RESEARCH ARTICLE
Using wearable sensors to classify subject-
specific running biomechanical gait patterns
based on changes in environmental weather
conditions
Nizam Uddin AhamedID1*, Dylan Kobsar1☯, Lauren Benson1☯, Christian ClermontID1☯,
Russell KohrsID1☯, Sean T. Osis1,2☯, Reed Ferber1,2,3☯
1 Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada, 2 Running Injury Clinic, University
of Calgary, Calgary, Alberta, Canada, 3 Faculty of Nursing, University of Calgary, Calgary, Alberta, Canada
☯ These authors contributed equally to this work.
* nizam.ahamed1@ucalgary.ca
Abstract
Running-related overuse injuries can result from a combination of various intrinsic (e.g., gait
biomechanics) and extrinsic (e.g., running surface) risk factors. However, it is unknown how
changes in environmental weather conditions affect running gait biomechanical patterns
since these data cannot be collected in a laboratory setting. Therefore, the purpose of this
study was to develop a classification model based on subject-specific changes in bio-
mechanical running patterns across two different environmental weather conditions using
data obtained from wearable sensors in real-world environments. Running gait data were
recorded during winter and spring sessions, with recorded average air temperatures of -10˚
C and +6˚ C, respectively. Classification was performed based on measurements of pelvic
drop, ground contact time, braking, vertical oscillation of pelvis, pelvic rotation, and cadence
obtained from 66,370 strides (~11,000/runner) from a group of recreational runners. A non-
linear and ensemble machine learning algorithm, random forest (RF), was used to classify
and compute a heuristic for determining the importance of each variable in the prediction
model. To validate the developed subject-specific model, two cross-validation methods
(one-against-another and partitioning datasets) were used to obtain experimental mean
classification accuracies of 87.18% and 95.42%, respectively, indicating an excellent dis-
criminatory ability of the RF-based model. Additionally, the ranked order of variable impor-
tance differed across the individual runners. The results from the RF-based machine-
learning algorithm demonstrates that processing gait biomechanical signals from a single
wearable sensor can successfully detect changes to an individual’s running patterns based
on data obtained in real-world environments.
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OPEN ACCESS
Citation: Ahamed NU, Kobsar D, Benson L,
Clermont C, Kohrs R, Osis ST, et al. (2018) Using
wearable sensors to classify subject-specific
running biomechanical gait patterns based on
changes in environmental weather conditions.
PLoS ONE 13(9): e0203839. https://doi.org/
10.1371/journal.pone.0203839
Editor: Yih-Kuen Jan, University of Illinois at
Urbana-Champaign, UNITED STATES
Received: May 8, 2018
Accepted: August 28, 2018
Published: September 18, 2018
Copyright: © 2018 Ahamed et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: Relevant data are
included with the submission, any additional data
(e.g., remaining 5 runners data) will be available
upon request from the Running Injury Clinic and
University of Calgary Institutional Data Access /
Ethics Committee (CHREB) by contacting Dr.
Stacey A. Page, chair of CHREB at omb@ucalgary.
ca.
Funding: This study was partially funded by the
Natural Sciences and Engineering Research
Introduction
Running is one of the most common recreational activities around the world but despite its
popularity, each year approximately 50% of runners experience a running-related musculo-
skeletal injury [1–3]. The etiology of overuse running injuries is multifactorial, and can result
from the interaction of many extrinsic factors, such as environmental conditions, running sur-
face, footwear, and weekly training mileage, as well as intrinsic risk factors such as age, foot
strike pattern, and gait biomechanics [1–4]. Prolonged exposure to these intrinsic and extrinsic
risk factors may lead to overuse running injury [5]. One risk factor that has received very little
attention in the literature is whether gait biomechanical patterns change as a result of environ-
mental weather conditions.
Previous investigations of injury risk, based on ambient temperature, have suggested that
tissue damage may occur due to a lack of proper warm up. For example, Milgrom et al.
reported an increased risk of Achilles paratendinitis among infantry recruits in winter condi-
tions, as compared to summer [6]. On the other hand, cold weather has been shown to reduce
shoe-surface traction, resulting in a reduced risk of acute knee and ankle injuries among foot-
ball players [7, 8]. Only a handful of studies have investigated the effect of environmental
weather conditions on running performance, but none have investigated whether gait biome-
chanics change as a result of environmental weather. For example, Ely et al., [9] reported a
progressive reduction in marathon performance as temperatures increased from 5 to 25
degrees C, for both males and females and across competitive and recreational runners, but
performance was more negatively affected for slower runners. These studies suggest that
weather can affect both physiological and mechanical aspects of running gait. Thus, it is possi-
ble that different weather conditions may be associated with concomitant changes in gait bio-
mechanical running patterns, however, to our knowledge no study has directly investigated
this hypothesis.
The main reason the inter-relationship between environmental weather conditions and gait
biomechanics has not been investigated is most likely due to the inability to collect such data
in a laboratory setting. However, due to the availability and utility of modern portable inertial
measurement units (IMUs) and global positioning system (GPS), it is now possible to collect
data outside of the laboratory setting [10–12]. Since large quantities of data can be collected
using wearable devices, machine learning (ML) techniques are also needed to better under-
stand the complexities of gait biomechanics and how concomitant changes in biomechanical
patterns may be related to injury or performance [13, 14]. Furthermore, traditional biome-
chanics research generally investigates potential differences between two groups using group-
based analyses. For example, several researchers have identified differences in running patterns
based on different age groups, gender and/or injury status [15–17]. In contrast, more recent
research has shown that group-based comparisons are not efficacious due to the existence of
sub-groups [18, 19], and other studies have shown that subject-specific models are necessary
to understand individual differences and risk factors [20–23]. Several authors have also used
different ML algorithms to develop these sub-group-based models, including principal compo-
nent analysis, support vector machine and hierarchical cluster analysis [17–19]. However, to
our knowledge no study has directly investigated whether a subject-specific model provide
deeper insight into emerging IMU-based biomechanical investigations based on changes in
environmental weather conditions.
Therefore, the purpose of this study was to develop a classification model based on subject-
specific changes in biomechanical running patterns across two different environmental
weather conditions using data obtained from wearable sensors in out-of-laboratory environ-
ments. We hypothesized that we could classify changes in subject-specific running patterns
Running biomechanical gait patterns identification based on environmental weather conditions
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Council of Canada (NSERC: Discovery Grant
1028495, Accelerator Award 1030390, and Idea-2
Innovation Awared I2IPJ 493875-16), a University
of Calgary Eyes High Postdoctoral Research award,
and a Strategic Research Grant from the Vice-
President (Research) at the University of Calgary.
The funders had no role in study design, data
collection and analysis, decision to publish, or
preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
based on weather conditions with a classification accuracy greater than 80% and that the
ranked order of variable importance would be based on subject-specific ML models. A second-
ary objective was to determine the ranking of the biomechanical variables, based on their
importance in the classification margin, in order to better understand changes in subject-spe-
cific running patterns.
Methods
Participants
Six recreational runners (Five females: age = 47.5±9.69 years, height = 169.17±6.56 cm,
weight = 67.42±11.5 kg; and one male: age = 29 years, height = 170 cm, weight = 75 kg) volun-
teered to participate in the study. The runners were free of any neuromuscular diseases or
musculoskeletal injuries and they were registered for a half-marathon training program man-
aged by a local running group. This protocol was approved by the University of Calgary Con-
joint Health Research Ethics Board (REB 16–2035) and all runners provided their written
informed consent.
Instrumentation
Biomechanical gait variables from each runner were recorded using the Lumo Run1 (Lumo
Bodytech Inc., Mountain View, CA) wearable inertial measurement unit (IMU), consisting of
a 3-dimensional (3D) accelerometer, magnetometer, and gyroscope. (dimension: 4.98cm x
2.84cm x 0.99cm). The Lumo Run IMU was attached to the posterior aspect of either the run-
ner’s waistband or running belt as per the manufacturer’s instructions [24] (Fig 1). This wear-
able sensor device measured and recorded data for six different biomechanical variables [24]
and averaged these data for each ten-strides (Table 1) and a complete description of these vari-
ables can be found on the manufacturer’s website [24]. A GPS watch (Garmin vı´voactive1
HR; Garmin International Inc., KS, USA) was attached to each runner’s preferred wrist (Fig 1)
and recorded running speed (m/s), distance (kilometers (km)), and global positioning data,
including latitude, longitude and altitude, every second.
Data collection
Gait variables from winter runs were recorded from mid-February to mid-March, while spring
runs were recorded from late April to mid-May. Each runner performed two training runs
during each weather condition for a total of four runs used in this analysis. Each run began at
8:30 AM on a Sunday, and was completed outdoors on pavement, and along a similar route.
Data corresponding to the temperature (degrees Celsius), snow depth (cm), precipitation
(mm), and humidity (%) for each run were derived from three different International Air
Transport Association-affiliated weather stations in Calgary, AB: Canada Olympic Park
(WDU), Calgary International Airport (YYC), and Calgary INT’L CS Alberta (PCI).
For each run, data from km 0 to 1 were discarded, as this was considered a warmup period,
and any data following 6-km was also not used in the analysis in order to minimize the effects
of fatigue, if any. Therefore, only 5-km of data (i.e., from km 1 to 6) were analyzed from each
run and in total, the input data consisted of 66,370 strides (~11,000/runner) across the four
runs. Altitude, latitude and longitude data from the Garmin watch were used to ensure the ele-
vation profile for each of the four runs were similar, and that the data from each run were col-
lected from a route with minimal changes in elevation, in order to minimize the effect of
running on uphill and/or downhill.
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Data analysis
A robust, and non-linear machine learning classifier, called Random Forest (RF), was used to
develop the classification model which measured the accuracy and importance of gait bio-
mechanical variables in classifying runs of differing environmental weather conditions. The
RF classifier has been shown to provide a higher classification accuracy than other existing ML
classifiers with a faster computation speed, while facilitating complex interactions among pre-
dictor variables and providing information about the importance of each predictor variable
[25–27]. In other word, RF provides variable importance measures to rank predictors accord-
ing to their predictive power [28]. Two validation methods (Method 1: one-against-another
and Method 2: partitioning datasets) were used to ensure that the proposed RF-based subject-
specific classification approach was robust and that the data were not overfit [29]. With
Method 1 (one-against-another) data combining one winter run and one spring run were con-
sidered the training dataset, and the testing dataset consisted of the remaining winter and
spring runs. With Method 2 (partitioning datasets), 70% of each runner’s total strides per-
formed in both weather condition were randomly selected for training, and the remaining
Fig 1. The two wearable sensors devices (Lumo Run and Garmin) used to record the data during running.
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30% were used for testing purposes. Individual training and test sets were generated for each
subject. Each classification method was applied using the standalone Python programming
language (version 3.6, www.python.org) [30]. The developed RF model was trained and cross-
validated using the built-in Anaconda distribution of Python with notable packages including
matplotlib, numpy, scipy, and scikit-learn (“sklearn.ensemble.RandomForestClassifier”) [31,
32]. The number of trees in the RF was set to 100, as previous research has shown this is a suffi-
cient number for obtaining high accuracy solutions to similar classification problems [33, 34].
Additionally, the RF used a Gini index to calculate the impurity of a node from the CART
(classification and regression tree) learning system in order to construct the decision trees
[26]. The RF trees compute a heuristic for determining how significant a variable (6 Lumo
Run gait variables) is in predicting a target (weather). Statistical analyses were performed using
repeated measures ANOVA (P<0.05) and Cohen’s d effects size estimates were calculated for
each difference on the outcome measures between each weather condition.
Results
Fig 2 presents an overview of the RF-based classification accuracy obtained with test data gen-
erated using the two validation methods. Using Method 2 (partitioning datasets), the RF-based
model demonstrated an excellent overall mean classification accuracy of 95.42%. In fact, all
runners yielded a classification accuracy higher than 90% with the exception of Runner 5, who
exhibited a classification accuracy of 89.06%. In contrast, the overall mean classification accu-
racy obtained with Method 1 (one-against-another) was 87.18%, and all the runners yielded a
classification accuracy higher than 85% except for Runner 5, who exhibited an accuracy of
70.47%. Significant differences (P<0.05) in the overall classification accuracies were also
found between the methods. Overall, for all runners, Method 2 yielded a higher classification
accuracy than Method 1. Moderate differences in classification accuracy were also observed
between Methods 1 and 2 for Runner 5 (18.59%) and Runner 6 (14.37%), but the differences
in classification accuracy between the methods were slight for Runner 3 (8.0%) and Runner 4
(6.14%), and non-existent for Runner 1 (2.16%), and Runner 2 (0.45%).
Table 1. Features recorded from the wearable devices.
Device
Features
Unit
Frequency
Lumo Run
Pelvic drop (PD)
(frontal plane motion of the runner’s pelvis)
Degree (deg)
100- Hz
Vertical oscillation of pelvis (VOP)
(measurement of vertical displacement)
Centimeter (cm)
Ground contact time (GCT)
(time of total foot contact with the ground)
Millisecond (ms)
Braking
(reduction in forward velocity following foot strike)
Meter/sec (m/s)
Pelvic rotation (PR)
(transverse plane motion of the runner’s pelvis)
Degree (deg)
Cadence
(number of bilateral steps per minute)
Steps per minute (SPM)
Garmin Vı´voactive HR
Heart rate (HR)
Beats per minute (BPM)
1- Hz
Altitude
Meter (m)
Distance
Kilometer (km)
Global position-latitude
Degree (deg)
Global position- longitude
Degree (deg)
Running speed
Meter/sec (m/s)
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Overall, the ranking of the variables, based on their importance in the classification margin,
differed across all runners and classification methods (Table 2 and Fig 3). For example,
although vertical oscillation of pelvis was the most important variable, using both methods, for
Runners 2 and 5, it ranked lower for Runner 1, wherein pelvic drop was the most important
variable across both methods. Similarly, pelvic rotation was the second-ranked variable for
both methods for Runners 2 and 4 but was less significant for the other runners. Overall,
cadence was less important for all runners, with the exception of for Runner 3, wherein it was
the second most important variable using Method 2. Another notable difference was found for
braking where for Runner 4 it was the most important variable using Method 1 but only the
third most important variable with Method 2. A similar inconsistency was found for pelvic
rotation, which was identified as the most important variable with Method 1 but was ranked
fourth with Method 2. The remaining three variables, braking, ground contact time, and
cadence, were not found to be important for the classification task and were consistently
ranked third, fifth and sixth across both methods, respectively (Fig 4).
Table 2 also presents the results of the statistical analyses of the individual and overall
results from both weather conditions. All runners, except Runner 4, demonstrated lower verti-
cal oscillation of the pelvis in winter than in spring. The pelvic drop of two runners (Runner 2
and Runner 3) and the pelvic rotation of three runners (Runner 3, 4 and 6) were higher in win-
ter than in spring. There was no clear difference in braking between winter and spring because
three runners (Runners 1, 3 and 4) exhibited the same values during both conditions, two run-
ners (Runners 4 and 6) had lower braking values in winter, and one runner (Runner 2)
Fig 2. Classification accuracies obtained with Method 1 (black) and Method 2 (white); : P<0.05.
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presented a higher braking value in winter. Two runners (Runners 1 and 2) had lower ground
contact time values in winter, whereas two runners (Runners 3 and 4) had a higher ground
contact time in winter, and the remaining two runners (Runners 5 and 6) had a similar value
during both weather conditions. Finally, with the exception of Runner 4, all runners demon-
strated a higher cadence during winter. Overall, five biomechanical variables (excluding
cadence) demonstrated lower values during winter runs as compared to spring runs. However,
no significant differences were found between the two weather conditions for any of the six
variables (P>0.05). Cohen’s d effect size and 95% confidence intervals [95%CI] are presented
in Table 2 and reveal the effect sizes between winter and spring runs were small (i.e., d<0.5),
except for vertical oscillation of the pelvis, pelvic drop, and cadence, which were moderate (i.
e., 0.5<d<0.8).
The results of the environmental weather conditions are presented in Table 3 and show the
average temperature, humidity and snow depth were significantly different between winter
and spring runs, along with no differences in precipitation.
Table 2. RF-based variable importance and descriptive statistics obtained with both methods and for all individual runners.
Gait Variable
Analyzed
parameters
Subject-specific results
Overall results
R-1
R-2
R-3
R-4
R-5
R-6
Mean±SD
95%CI
P
ES
Vertical oscillation
of pelvis (cm)
M1-VI (%)
5.84
46.24
8.87
6.59
32.39
15.15
19.18±16.53
(-17.55, 7.07)
0.32
-0.45
M2-VI (%)
2.18
40.84
33.64
12.87
44.63
12.37
24.42±17.53
Win (mean)
6.08
4.53
6.34
7.03
11.38
7.90
7.21±2.33
(-1.38, 0.21)
0.12
-0.78
Spr (mean)
6.22
5.03
7.97
6.68
12.72
8.17
7.81±2.68
Pelvic drop
(deg)
M1-VI (%)
39.62
6.25
21.59
14.03
9.67
15.63
17.80±11.91
(-16.98, 7.62)
0.37
-0.41
M2-VI (%)
58.23
7.78
9.57
31.46
7.91
19.93
22.48±19.8
Win (mean)
8.8
9.16
11.2
8.26
10.24
7.59
9.21±1.32
(-0.57, 2.89)
0.14
-0.71
Spr (mean)
11.61
7.81
10.92
10.5
11.53
9.88
10.37±1.41
Pelvic rotation
(deg)
M1-VI (%)
15.57
23.41
10.74
27.62
26.55
26.15
21.67±6.91
(-4.35, 16.9)
0.19
0.62
M2-VI (%)
12.9
27.17
4.85
30.38
3.66
13.17
15.36±11.16
Win (mean)
14.27
11.52
11.31
19.39
15.51
11.74
13.96±3.16
(-3.70, 3.89)
0.95
-0.03
Spr (mean)
15.98
16.36
10.49
13.69
17.48
10.29
14.05±3.09
Braking
(m/s)
M1-VI (%)
13.85
9.1
12.91
38.42
6.1
27.33
17.95±12.39
(-12.50, 12.29)
0.98
-0.01
M2-VI (%)
9.5
10.35
14.3
19.88
9.02
45.29
18.06±13.95
Win (mean)
0.27
0.25
0.36
0.34
0.3
0.31
0.31±0.04
(-0.03, 0.06)
0.34
-0.43
Spr (mean)
0.27
0.22
0.36
0.37
0.31
0.4
0.32±0.07
Ground contact time (ms)
M1-VI (%)
19.87
11.05
41.98
8.68
13.03
6.42
16.84±13.15
(-6.59, 17.15)
0.31
0.47
M2-VI (%)
15.01
11.06
15.73
3.36
20.63
3.56
11.56±6.97
Win (mean)
258.33
254.47
298.37
247.04
290.8
272.88
270.32±20.7
(-7.8,
3.93)
0.43
-0.35
Spr (mean)
267.47
263.26
297.04
243.13
290.03
272.59
272.25±19.4
Cadence
(steps/min)
M1-VI (%)
5.25
3.95
3.91
4.66
12.26
9.32
6.56±3.44
(-10.27, 7.13)
0.66
-0.19
M2-VI (%)
2.18
2.8
21.91
2.05
14.17
5.66
8.13±8.16
Win (mean)
173.81
183.63
161.67
173.73
151.64
166.21
168.45±11.1
(-1.62, 7.21)
0.16
0.66
Spr (mean)
172.53
181.29
150.68
174.48
149.05
165.89
165.65±13.2
VI: variable importance; M1: Method 1; M2: Method 2; R: Runner. Win: Winter; Spr: Spring.
 P: significantly different (P<0.05)
ES: effect size (Cohen’s d). 95%CI: 95% confidence intervals
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The speed and overall route were similar between sessions, as presented in Table 4. In addi-
tion, the speed, heart rate, altitude, latitude and longitude showed no significant differences
between the two weather conditions (Table 4).
Discussion
The objective of this study was to classify changes in subject-specific running gait patterns
based on the environmental weather (winter vs. spring) conditions using an RF classifier. The
findings of the current study support our hypotheses and demonstrate that an RF-approach
was a robust method for accurately classifying large datasets collected using wearable sensors
in real-world settings. Interestingly, each subject’s classification method had different impor-
tant predictor variables based on the RF evaluation. Therefore, each individual runner exhib-
ited different changes in overall gait biomechanics, and changes in the weather conditions
affected the mechanics of individual runners differently. To our knowledge, this study consti-
tutes the first examination of changes in subject-specific gait biomechanics based on environ-
mental weather conditions. These findings also support the efficacy of wearable technology,
and subsequent data science approaches for understanding the complexities of running gait
patterns based on collecting data in out-of-laboratory environments [29, 35].
Overall, the results of this investigation demonstrate that the presence of snow and colder
temperatures results in runner-specific changes in biomechanical gait patterns, possibly in an
effort to reduce the risk of falling due to the slippery surface [36]. These assumptions are sup-
ported by previous studies that also indicated injury rates were higher in colder weather
Fig 3. Importance of the different variables for each runner identified using two validation methods. All the variables in this stacked bar graph are shown
in the same vertical order for both methods (VOP, PD, PR, braking, GCT and cadence).
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conditions compared to warmer weather due to running on icy and slippery running paths
[37–39]. Moreover, the results of the current study also indicate that the changes in running
biomechanical patterns between weather conditions may contribute to overuse running-
related injuries [5]. For example, when pelvic drop was important for classification (e.g. Run-
ner 1), there was greater pelvic drop in spring than winter, but when it was not important (e.g.
Runners 2 and 3), it was lower in spring than winter. A similar pattern was observed in vertical
oscillation of the pelvis: when it was important (e.g. Runners 2 and 5), there was greater
amounts of oscillation in spring than winter, but when it was less important (e.g. Runner 4),
there was greater oscillation in winter than spring. These results suggest that the runners
involved in the current study adjusted to different weather conditions by reducing vertical or
frontal plane motion accompanied by slight increases in running cadence and shorter stride
Fig 4. Graphical representation of the three most important variables (braking, PD and PR) for Runner 4 with Method 2. Each point is
equivalent to five strides. Data from both the training and testing sets are shown.
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Table 3. Environmental weather conditions experienced during running.
Weather
Temperature (˚C)
Snow depth (cm)
Humidity (%)
Precipitation (mm)
Winter
-9.74±4.85
P =
0.001 
2.97±2.83
P =
0.001 
75.41%
P = 0.000 
1.35±0.89
P = 0.46
Spring
+5.33±2.65
0.31±0.21
63.32%
1.73±0.62
: Significantly different (P <0.05)
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length. However, it is important to note that all of the participants were injury-free and these
aforementioned gait changes were not necessary to mitigate symptoms of injury. On the other
hand, adopting a more constrained running pattern may, over time, may contribute to an
overuse running injury [40]. Future prospective research is therefore necessary to help under-
stand the inter-relationship between environmental weather conditions, concomitant and sub-
ject-specific changes in gait patterns, and the etiology of injury.
The RF classifier has received increasing attention within the gait-related research commu-
nity due to its ability to yield excellent classification results and its fast-computational process-
ing speed [41, 42]. In addition, this classifier provides consistent classifications using
predictions derived from an ensemble of decision trees as well as a ranking of the variables
according to their ability to differentiate between the target classes [41, 43]. The results of the
current study are largely consistent with previous RF-based gait biomechanics studies involv-
ing wearable sensors (40,41). However, while research has investigated how IMUs systems can
be used for the assessment of running biomechanics in laboratory and clinical settings [44],
very few studies have been conducted in real-world settings [45, 46]. Therefore, to provide
insights into this knowledge gap and open new research directions, the current study devel-
oped and evaluated subject-specific methods, using an RF classifier using data from a single
IMU, and achieved excellent classification accuracy results. Interestingly, the slight differences
in classification accuracy obtained between the two tested RF-methods suggest that the inclu-
sion of information from multiple runs is beneficial for building a successful model. In addi-
tion, the current study demonstrates that the RF algorithm was able to accurately classify and
determine the relative importance of each input variable for an individual runner [47, 48].
While it is important to note that the combination of multiple variables was needed to
achieve a high classification accuracy and fully understand the multidimensional characteris-
tics of the subject-specific running biomechanics associated with different weather conditions,
the current findings can be compared to previous studies that have either addressed the effects
of temperature on running performance [9, 49, 50] or injury rates [51]. For example, our find-
ings are consistent with previous work demonstrating the usefulness of multidimensional anal-
yses to better understand the complex patterns and inter-relationships between multiple
biomechanical variables when classifying runners based on subtle differences in gait patterns
that may be indicative of performance and/or injury [52–55]. Moreover, in the current study,
regardless of the classification method, all runners exhibited slightly lower values for all bio-
mechanical gait variables, except cadence, during winter as compared to spring. These findings
support previous research indicating a more economical running technique with a lower risk
Table 4. Specific running measurements of the different runners recorded using a wearable GPS (Garmin Vı´voactive HR).
Runners
Speed (m/s)
Heart rate (BPM)
Altitude (m)
Latitude (deg.)
Longitude (deg.)
Winter
Spring
Winter
Spring
Winter
Spring
Winter
Spring
Winter
Spring
R-1
2.40
2.39
161.13
154.01
1050.32
1067.18
51.05
51.05
-114.07
-114.05
R-2
2.36
2.36
112.45
117.97
1049.44
1071.58
50.84
51.06
-113.61
-114.06
R-3
2.18
2.27
146.65
143.02
1045.34
1066.30
51.05
51.06
-114.08
-114.07
R-4
2.39
2.36
140.68
126.82
1036.76
1061.51
51.05
51.05
-114.05
-114.05
R-5
2.54
2.32
154.94
155.96
1046.59
1064.64
51.05
51.06
-114.08
-114.06
R-6
2.36
2.40
141.99
151.14
1072.51
1051.28
51.05
51.05
-114.07
-114.05
Overall
2.37
2.35
142.97
141.49
1050.16
1063.75
51.02
51.06
-113.99
-114.06
P = 0.54
P = 0.57
P = 0.19
P = 0.27
P = 0.42
R: Runner.
https://doi.org/10.1371/journal.pone.0203839.t004
Running biomechanical gait patterns identification based on environmental weather conditions
PLOS ONE | https://doi.org/10.1371/journal.pone.0203839
September 18, 2018
10 / 15
of overuse injury during winter (colder) weather conditions [56–58]. Reduced pelvic drop has
also been considered a protective factor for patellofemoral pain [59, 60], as well as a gait
retraining strategy to reduce pain associated with this common running-related injury [61].
Future research is therefore necessary using wearable sensors in real-world situations to help
better elucidate these inter-relationships.
To our knowledge, this is the first study to quantify subject-specific changes in real-world
running gait biomechanics as a result of changes in environmental weather conditions. More-
over, the current study also represents one of the first investigations to analyse data from a
runner’s actual training run. Specifically, a recent systematic review [62] suggested that future
studies should involve long-term data collections, across multiple running bouts, and in a run-
ner’s natural environment, thus enabling prospective studies and the development of subject-
specific models of gait. Considering that the etiology of overuse running injuries is multifacto-
rial, and can result from the interaction of many extrinsic factors such as environmental condi-
tions, the results of the current study are an important contribution to help to better
understand injury etiology.
Limitation and future directions
The stated findings should be considered with respect to limitations. First, although there was
a small number of runners (n = 6), the method employed is generalizable considering that we
used subject-specific models to measure changes in gait parameters across 66,370 strides.
Regardless, further investigation using a larger sample size is necessary to determine if homog-
enous sub-groups, or clusters, will form as a result of consistent within-group biomechanical
changes (18,58). Second, we did not include any non-weather-related factors such as changes
in runner’s clothing, footwear, nutrition, sleep, or daily mood state profile. Future research
should consider these factors in order to gain a more complete understanding of how external
factors can influence running gait biomechanics. Third, although the present study examined
two different weather conditions, these results of the present study may only be applicable to
these weather conditions and temperatures. As well, the temperatures in the present study (i.e.,
-10˚C to +6˚C) were lower than those of Ely et al., [9] (i.e., +5˚C to +25˚C) and Knapik et al.,
[51] (i.e., +15˚C to +35˚C). Lastly, a limited number of spatiotemporal and biomechanical var-
iables obtained from a commercially available wearable sensor device were used for the current
study. While it is likely that additional or more complex variables from one or more wearable
sensors could improve the classification accuracy of the current study, we posit that the sim-
plicity and translatability to the current market of wearable sensors is a significant advantage
that should not be overlooked. Regardless, future research should include a broader range of
variables, and possibly more wearable sensor devices, in order to gain a deeper understanding
for subject-specific changes in gait patterns during out-of-laboratory data collections.
Conclusion
In summary, our developed RF-based subject-specific classification model demonstrated
excellent mean classification accuracies (87.18% and 95.42%) based on a large set of running
gait data from a small group of runners. These novel results support the use of a robust
machine learning approach for determining subject-specific changes in running gait patterns
based on differences in external weather conditions using a single IMU device. We believe that
our RF-based method may provide a more in-depth understanding of changes in gait biome-
chanics in response to extrinsic injury-risk factors and therefore conclude that the relationship
between environmental weather conditions and gait biomechanics is subject-specific and mul-
tifactorial and involves unique interactions between intrinsic and extrinsic factors.
Running biomechanical gait patterns identification based on environmental weather conditions
PLOS ONE | https://doi.org/10.1371/journal.pone.0203839
September 18, 2018
11 / 15
Supporting information
S1 File. Validation for Runner 4 (R-4) using Method 1 (one-against-another); classification
accuracy: 91.42%.
(XLSX)
S2 File. Validation for Runner 4 (R-4) using Method 2 (partitioning datasets in two sets at
a ratio of 7:3); classification accuracy: 97.56%.
(XLSX)
Acknowledgments
This study was partially funded by the Natural Sciences and Engineering Research Council of
Canada (NSERC) Idea-2-Innovation Award (grant #I2IPJ 493875–16), a University of Calgary
Eyes High Postdoctoral Research award, and a Strategic Research Grant from the Vice-Presi-
dent (Research) at the University of Calgary.
Author Contributions
Conceptualization: Dylan Kobsar, Reed Ferber.
Data curation: Nizam Uddin Ahamed.
Formal analysis: Lauren Benson, Sean T. Osis.
Funding acquisition: Reed Ferber.
Investigation: Christian Clermont.
Methodology: Nizam Uddin Ahamed.
Resources: Russell Kohrs, Reed Ferber.
Software: Christian Clermont, Russell Kohrs.
Supervision: Reed Ferber.
Validation: Lauren Benson.
Visualization: Christian Clermont.
Writing – original draft: Nizam Uddin Ahamed, Dylan Kobsar.
Writing – review & editing: Nizam Uddin Ahamed, Lauren Benson, Sean T. Osis, Reed
Ferber.
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Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions.
Ahamed, Nizam Uddin,Kobsar, Dylan,Benson, Lauren,Clermont, Christian,Kohrs, Russell,Osis, Sean T,Ferber, Reed