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STATISTICAL PREDICTION OF LOAD CARRIAGE
1
Statistical prediction of load carriage mode and magnitude from inertial sensor derived gait
kinematics
Sol Lim a, 1
Clive D’Souza, Ph.D. a
a Center for Ergonomics, University of Michigan, Ann Arbor, Michigan
1 Corresponding author: Sol Lim, Center for Ergonomics, Department of Industrial and
Operations Engineering, University of Michigan, 1205 Beal Avenue, Ann Arbor, MI 48109-2117
USA; phone: 1-734-764-9965; email: solielim@umich.edu
STATISTICAL PREDICTION OF LOAD CARRIAGE
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Abstract
Load carriage induces systematic alterations in gait patterns and pelvic-thoracic coordination.
Leveraging this information, the objective of this study was to develop and assess a statistical
prediction algorithm that uses body-worn inertial sensor data for classifying load carrying
modes and load levels. Nine men participated in an experiment carrying a hand load in four
modes: one-handed right and left carry, and two-handed side and anterior carry, each at 50%
and 75% of the participant’s maximum acceptable weight of carry, and a no-load reference
condition. Twelve gait parameters calculated from inertial sensor data for each gait cycle,
including gait phase durations, torso and pelvis postural sway, and thoracic-pelvic coordination
were used as predictors in a two-stage hierarchical random forest classification model with
Bayesian inference. The model correctly classified 96.9% of the carrying modes and 93.1% of
the load levels. Coronal thoracic-pelvic coordination and pelvis postural sway were the most
relevant predictors although their relative importance differed between carrying mode and
load level prediction models. This study presents an algorithmic framework for combining
inertial sensing with statistical prediction with potential use for quantifying physical exposures
from load carriage.
Keywords: load carriage; inertial sensors; load classification; gait kinematics;
STATISTICAL PREDICTION OF LOAD CARRIAGE
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1.0 Introduction
Prolonged exposure to manual load carriage is a known risk factor for low back
disorders (Knapik, Harman, & Reynolds, 1996; Putz-Anderson et al., 1997). Epidemiological
findings suggest an increased odds of developing a prolapsed lumbar disc from frequently
carrying objects more than 11.3 kg (25 lbs.) (Kelsey et al., 1984). Heavy and frequent load
carriage may accelerate spinal degeneration due to an increased loading on the spine and
would damage spinal tissues in the vertebral column (Jensen, 1988). While minimizing the
frequency and intensity of manual load carriage is ideal, such tasks are still common and
inevitable in non-routinized work such as in construction (Anderson et al., 2007), firefighting
(Park, Hur, Rosengren, Horn, & Hsiao-Wecksler, 2010), and manufacturing (Cheng & Lee, 2006).
Accurate measurement of exposures to biomechanical risk factors is an important step to
develop effective musculoskeletal injury prevention and risk reduction programs (David, 2005).
Measuring the duration, frequency, and magnitude of hand loads longitudinally is an essential
step for assessing the biomechanical impacts to the musculoskeletal system and identifying
strategies for intervention.
Measuring longitudinal exposures to load carriage in field settings presents unique
challenges. Traditional exposure assessment techniques that rely on direct observations have
limitations in non-repetitive job conditions where the work tasks vary considerably in duration,
frequency, or intensity levels (Gold, Park, & Punnett, 2006). Direct measurement of task
durations and load magnitudes in applied settings would require instrumentation system that is
wireless and portable and unrestricted by changes in a worker’s location. In addition to load
magnitude, the biomechanical effects of load carriage are influenced by the mode of load
STATISTICAL PREDICTION OF LOAD CARRIAGE
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carriage (e.g., two-handed anterior, one-handed side). A study by Rose, Mendel, and Marras
(2013) demonstrated that carrying the same load with different carrying modes generates a
significant difference in the anterior-posterior shear loading at L2/L3. Carrying a two-handed
anterior load of 11.3 kg was sufficient to produce an average shear load of 856 N, which
exceeded the recommended exposure limits of 700 N (Gallagher & Marras, 2012) and can
potentially damage spinal tissues. The same load carried in a backpack produced a lower
average shear load of 345 N (Rose et al., 2013). Thus, methods for direct measurement of such
exposures need to identify and quantify both dimensions, namely, carrying mode and load
magnitude, besides temporal aspects of duration and frequency.
Wearable inertial sensors (or inertial measurement units, IMUs) have gained attention
in ergonomics research (Valero, Sivanathan, Bosché, & Abdel-Wahab, 2016) for field-based
direct measurement of worker postures. Inertial sensors are light-weight, portable, less
obtrusive, and have on-board power and data storage capacity that allows for data collection
over a long work period (Bergmann, Mayagoitia, & Smith, 2009; Mayagoitia, Nene, & Veltink,
2002). Typical use of wearable inertial sensors in ergonomics studies to date have focused on
posture measurement in occupational tasks (e.g., lifting and pushing/pulling) to estimate the
orientation of a body segment or joint angle between segments (Estill, MacDonald, Wenzl, &
Petersen, 2000; Nath, Akhavian, & Behzadan, 2017; M. C. Schall Jr., Fethke, Chen, & Gerr, 2015;
Valero et al., 2016) relative to a neutral posture (i.e., typically upright standing) in order to
quantify the extent and proportion of time spent in a deviated or non-neutral posture. During
load carriage, postural deviation relative to an upright standing posture is subtle compared to
other occupational tasks and less consequential than the duration, magnitude and mode of
STATISTICAL PREDICTION OF LOAD CARRIAGE
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load carriage. However, the magnitude and position of hand loads can alter gait kinematics and
posture (Ghori & Luckwill, 1985; Goh, Thambyah, & Bose, 1998; Hong & Cheung, 2003;
Majumdar, Pal, & Majumdar, 2010; Park et al., 2010; Qu & Yeo, 2011).
Movements of the torso, pelvis, and lower extremities change systematically with
external load levels and carrying modes when walking (Kinoshita, 1985; LaFiandra, Wagenaar,
Holt, & Obusek, 2003). Kinematic adjustments for maintaining posture and stability during
walking are reflected in temporal and kinematic gait parameters, and rotational movement
coordination between the torso and pelvis (LaFiandra et al., 2003; van Emmerik & Wagenaar,
1996). Using data from body-worn inertial sensors, a recent study confirmed systematic
difference in thoracic and pelvic sway and movement coordination based on load level between
two-handed anterior and side carry (Lim & D'Souza, under review). Specifically, in that study,
carrying hand-loads that weighed 4.5 kg, 9.1 kg, and 13.6 kg in two-handed anterior vs. side
carrying modes were associated with significant differences in coronal and transverse thoracic-
pelvic coordination measured using relative phase angles after adjusting for stride length and
gait speed. The present study aims to leverage information about changes in gait kinematic
patterns for estimating the duration, relative magnitude and mode of load carriage using
inertial sensing and predictive modeling.
Predictive modeling or machine-learning (ML) techniques have been used in
combination with wearable sensor data to extract contextual task information beyond just
quantifying posture. For example, activity recognition is an area of active research where data
from body-worn inertial sensors are used for classifying daily activities (Oshima et al., 2010;
Ravi, Dandekar, Mysore, & Littman, 2005), detecting gait events (Aminian, Najafi, Büla, Leyvraz,
STATISTICAL PREDICTION OF LOAD CARRIAGE
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& Robert, 2002; Coley, Najafi, Paraschiv-Ionescu, & Aminian, 2005; Sabatini, Martelloni,
Scapellato, & Cavallo, 2005), and predicting safety critical events such as falls (Bagalà et al.,
2012; Schwickert et al., 2013; Wu & Xue, 2008). The application of such techniques to
occupational ergonomics is still lagging. A few ergonomics studies have combined predictive
modeling with the wearable sensor data in activity recognition to classify manual material
handling tasks (Kim & Nussbaum, 2014), assembly tasks (Stiefmeier et al., 2006), and patient
handling activities (Lin, Song, Xu, Cavuoto, & Xu, 2017), and to detect states of fatigue from gait
kinematics during walking (Baghdadi, Megahed, Esfahani, & Cavuoto, 2018; Janssen et al., 2011;
Zhang, Lockhart, & Soangra, 2014). Lee (2008) applied linear discriminant analysis (LDA) to gait
kinematics data obtained from a 3-D optical motion capture system to distinguish between
unloaded versus loaded gait with participants wearing a vest weighing 12.5 kg. Their study
showed potential for using gait kinematics to classify carrying load condition but was limited to
a single carrying mode and load magnitude. Collectively all of these previous studies suggest
the possibility for leveraging information about postural adaptations during load carriage
obtained by inertial sensors combined with predictive modeling techniques to create new
algorithmic approaches for assessing physical exposures from load carriage in situ.
The aim of this paper was to develop and assess a statistical prediction algorithm as
proof-of-concept that uses gait kinematics calculated from body-worn inertial sensor data for
classifying hand-load carrying mode and load level. The statistical prediction algorithm
implemented in this study incorporates a priori biomechanical knowledge about the effects of
load carriage on human gait patterns to inform the data segmentation process, computing and
STATISTICAL PREDICTION OF LOAD CARRIAGE
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selecting of predictor variables, and the structure of the statistical model. We discuss these
steps in the context of leveraging ML techniques for ergonomics exposure assessment.
2.0 Methods
2.1 Study Participants
Nine healthy men were recruited from the university community for the study.
Participants had ages ranging from 18 to 25 years with an average ± standard deviation (SD) of
22.0 ± 3.0 years, stature of 1.75 ± 0.05 m, weight of 77.11 ± 9.98 kg, and BMI of 24.87 ± 2.84
kg/m2. Participants were screened for pre-existing back injuries or chronic pain with a body
discomfort questionnaire adapted from the body mapping exercise by NIOSH (Cohen, Gjessing,
Fine, Bernard, & McGlothlin, 1997 for more details). All participants were right-handed and
right-footed when tested with the questionnaire adapted from the Edinburgh handedness
inventory (Oldfield, 1971). Prior to the study, participants completed a written informed
consent approved by the university’s institutional review board.
2.2 Experiment Procedures
A pre-experimental session was conducted to determine each participants’ Maximum
Acceptable Weight of Carry (MAWC; Cheng & Lee, 2006), which was later used to set the
normalized load levels in the main experiment. For the measurement of one-handed MAWC,
participants were asked to carry a 2.3 kg box with their right hand and walk 5 m back and forth.
The box had dimensions of 152.4 mm width x 177.8 mm depth x 127 mm height, and one
handle on the top (Figure 1-a). The weight of the box could be increased in increments of 2.3
kg. A method of limits discussed in Snook and Ciriello (1991) was used for determining the
STATISTICAL PREDICTION OF LOAD CARRIAGE
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maximum acceptable weight that the participant could carry without perceiving unusual
tiredness, weakness, overheating, or breathlessness. The procedure was repeated to measure a
two-handed MAWC by using a box with dimensions of 177.8 mm width x 228.6 mm depth x
203.3 mm height held anteriorly with both hands using handles located on the side (Figure 1-d).
Figure 1: Images showing the four carrying modes performed in this study: (a) one-handed right
hand carry (1H-R), (b) one-handed left hand carry (1H-L), (c) two-handed side carry (2H-Side),
(d) two-handed anterior carry (2H-Anterior) along with the location of four inertial sensors
attached on the body at T6, S1, and shank (R, L).
During the main experiment, participants carried a weighted box down a levelled
corridor (26.2 m length x 1.6 m width) for a distance of 24 m in four carrying modes commonly
used in occupational settings (Figure 1), viz., one-handed right hand carry (1H-R), one-handed
left hand carry (1H-L), two-handed side carry (2H-Side), and two-handed anterior carry (2H-
Anterior), in addition to a no-load (i.e., empty-handed reference) condition. Two levels of box
weights were carried in each mode, namely, 50% MAWC and 75% MAWC. The one-handed
STATISTICAL PREDICTION OF LOAD CARRIAGE
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MAWC for each participant was used to calculate the normalized load levels of 50% and 75% for
the one-handed conditions (i.e., 1H-R and 1H-L). Likewise, the two-handed anterior MAWC was
used to calculate the 50% and 75% normalized load levels for the 2H-Anterior and 2H-Side
carrying modes. Hand load was equally divided between the right and left boxes in the 2H-Side
carry.
Two no-load walk trials were performed first, and subsequently each participant
performed two consecutive trials of eight loaded conditions ( = 4 carrying modes x 2 load levels)
in random order. Walking speed was self-selected to observe the natural adaptation in walking
patterns due to different load carriage conditions. Two-minute rest breaks between each walk
trial were given to participants to minimize carry-over effects of fatigue.
2.3 Instrumentation
Four commercial inertial sensors (Opal, APDM Inc, Portland, OR, USA) were attached on
the participant at the sixth thoracic vertebra (T6), the first sacral vertebra (S1), and superior
aspect of the right and left shank midway between the lateral femoral and malleolar
epicondyles (Figure 1-d). Sensor placement was informed by the need for computing specific
predictor variables. Sensors attached on the right and left shank were used for detecting key
gait events (e.g., heel strike and toe-off) and subsequent temporal gait parameters (Aminian et
al., 2002). Sensors placed on the T6 and S1 were used for calculating torso and pelvis postural
sway and thoracic-pelvic coordination measures (Lim & D’Souza, 2018) that were related to the
objectives of this study. Velcro straps were used to secure the sensors located at T6 and S1, and
double-sided hypoallergenic tape and medical wrap were used to attach the sensors to shank
STATISTICAL PREDICTION OF LOAD CARRIAGE
10
(R, L). One of the sensor axes (i.e., x-axis) was attached aligned with the proximal-distal axis of
the body segment and pointing downward.
The inertial sensors recorded 3-D accelerometer, gyroscope, and magnetometer sensor
data at a sampling frequency of 80 Hz. Accelerometer and gyroscope data were filtered using a
second-order low-pass zero-lag Butterworth filter with a cut-off frequency of 2-Hz. Gyroscope
data (angular velocity in radians/s) was 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 (Williamson & Andrews, 2001).
2.4 Algorithm to Classify Carrying Mode and Load Level
A statistical classification algorithm was developed with six general steps described in
the following section (Figure 2). Four carrying modes (i.e., 1H-R, 1H-L, 2H-Side, 2H-Anterior) and
no-load and two load levels, 50% MAWC vs. 75% MAWC, were the target outcome variables for
each walking trial.
2.4.1 Step 1: Detect Gait Cycles
Individual gait cycles were detected using a custom gait detection algorithm adapted
from Aminian et al. (2002) and described in detail by Lim and D'Souza (under review). To
summarize this process, first, gait events signifying heel strike and toe-off were detected from
the angular velocity (rad/s) data obtained from the sensors on the right and left shank (Figure
2). Second, gait cycles were denoted by finding the sequence of the following events: right heel
strike → left toe-off → left heel strike → right toe-off → next right heel strike. The algorithm
was implemented in MATLAB (MATLAB R2016b, The MathWorks Inc., Natick, MA, USA).
STATISTICAL PREDICTION OF LOAD CARRIAGE
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Figure 2: Overview of the carrying mode and load level classification algorithm developed in the study. The right panel shows
example classification results for three consecutive gait cycles at a two-handed anterior carry with 50% MAWC load condition.
STATISTICAL PREDICTION OF LOAD CARRIAGE
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2.4.2 Step 2: Calculate Predictor Variables
Sixteen gait parameters were calculated over each gait cycle, namely, seven temporal gait
measures, six torso and pelvis postural sway and three thoracic-pelvic coordination measured
in the transverse, sagittal, and coronal planes, respectively (Table 1). Thoracic-pelvic
coordination was measured as the relative phase angle of rotational movement between the
torso and pelvis segments (Burgess-Limerick, Abernethy, & Neal, 1993; LaFiandra et al., 2003).
This particular set of variables were considered based on preliminary work on 2H-Anterior load
carriage (Lim & D'Souza, 2018). Swing, left leg (%) and stance, left leg (%) durations were highly
correlated with the initial double support (%) duration with a Pearson’s correlation coefficient
of |R| > 0.8. Swing, right leg (%) and stance, right leg (%) durations were also highly correlated
with the terminal double support (%) duration. Thus, four temporal parameters, i.e., stance,
right and left leg (%) and swing, right and left leg (%) were excluded from further analysis to
avoid multi-collinearity, reducing the final set of predictor variables to twelve.
STATISTICAL PREDICTION OF LOAD CARRIAGE
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Table 1: List and definitions of gait parameters calculated from the inertial sensor data for each
gait cycle. Excluding stance right and left leg (%) and swing right and left leg (%), all of the
remaining 12 parameters were used as predictors in the classification model.
Parameter
Definition
Temporal parameters (7 nos.)
1
Gait cycle duration (sec)
Duration of one gait cycle (one right plus left step
duration).
2,3
Stance, Right and Left Leg (%)
Percentage of the gait cycle for when the right or left foot
is on the ground.
4,5
Swing, Right and Left Leg (%)
Percentage of the gait cycle for when the right or left foot
is not on the ground.
6
Initial double support (%)
Percentage of the gait cycle for when both feet are on the
ground after a right foot heel-strike.
7
Terminal double support (%)
Percentage of the gait cycle for when both feet are on the
ground after a left foot heel-strike.
Torso and pelvis postural sway (6 nos.)
8~13
ROM at T6 and S1 in the transverse, sagittal,
and coronal planes (deg.)
Range of rotation angle at torso and pelvis in transverse,
sagittal, and coronal planes: Max (integrated angular
velocity) – min (integrated angular velocity)
Thoracic-pelvic coordination (3 nos.)
14~16
Mean relative phase angle between T6 and
S1 in the transverse, sagittal, and coronal
planes (deg.)
Average (pelvic phase angle – thoracic phase angle) in
transverse, sagittal, and coronal planes. Phase angle (t) =
arctan (normalized angular velocity (t) /normalized
integrated angular velocity (t))
2.4.3 Step 3: Predict Carrying Mode per Gait Cycle
A two-stage hierarchical model was implemented comprising a first stage classification
model for predicting carrying mode (Step 3 in Figure 2), and a second stage classification model
for predicting the load level (Step 5 in Figure 2). The design of the hierarchical structure was
informed by prior studies demonstrating that the mode of load carriage influences alterations
in gait kinematics significantly more than changes in the load levels within the same carrying
mode (Ghori & Luckwill, 1985; Kinoshita, 1985). The random forest technique (Breiman, 2001)
was chosen as the classification algorithm for both stages because it produced the highest
prediction accuracy in a preliminary study on estimating the carrying mode and load level
STATISTICAL PREDICTION OF LOAD CARRIAGE
14
compared to other common multiclass classification algorithms such as classification and
regression trees, multinomial logistic regression, linear discriminant analysis, and support
vector machines. Random forest is a nonparametric machine-learning technique based on a
decision tree that grows recursive binary partitioning at the nodes of the tree. Hundreds of
decision trees are grown by random selection of a subset of predictor variables each time. The
prediction results across all trees are averaged to obtain the final consensus prediction. In this
study, prediction was performed over 500 trees for each gait cycle with a selection of four
predictor variables each time. This step was implemented using the randomForest package
v.4.6-12 (Liaw & Wiener, 2002) in R v.3.3.1 (R Core Team, 2016).
2.4.4 Step 4: Select Carrying Mode per Trial
Classification results from each gait cycle within a walk trial were used to decide the
final classification result for the walk trial. In our algorithm, predictions in Steps 3 and 5 were
performed independently for each gait cycle; however, under the assumption that the carrying
mode and load level does not change within a walk trial, probabilities of the current gait cycle
data belonging to a specific carrying mode were updated based on the prior gait cycles using
the method of Bayesian inference (Box & Tiao, 2011). Assume that a prediction model (M) is
developed based on the current gait cycle data (Y). When data on new gait cycle (Y*) is
obtained, the posterior distribution can be updated using Baye’s theorem as follows:
!
"
#
$
%&
'
( ) & !
"
%&
$
#
'
& !"#'
where p(M|Y*) is the posterior distribution updated by the new data (Y*), p(Y*|M) is the
probability that the new data belongs to each class given the prediction model, and p(M) is the
prior probability before updating the new data. A normalizing constant, c, ensures that the
STATISTICAL PREDICTION OF LOAD CARRIAGE
15
posterior probabilities of all classes add up to one. Using this method, the classification results
from prior gait cycles were cumulatively used to update the classification result of the current
gait cycle until the last gait cycle identified in a walk trial. The carrying mode with the highest
posterior probability at the final gait cycle within the walk trial was selected as the final
prediction outcome for the carrying mode.
2.4.5 Step 5: Predict Load Level per Gait Cycle
Steps 5 and 6 were performed to classify the load level within each predicted carrying
mode. While one model was developed for Step 3, four separate models were developed in
Step 5 for each carrying mode excluding the no-load condition. Separate load level prediction
models were built based on a priori knowledge that the important kinematic parameters to
distinguish load levels differ by carrying mode (Ghori & Luckwill, 1985; Kinoshita, 1985). Gait
data from each walk trial was subjected to one of four classification models for predicting the
load level depending on the carrying mode that was predicted in Step 4. Load levels were
predicted for each gait cycle in the walk trial.
2.4.6 Step 6: Select Load Level per Trial
Similar to step 4, Bayesian inference was used to update the classification result of the
load level within a walk trial.
2.5 Evaluating Model Performance
The performance of the prediction model was evaluated using 10-fold cross-validation
tests. All walk trials were split into ten roughly equal-sized subsamples or folds (k = 1,2, … ,10).
In each iteration, one subsample (k) was selected as the validation data for testing the model,
and the remaining k-1 subsamples were used for training the model (Hastie, Tibshirani, &
STATISTICAL PREDICTION OF LOAD CARRIAGE
16
Friedman, 2008). This test was iterated k times until all subsamples were used as a validation
set. Gait cycles from the same walk trial were grouped when partitioning, so that all of the gait
cycles from the same walk trial were included in the same subsample.
An identical test was performed for the classification algorithm without the Bayesian
update (Step 4 and 6) as a comparison to investigate the benefit of applying the Bayesian
inference to the algorithm. In this model, the final classification result for the walk trial was
decided by averaging the classification results from individual gait cycles within the walk trial.
Three performance measures were computed:
§ Average prediction accuracy = [# true positives + # true negatives] / [# total walk trials],
§ Precision = [# true positives] / [# true positives + # false positives]), and
§ Sensitivity = [# true positives] / [# total positives].
2.6 Interpreting the Predictive Model
The relative importance (%) of predictor variables in each model was calculated to
investigate the importance of each predictor variable in predicting the response variable
(Boulesteix, Janitza, Kruppa, & König, 2012). In a random forest model, variable importance is
measured as the impurity of data after it is split at each node. The Gini impurity Index, a
common measure of the node impurity, is computed by averaging impurity at a data partition
across all classes of the response variable (Strobl, Boulesteix, Zeileis, & Hothorn, 2007). A larger
decrease in the Gini index represents a larger decrease in impurity at a data partition and a
greater importance of the predictor variable in the classification model. The magnitude of the
Gini index can differ by models, so it is a common practice to calculate a normalized index as
the relative importance (%) by giving the most important variable a score of 100% in each
STATISTICAL PREDICTION OF LOAD CARRIAGE
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model.
3.0 Results
A total of 162 walk trials were performed across all participants (9 participants x 9
conditions per participant x 2 walk trials per condition). Excluding three interrupted walk trials
during the data collection, 159 walk trials were used for building and testing the algorithm. A
total of 2028 gait cycles were recorded across all participants with an average ± standard
deviation (SD) of 12.8 ± 1.5 (range: 9 to 17) gait cycles in each repetition of the walk trials.
Across all participants the average ± SD for the MAWC (kg) in the one-handed condition
was 32.5 ± 7.9 kg and in the two-handed condition was 31.0 ± 4.5 kg, respectively. Load levels
for the walk trials were set to 50% and 75% of the participant’s one-handed and two-handed
MAWC value. Average ± SD values of normalized MAWCs were 17.4 ± 3.9 (50% MAWC) and
24.4 ± 6.3 (75% MAWC) for the one-handed conditions, and 16.6 ± 2.3 (50% MAWC) and 23.2 ±
3.53 (75% MAWC) for the two-handed conditions.
3.1 Bayesian Inference Update vs. Averaging
Applying Bayesian inference in Steps 4 and 6 outperformed the averaging approach in
terms of the prediction accuracy. The model with the Bayesian inference correctly classified the
carrying mode in 96.9% of the walk trials and load level in 93.1% of the walk trials, resulting in
an average overall prediction accuracy of 91.8%. In comparison, the averaging approach
correctly classified the carrying mode in 95.3% and load level in 72.7% of the walk trials,
resulting in an average overall prediction accuracy of 72.2%, which was 19.6% lower compared
to the Bayesian approach.
STATISTICAL PREDICTION OF LOAD CARRIAGE
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Figure 3 depicts an example model prediction for a walk trial in a 2H-Anterior carry
consisting of fifteen consecutive gait cycles. The Bayesian inference approach showed
convergence in the posterior probability after four gait cycles in the example described in
Figure 3. Across all conditions an average ± SD of 4.5 ± 1.5 gait cycles were needed to correctly
classify carrying mode with a posterior prediction over 90%. Since the Bayesian approach
demonstrated a clear advantage in prediction performance, we limit the subsequent analysis
and discussion to this approach.
STATISTICAL PREDICTION OF LOAD CARRIAGE
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Figure 3: Example results from the random forest classification to predict carrying mode for
fifteen consecutive gait cycles from a two-handed anterior carry walk trial without (top-panel)
and with (bottom-panel) Bayesian inference applied. In each gait cycle, the mode with the
highest predicted probability is labeled as the classification result for that gait cycle. In this
example, without Bayesian inference applied (top-panel) 3 of the 15 gait cycles were
misclassified as either 1H-L (gait cycle #1) or no-load (gait cycle #9 and #10). In the bottom
graph, Bayesian inference was applied to the same data and updated the posterior probability
of the gait cycle based on prior gait cycles cumulatively. The probability of the data predicted as
the correct class (i.e., two-handed anterior carry in this case) exceeded 0.9 after four gait cycles
and converged to 1.0 in subsequent cycles.
STATISTICAL PREDICTION OF LOAD CARRIAGE
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3.2 Model Performance
3.2.1 Carrying Mode Classification
Table 2 presents the confusion matrix for the prediction model along with the precision
and sensitivity values from the 10-fold cross-validation test. The prediction accuracy for
classifying the carrying mode was 96.9%. The precision of the no-load, 2H-Side, and 2H-Anterior
conditions were 100% while it was lower in the 1H-R and 1H-L at 91.4% and 94.3% respectively.
Sensitivity was also highest for the no-load, 2H-Side and 2H-Anterior conditions at 100%,
compared to 1H-R and 1H-L at 94.1% and 91.7% respectively. Five walk trials were misclassified
in carrying mode between the 1H-R and 1H-L conditions, and interestingly were all at the load
level of 75% MAWC.
Table 2: Confusion matrix showing the classification result for carrying modes from each walk
trial data: No-load = empty-handed reference condition, 1H-R = one-handed right carry, 1H-L =
one-handed left carry, 2H-Side = two-handed side carry, 2H-Anterior = two-handed anterior
carry.
Predicted Carrying Mode
Total
Walk Trials
Sensitivity
No-load
1H-R
1H-L
2H-Side
2H-
Anterior
Actual Carrying
Mode
No-load
18
0
0
0
0
18
100%
1H-R
0
32
2
0
0
34
94.1%
1H-L
0
3
33
0
0
36
91.7%
2H-Side
0
0
0
36
0
36
100%
2H-Anterior
0
0
0
0
35
35
100%
Total Walk Trials
18
35
35
36
35
159
Precision
100%
91.4%
94.3%
100%
100%
STATISTICAL PREDICTION OF LOAD CARRIAGE
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To further investigate the misclassified cases, posterior probabilities of the target
carrying mode for individual walk trials were plotted by gait cycle (Figure 4). Consistent with
Table 2, there were no misclassified cases for the no-load, 2H-Side, and 2H-Anterior conditions.
The posterior probabilities in these conditions (n = 89 walk trials) converged to 1.0 typically
after 4 to 5 gait cycles even though the initial probability at the first gait cycle was very low (P <
0.5) in many cases. On the other hand, the posterior probabilities for multiple walk trials in the
1H-R and 1H-L conditions did not converge to 1.0 and fluctuated throughout the walk trial.
Figure 4: Posterior probabilities of the target carrying mode in each walk trial depicted by gait
cycles. Misclassified classes are marked as red dotted lines.
STATISTICAL PREDICTION OF LOAD CARRIAGE
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3.2.2 Load Level Classification
The average prediction accuracy for classifying the load level across all carrying modes
was 93.1% (n = 148 of 159 walk trials), which was 3.8% lower than the classification of carrying
mode. Table 3 summarizes the confusion matrices for the models by carrying mode along with
the precision and sensitivity values from the cross-validation test. Prediction accuracies within
each carrying mode were 91.4% for 1H-R, 91.4% for 1H-L, 94.4% for 2H-Side, and 91.4% for 2H-
Anterior. Among the carrying modes, the model for 2H-Side had the highest prediction accuracy
with just 1 out of 18 walk trials misclassified between the 50% MAWC and 75% MAWC load
levels each.
Table 3: Summary of the classification results in terms of sensitivity and precision of predicted
load levels for each predicted carrying mode.
Carrying mode
Load level
Sensitivity (%)
Precision (%)
One-handed, Right
50% MAWC
88.9
94.1
75% MAWC
94.1
88.9
One-handed, Left
50% MAWC
88.9
94.1
75% MAWC
94.1
88.9
Two-handed, Side
50% MAWC
94.4
94.4
75% MAWC
94.4
94.4
Two-handed, Anterior
50% MAWC
88.9
94.1
75% MAWC
94.1
88.9
3.3 Variable importance
Figure 5 shows the relative importance (%) of the predictor variables in each
classification model. Thoracic-pelvic coordination in the coronal plane was the most important
predictor in the classification model for carrying mode, followed by postural sway of the pelvis
in the coronal plane and transverse thoracic-pelvic coordination in a distant second and third,
STATISTICAL PREDICTION OF LOAD CARRIAGE
23
respectively (Figure 5, panel A). For 1H-R, gait cycle duration and thoracic-pelvic coordination in
the coronal plane were nearly equally most important predictors for classifying the load level
(Figure 5, panel B-1). However, unlike the classification model for carrying mode with one
dominant predictor variable, the load level classification model for 1H-R also indicated terminal
double support, torso and pelvis postural sway in the coronal plane, and torso postural sway in
the transverse plane as relatively important (i.e., > 75%). Coronal plane measures of torso and
pelvis postural sway and thoracic-pelvic coordination were the three most important predictors
in the load level classification model for 1H-L compared to the rest of the predictor variables
(Figure 5, panel B-2). Pelvis postural sway in the sagittal plane was the most important
predictor when classifying the load level in the 2H-Side carry (Figure 5, panel B-3). Pelvis
postural sway in the transverse plane and coronal plane were the second and third most
important predictors. Pelvic postural sway in the transverse and coronal planes were both
equally important when predicting the load level in the 2H-Anterior carry, followed by torso
postural sway in the coronal plane (Figure 5, panel B-4).
STATISTICAL PREDICTION OF LOAD CARRIAGE
24
Figure 5: Relative importance (%) of the predictor variables computed as the mean decrease in the Gini index relative to the
maximum (100%) for each of the five classification models, namely, for carrying mode (panel A) and for load level (Panels B-1 to B-
4).
STATISTICAL PREDICTION OF LOAD CARRIAGE
25
4.0 Discussion
Wearable sensing technology combined with predictive modeling has the potential to
advance the science of field-based exposure assessment by providing information about work
content beyond just quantifying worker postures. This study assessed the potential for
classifying carrying mode and load level using gait kinematics calculated from the inertial
sensor-derived data. As an initial investigation, the study was intentionally limited to a small
homogenous participant sample (n = 9) with gait data recorded at self-selecting walking speeds
over multiple walk trials and conditions, namely 159 walk trials and 2028 gait cycles in total, to
build and assess the statistical model. Thoracic and pelvic range of motion and thoracic-pelvic
coordination were important predictors in classifying carrying mode and relative load level
compared to unloaded gait. The accuracy of statistically classifying carrying mode and load
levels were 96.9% and 93.1%, respectively. Use of the Bayesian inference for updating
probabilities with the incoming gait cycles improved the overall prediction accuracy by 19.6%
with 4 to 5 gait cycles needed to converge on the classification result.
4.1 Methodological Contributions
Biomechanical exposures during physical work are typically characterized by three main
dimensions (Winkel & Mathiassen, 1994), i.e., intensity (magnitude or amount of the forces and
loads which are also a function of task and posture), frequency (repetition), and duration (the
time the physical activity is performed). The algorithm presented provides information on all
three dimensions of physical exposures during load carriage. The gait detection algorithm (Step
1) used in this study implements a robust detection of the start and end of walking, so the
duration of the walking (either unloaded or loaded) can be accurately estimated. The load level
STATISTICAL PREDICTION OF LOAD CARRIAGE
26
classification (Step 5 & 6) predicts the relative intensity of the load carried, the measurement of
which can be obtrusive in work settings that involve carrying loads of different magnitudes
(e.g., construction work, distribution centers). The carrying mode classification (Step 3 & 4)
combined with the load level classification (Step 5 & 6) quantifies the frequency of load carriage
by categorizing the task in terms of its mode and load level.
Developing a successful prediction algorithm requires knowledge of the underlying
system or domain when deciding on the structure of the statistical model (e.g., single-stage vs.
multi-stage), segmenting the data, and selecting predictor variables or features within the data
segment (Hastie et al., 2008). The current study incorporated biomechanical information about
the association between human gait kinematics and load carriage to develop and assess the
statistical prediction model, which had direct bearing on model performance. We discuss key
aspects of the model development and assessment in the subsequent sections.
4.1.1. Data Segmentation and Choice of Predictor Variables
Statistical prediction with continuous time series data requires that the data be re-
structured into segments. The methods of segmenting a continuous stream of sensor data
influences the performance of the prediction model and thus needs consideration (Avci, Bosch,
Marin-Perianu, Marin-Perianu, & Havinga, 2010). To be useful, the method of data
segmentation needs to represent the data such that the prediction error of all segments across
time is minimized (Keogh, Chu, Hart, & Pazzani, 2001). A common approach to segmenting time
series data uses sliding windows with a fixed sliding width (e.g., Kim & Nussbaum, 2014). Other
approaches include a top-down approach of splitting time-series data into partitions by
decreasing the segment length iteratively until the prediction error is below a user-specified
STATISTICAL PREDICTION OF LOAD CARRIAGE
27
threshold (Keogh et al., 2001), and a bottom-up algorithm that starts from the finest possible
partition of the time-series data and increases the length of the segment iteratively. Unlike the
latter two iterative approaches, use of a sliding window is the most popular form in online
applications since the segmentation can be performed while the data is streaming. Another
online approach involves segmenting data based on pre-defined events, as was the case in this
study.
In this study, inertial sensor data were segmented by first detecting gait cycles, which
represents a meaningful segmentation of the data stream. For a given carrying mode and load
magnitude, gait cycle duration for a person shows little variability over short bouts of walking;
however, the cycle duration can vary significantly between participants and across load carry
conditions for the same person (LaFiandra et al., 2003; Martin & Nelson, 1986). For example, in
the present study gait cycle duration ranged between 0.93 s to 1.16 s across all participants and
carrying conditions. If a fixed window of average gait cycle, for example 1 s, was used for data
segmentation instead of the proposed adaptive algorithm, the kinematic variables calculated
within a segment would be less representative of the gait patterns relative to a data segment
that captures a complete gait cycle. Another advantage of the proposed data segmentation
method is that it can be used in online applications in near-real-time. Once a gait cycle is
detected from an incoming data stream, predictor variables for the gait cycle can be computed
and used as input to the classification algorithm. The delay in the classification output would be
just over one gait cycle (~ 1 sec). The detection of an exact start and end of gait cycles resolves
an issue of underestimating the task duration reported in previous studies on classifying
manual material handling tasks (Kim & Nussbaum, 2014). In that study, task durations were
STATISTICAL PREDICTION OF LOAD CARRIAGE
28
underestimated by about 14% when classifying tasks using inertial sensor data that were
segmented by sliding window of fixed duration. The detection of gait events and subsequent
data segmentation based on gait cycles used in the current study would produce a more
accurate estimate of duration of load carriage compared to a sliding window of fixed duration.
Choice of the predictor variables, which requires computing (i.e., extracting) and
selecting features from sensor data, is also an important step towards building a simpler,
comprehensible model while ensuring adequate prediction accuracy (Liu, Motoda, Setiono, &
Zhao, 2010). Predictor variables need to represent the main characteristics of a data segment,
so that it contains important cues for distinguishing levels of outcome variables (Avci et al.,
2010). Use of domain specific features such as step detection, step variance, and vertical and
horizontal acceleration of the sensor segment were found to increase prediction performance
when classifying physical activities such as walking, running, cycling, and resting compared to
using only time- and frequency-domain features (Bieber & Peter, 2008). Kim and Nussbaum
(2014) used descriptive statistics on whole-body joint angles to classify manual material-
handling tasks. The present study used temporal, and thoracic and pelvic kinematic gait
parameters as predictor variables. As opposed to using raw sensor data, the use of such domain
specific features could significantly reduce the number of feature vectors used in a classification
algorithm and also increase prediction accuracy.
4.1.2. Structure of the Model
A two-stage hierarchical model structure was implemented in this study where the
carrying mode was classified first followed by classification of load levels within mode. Without
the hierarchical structure, nine classes or categories would need to be predicted (i.e., 4 carrying
STATISTICAL PREDICTION OF LOAD CARRIAGE
29
modes x 2 load levels, and 1 no-load condition). With the same number of test datasets,
increasing the number of target classes often increases the possibility of misclassification and
lowers prediction accuracy. Reducing a k-class problem to a set of k two-class problems by
building a separately trained binary classification model for each of the k problems is a common
approach to deal with the multiclass classification (Anand, Mehrotra, Mohan, & Ranka, 1995).
However, this approach does not provide guidance about which the two classes need to be
paired or the effect of having different pairs on model performance. Considering that
classification problems in occupational settings may have a high number of potential outcome
classes such as task type (e.g., lifting, pushing, pulling, carrying, etc.) and intensity level (e.g.,
forceful exertions; Mark C Schall Jr., Sesek, & Cavuoto, 2018), multiclass classification models
would be more common than two-class classification.
Implementing a hierarchical structure in classification models with multiple target
classes has three advantages. First, implementation of the hierarchical model significantly
improves the prediction accuracy compared to classifying the combination of different task
conditions at one time. In our preliminary testing with the same test dataset, the multiclass
prediction model with no hierarchical structure resulted in a prediction accuracy of 48.0%,
which was 43.8% lower than the proposed hierarchical model. In a different study aimed at
classifying the handle height and force intensity level in a pushing task, the hierarchically
structured model produced a 50.0% greater prediction accuracy compared to the multiclass
prediction model (Lim & D'Souza, 2017). In both cases, the hierarchy of the models was built
with an empirical understanding of the relative influence of different task variables (Ghori &
Luckwill, 1985; Kinoshita, 1985; Lim, Case, & D’Souza, 2016; Lim & D'Souza, 2018).
STATISTICAL PREDICTION OF LOAD CARRIAGE
30
A second advantage of having a hierarchical model structure is the opportunity for
optimizing the predictor variable set in each model. The analysis of variable importance (Figure
5) suggests that the important variables in each model differ across classification models. This
information can be used to reduce the number of predictor variables in each model thereby
decreasing model complexity and computational effort, and increasing model interpretability.
Third, the hierarchical structure allows prediction performance assessment at every
level of the hierarchy independently. In addition, the algorithms that occur in the lower level do
not affect the performance of the algorithms that occur in the upper levels (Mathie, Celler,
Lovell, & Coster, 2004).
4.1.3 Model Interpretability
The random forest method used in this study is flexible in modeling relationships
between multiple predictors and outcome classes, without requiring any a priori assumptions
about the type of relationships, e.g., linear vs. nonlinear. This flexibility lends to high prediction
accuracy as was evident in this study, but comes at some expense of interpretability, i.e., it is
difficult to quantify how any individual predictor is associated with the outcome. The primary
means for interpreting a random forest model uses the average decrease in the Gini Index as an
indicator of the relative importance of predictor variables. Our results on important predictor
variables are supported by findings from previous studies that demonstrate that changes in gait
from hand-load carriage are evident in the measures of thoracic and pelvic postural sway and
thoracic-pelvic coordination (Anderson et al., 2007; Madinei & Ning, 2017, LaFiandra et al.,
2003; van Emmerik & Wagenaar, 1996).
STATISTICAL PREDICTION OF LOAD CARRIAGE
31
The relative importance of thoracic and pelvic sway and intersegment coordination
differed across all five prediction models (Figure 5). Specifically, 2H-Side carriage increases
angular momentum and moment of inertial in the coronal and transverse planes (Madinei &
Ning, 2017). With this increase, postural stability is maintained by an increased but anti-phasic
sway (i.e., counter-rotation) of the pelvis in the coronal, transverse, and sagittal planes with
increasing loads relative to unloaded gait (Lim and D’Souza, 2018). Similar contributions of
thoracic and pelvic sway and intersegment coordination in the coronal plane were also
identified in the 1H-R and 1H-L side carry. However, the load prediction models for 1H right vs.
left side carry showed differences in relative importance of other temporal gait parameters,
namely, gait cycle duration and terminal double support. These differences may be due to
bilateral asymmetries in strength and gait, and is a topic of further investigation.
Restricted arm movements and close coupling between the torso and pelvis during 2H-
Anterior load carriage is associated with increased pelvic sway in the coronal and sagittal
planes, and a decrease in pelvic sway in the transverse plane relative to unloaded gait
(Anderson et al., 2007; Madinei & Ning, 2017). Consequently, movement coordination between
the thoracic-pelvic segments is more in-phase or synchronized in the coronal and transverse
planes with increasing load relative to unloaded gait (Birrell & Haslam, 2008; Majumdar et al.,
2010).
4.2 Study Limitations
Certain limitations of this laboratory study are worth emphasizing in order to
contextualize the study findings and implications for practice. Given its focus on model
development and assessment, the study sample comprised of a relatively small and
STATISTICAL PREDICTION OF LOAD CARRIAGE
32
homogenous sample of healthy, young male participants. Further investigation is needed to
test the generalizability of the model across the spectrum of worker demographics on known
sources of variability in gait such as age (Ko, Hausdorff, & Ferrucci, 2010), gender (Mazzà, Iosa,
Picerno, & Cappozzo, 2009), obesity (Cau et al., 2014; Pamukoff, Dudley, Vakula, & Blackburn,
2016) and strength (Lord et al., 1996; Nigg, Fisher, & Ronsky, 1994). The present study found
that a minimum of 4 to 5 gait cycles needed to converge on a prediction result. This finding
suggests that in subsequent studies the amount of data collected from each participant can be
economized in lieu of a larger and more diverse sample.
For jobs that might involve long duration of manual load carriage, cumulative fatigue
from load carriage may induce alterations in gait (Barbieri et al., 2013; Helbostad, Leirfall, Moe-
Nilssen, & Sletvold, 2007; Yoshino, Motoshige, Araki, & Matsuoka, 2004). Prior studies have
associated fatigue with increased variability in step length, step width, and mediolateral trunk
accelerations while walking, and increased double support duration during load carriage. Qu
and Yeo (2011) reported hip and torso range of motion to increase while carrying a backpack
load immediately following a fatiguing treadmill exercise. Age is also reported to moderate the
effects of fatigue on gait (Barbieri et al., 2013; Helbostad et al., 2007). To minimize the
confounding effects of fatigue, the present study introduced two-minute rest breaks between
each walk trial of 24 m distance. However, subsequent studies will need to account for the
effects of cumulative fatigue from long duration exposures to load carriage on thoracic and
pelvic range of motion and coordination.
The proposed model also requires that load magnitudes be normalized to individual
carrying capacity determined using either biomechanical strength or psychophysical criteria.
STATISTICAL PREDICTION OF LOAD CARRIAGE
33
This may be a limitation in certain work settings that do not have a steady cohort of workers
that can be assessed. Additional study is also needed to consider more diverse task conditions
that are representative of applied settings (e.g., size and form-factor of the load carried,
location of handles, and weight distribution of the load) and over extended periods before the
proposed algorithm can be used as a field evaluation tool for manual load carriage work.
4.3 Application and Relevance
Quantifying physical exposures to load carriage can be challenging in non-routinized
work settings where load intensity, duration and frequency vary between workers and within
worker across time. The present study represents an initial step towards the development of a
real-time exposure assessment tool that leverages wearable inertial sensing and predictive
modeling for use in occupational settings. Multiple previous studies have used inertial sensors
to classify between different types of activities (e.g., walking vs. sitting), however few studies
have delved into predicting task demands within a specific activity (i.e., relative changes
between load within the same task). The present study is novel in this regard. A key
contribution of this study was the reliance on a biomechanical understanding of the effects of
load carriage on pelvic and thoracic movement and coordination into a practical framework for
predicting carrying mode and relative load conditions. From a practical standpoint, our findings
have direct implications for attachment locations of inertial sensors. Leveraging subtle
movement patterns of the torso and pelvic implies that the sensors be closely attached to the
skin on these segment as opposed to worn on top of loose clothing, helmet, or gloves. Newer
forms of wearable sensing embedded in smart clothing may help overcome these potential
usability concerns (Esfahani & Nussbaum, 2018).
STATISTICAL PREDICTION OF LOAD CARRIAGE
34
Extending the proposed approach to include other tasks that are of interest in
ergonomics exposure assessment such as lifting and pushing/pulling will require task-specific
models that capture intrinsic kinematic adaptations to task demands. For example, findings by
Zehr, Howarth, and Beach (2018) indicating that thoracic-pelvic coordination in the sagittal
plane is influenced by lifting mode (i.e., freestyle, flexed and neutral spine; Zehr et al., 2018)
can be leveraged to develop and assess predictive models of lifting modes using body worn
inertial sensors. These task specific models can be envisioned as modules nested within an
overarching activity classification model. This framework aligns with our proposed approach of
a multi-stage hierarchical model structure led by classification by task type (e.g., lifting, pushing,
pulling, carrying, etc.), followed by models that classifying mode and intensity within task type
to account for the large number of potential outcome classes in occupational settings.
5.0 Conclusions
This study presents an algorithmic framework for combining wearable inertial sensing,
gait kinematics, and statistical prediction for classifying carrying modes and load levels during
manual load carriage. Overall, the algorithm was sensitive in discerning loaded from unloaded
walk conditions within 4 to 5 gait cycles. Prediction accuracy and the relative importance of
thoracic and pelvic measures as predictors were found to differ by models for carrying mode
and load level. The few misclassified trials occurred largely in the 1H-R and 1H-L side carrying
modes. Further investigation is needed to test the generalizability of the model across the
spectrum of worker demographics and load carrying conditions. The present study also
provides practical information about locations for inertial sensor placement and the type and
STATISTICAL PREDICTION OF LOAD CARRIAGE
35
amount of data required for distinguishing carrying modes and relative load levels for use in
subsequent studies of higher ecological validity and increased generalizability.
Applying statistical classification techniques to movement analysis requires an
understanding of machine learning theory, signal processing, and feature extraction. By
emphasizing model development and assessment, this study also attempts to explain aspects of
classification and predictive modeling towards encouraging the application of these statistical
techniques to ergonomics practice.
Acknowledgements
Early work on this study was supported by the National Institute for Occupational Safety and
Health (NIOSH), Centers for Disease Control and Prevention (CDC) under the training Grant T42
OH008455. Data analysis and manuscript preparation was also supported by funding received
from the National Institute on Disability, Independent Living, and Rehabilitation Research
(NIDILRR) under the grant 90IF0094-01-00. 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 NIOSH, NIDILRR, ACL, or HHS, nor
imply endorsement by the U.S. Government.
Disclosure statement
The authors declare no conflict of interest.
STATISTICAL PREDICTION OF LOAD CARRIAGE
36
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