and 3D motion were simultaneously collected during overground walking at 1.5 m s⁻¹ in four conditions: Barefoot Unloaded, Shod Unloaded, Barefoot Loaded, and Shod Loaded. Barefoot walking reduced knee and hip joint ranges of motion, as well as stride length, stance time, swing time, and double support time. Load carriage increased stance and double support times. The 15% body weight load increased GRFs ∼15%. Walking barefoot reduced peak anteroposterior GRFs but not peak vertical GRFs. Load carriage increased hip, knee, and ankle joint moments and powers, while walking barefoot increased knee and hip moments and powers. Thus, spatiotemporal and kinematic adjustments to walking barefoot decrease GRFs but increase knee and hip kinetic measures during overground walking. The ankle seems to be less affected by these footwear conditions. Regardless of footwear, loading requires larger GRFs, joint loads, and joint powers.