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A growing number of people all over the world are running. Gathering in-field data with wearable sensors is attractive for runners, clinicians and coaches to improve running performance, avoid injury or return to running after an injury. However, it is yet to be proven that commercially available wearable sensors provide valid data. The objective of this study was to assess the validity of five wearable sensors (Moov NowTM, MilestonePod, RunScribeTM, TgForce and Zoi) to measure ground reaction force related metrics, step rate, foot strike pattern, and vertical displacement of the center of mass during running. Concurrent/criterion validity was assessed against a laboratorybased system using Pearson’s correlation coefficients and ANOVAs. Step rate measurement provided by all wearable sensors was valid (all r>0.96 and p<0.001). Only Zoi provided valid vertical displacement of the center of mass (r=0.81, p<0.001); only TgForce provided meaningful estimates of instantaneous vertical loading rate (r=0.76, p<0.001); only MilestonePod could discriminate between a rear-, mid- and fore-foot strike pattern during running (p<0.001). None of the wearable sensors was valid for estimating peak braking force. In conclusion, only a few metrics provided by these commercially available wearable sensors are valid. Potential buyers should therefore be aware of such limitations when monitoring running gait variables.
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AbstractA growing number of people all over the world are running. Gathering in-field data with wearable sensors
is attractive for runners, clinicians and coaches to improve running performance, avoid injury or return to running
after an injury. However, it is yet to be proven that commercially available wearable sensors provide valid data. The
objective of this study was to assess the validity of five wearable sensors (Moov NowTM, MilestonePod, RunScribeTM,
TgForce and Zoi) to measure ground reaction force related metrics, step rate, foot strike pattern, and vertical
displacement of the center of mass during running. Concurrent/criterion validity was assessed against a laboratory-
based system using Pearson’s correlation coefficients and ANOVAs. Step rate measurement provided by all
wearable sensors was valid (all r>0.96 and p<0.001). Only Zoi provided valid vertical displacement of the center of
mass (r=0.81, p<0.001); only TgForce provided meaningful estimates of instantaneous vertical loading rate (r=0.76,
p<0.001); only MilestonePod could discriminate between a rear-, mid- and fore-foot strike pattern during running
(p<0.001). None of the wearable sensors was valid for estimating peak braking force. In conclusion, only a few
metrics provided by these commercially available wearable sensors are valid. Potential buyers should therefore be
aware of such limitations when monitoring running gait variables.
Index Terms accelerometer; Bland-Altman; inertial measurement unit; impact force; kinematic.
I. INTRODUCTION
UNNING is a very popular activity that can greatly
improve individual health status [1]. Researchers have
This research was funded by The Running Clinic and the Ordre
Professionnel de la Physiothérapie du Québec (OPPQ) and supported
in part by the Sentinel North program of Université Laval (Canada First
Research Excellence Fund). B. PdF. is supported by a postdoctoral
fellowship from the Fonds de recherche du Québec—Santé (FRQS)
and the Sentinel North program of Université Laval. J-S.R. is
supported by a salary award from the Canadian Institutes of Health
Research (CIHR). J-F.E. is supported by a postdoctoral fellowship from
the CIHR.
B. Pairot de Fontenay is with the Centre for Interdisciplinary
Research in Rehabilitation and Social Integration, Quebec City,
Quebec, Canada G1M 2S8 and The Running Clinic, Lac Beauport,
Quebec, Canada (e-mail: benoit.pdf@fmed.ulaval.ca)
J. S. Roy is with the Centre for Interdisciplinary Research in
Rehabilitation and Social Integration, Quebec City, Quebec, Canada
G1M 2S8 and the Department of Rehabilitation, Faculty of Medicine,
Université Laval, Quebec City, Quebec, Canada G1V 0A6.
B. Dubois is with The Running Clinic, Lac Beauport, Quebec,
Canada.
L. Bouyer is with the Centre for Interdisciplinary Research in
Rehabilitation and Social Integration, Quebec City, Quebec, Canada
G1M 2S8 and the Department of Rehabilitation, Faculty of Medicine,
Université Laval, Quebec City, Quebec, Canada G1V 0A6.
J.F. Esculier is with the Department of Physical Therapy,
University of British Columbia, Vancouver, British Columbia, Canada
and The Running Clinic, Lac Beauport, Quebec, Canada.
been interested in better understanding the way humans run
for many years [2]–[4]. For healthcare professionals and
coaches alike, improving running gait is fundamental for
injury prevention [5], rehabilitation [6] and performance
improvement [7]. Thus, gathering in-field data could aid in
advancing knowledge and providing personalized advice to
runners aiming to improve their performance or avoiding
being sidelined due to injury.
A number of kinematic and kinetic variables have been
associated with running injuries. Impact characteristics, in
particular the vertical loading rate of the ground reaction force
during the stance phase, has been suggested as a key factor for
stress fractures and overall injuries [8], [9]. Importantly, gait
retraining to decrease vertical loading rate has shown
promising reductions in injury rates [10]. Although less
studied, high peak braking force of the ground reaction force
has also been identified as a potential contributor to injury risk
[11]. Step rate [12] and foot strike pattern [13] also represent
important variables as they have been linked with running
injuries. For runners seeking to improve performance, lesser
peak braking force [14] and vertical displacement of the center
of mass [15], and increased step rate [16] have been correlated
with better running economy. A non-rearfoot strike pattern has
Validating commercial wearable sensors for
running gait parameters estimation.
Pairot
de
Fontenay
B
,
JS
,
Du
bois
B
,
Bouyer
L
and
Esculier
JF
R
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Journal
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also been linked with performance in observational studies
[17]–[19].
Healthcare professionals and coaches wishing to analyze
or alter someone’s running gait need to gather objective data
from the targeted individuals. However, laboratory-based
motion analysis systems are expensive and not accessible to
most. Furthermore, they may not reflect a runner’s actual gait
pattern while running in their usual environment [20].
Wearable sensors represent a potential low-cost solution to
collect in-field data [21]. Accurate wearables could be used to
monitor the ability and compliance of runners when gait
modifications are recommended to reduce injury risk, return to
running after an injury or improve performance [22].
Current commercial devices usually include an inertial
measurement unit and use manufacturer proprietary
algorithms with specific combinations of data from
accelerometers, gyroscopes, and magnetometers. Some
manufacturers claim that their device provide metrics related
to vertical and antero-posterior ground reaction forces, step
rate, foot strike pattern and vertical displacement of the center
of mass, which may all be valuable for runners seeking to
improve running performance and avoid injuries.
However it is yet to be proven that these wearable
sensors offer valid data to runners, clinicians and coaches [23],
[24]. Sensor location, fixation methods, weight of the device
and both data collection/processing methods may influence
measurement of running gait characteristics and therefore
affect the quality of the extracted data [25], [26].
The main objective of this study was therefore to assess
the concurrent validity of several popular, commercially
available wearable sensors in estimating vertical ground
reaction force (vertical loading rates) during running against a
laboratory-based instrumented treadmill and motion capture
system (gold standard). The second aim was to assess
concurrent or criterion validity of the same wearable sensors
in estimating antero-posterior ground reaction force (peak
braking force), measuring step rate and vertical displacement
of the center of mass, and determining foot strike pattern. Our
hypothesis was that estimates of vertical loading rates and
peak braking force would be more accurate with devices
attached to the tibia, while foot strike pattern would be more
accurate with shoe-mounted sensors. We also hypothesized
that most wearable sensors would show good to excellent
estimates of step rate and vertical displacement of center of
mass.
II. MATERIALS AND METHODS
A. Population
A convenience sample of 32 healthy participants was
recruited through the electronic mailing list of employees and
students of Université Laval (Table 1). Participants were
excluded if they reported any lower extremity musculoskeletal
injury or surgery within 2 years prior to participation, or
neurological disorder that could interfere with the task. All
participants had to be physically active (Tegner score >5/10)
and aged 18 to 45 years old. Participation in the study was
voluntary and informed consent was provided by all
participants. This study was approved by the local ethics
committee.
TABLE 1
PARTICIPANTS CHARACTERISTICS
Characteristics
n (female, male)
32 (13, 19)
Age, years (x±sd)
27.0 ± 5.5
Height, cm (x±sd)
174.4 ± 8.5
Weight, kg (x±sd)
69.1 ± 11.4
Teg
ner score (x±sd
, range
)
6.
7
± 1.4
(
5
-
10)
B. Wearable Sensors
As vertical loading rate of impact is a potential key outcome
associated with running related injuries, we selected wearable
sensors that provided at least one measure of impact [8], [9].
Based on this criterion, we found five wearable sensors
available on the market when this study was designed: Moov
NowTM (Moov, San Mateo, California, USA), MilestonePod
(Milestone Sports, Long beach, California, USA),
RunScribeTM (Montara, California, USA), Zoi (Runteq,
Tampere, Finland) and TgForce (Kelsec Systems Inc.,
Montréal, Canada). Other parameters associated with running
injuries or running performance such as metrics related to
antero-posterior ground reaction force (peak braking force),
step rate, foot strike pattern and vertical displacement of the
center of mass were also collected when available. Detailed
characteristics including sensor placement of the selected
sensors are reported in Appendix 1.
C. Data Collection
1) Wearable sensors
Wearable sensors were placed according to the
manufacturers’ instructions (Appendix 1) and data were
collected with their dedicated commercial application (for
TgForce, a beta Windows version software was used to collect
data). Devices were not calibrated, as calibration is not a
necessary step as per manufacturers’ recommendations.
2) Lab-based System
Three-dimensional motion analysis was performed using
a 9-camera VICON motion capture system and VICON Nexus
software (VICON motion systems, CA, USA). Kinematic data
were collected at 200 Hz. Rigid triads of non-colinear
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reflective markers were placed over the lumbosacral junction,
and on the lateral part of the foot, shank and thigh, bilaterally.
Triads attached to the thighs were made of custom-molded
thermoplastic and were secured with Velcro straps to
minimize movement artefacts. An additional 21 markers were
temporarily placed bilaterally over specific anatomical
landmarks for a standing calibration trial (anterior and
posterior tip of shoe, first and fifth metatarsal heads, medial
TABLE 2
EXTRACTED DATA CHARACTERISTICS FOR THE TESTED WEARABLE
SENSORS AND VALIDATION TESTING WITH LAB-BASED SYSTEM
Wearable
sensor
Extracted
data for
analysis
(averaged
over 1 min)
Validation testing with lab-
based system
Moov NowTM
(v5.2.4809.252)
- Step rate
- Impact
MoovNow_Step rate / Lab-
based_Step rate
MoovNow_Impact / Lab-
based_AVLR&IVLR
Milestone Pod
(v3.4.10)
- Foot strike
type (heel,
mid, toe)
- Step rate
- Rate of
impact (low,
mid, high)
MilestonePod_Footstrike / Lab-
based_Footstrike
MilestonePod_Step rate / Lab-
based_Step rate
MilestonePod _Rate of impact /
Lab-based_AVLR&IVLR
RunScribeTM
(v2.7.1)
- Step rate
- Shock
(combination
of impact
and braking
score)
- Impact Gs
- Braking Gs
- Foot strike
type
RunScribe_Step rate / Lab-
based_Step rate
RunScribe _Shock / Lab-
based_AVLR&IVLR
RunScribe _Impact-Gs / Lab-
based_AVLR&IVLR
RunScribe _Braking-Gs / Lab-
based_PeakBrakingForce
RunScribe_Footstrike / Lab-
based_FootAngle
Zoi
Runteq (2012-
2016)
- Step rate
- Braking
- Impact
- Bouncing
Zoi_Step rate / Lab-based_Step
rate
Zoi_Braking / Lab-
based_PeakBrakingForce
Zoi_Impact / Lab-
based_AVLR&IVLR
Zoi_Bouncing / Lab-
based_VertDispCM
Wearable
sensor
Extracted
data for
analysis
(averaged
over 1 min)
Validation testing with lab-
based system
TgForce
(v2.0.0.10)
- Step rate
- Max
vertical
acceleration
- Max
sagittal
acceleration
- Max vector
acceleration
TgForce_Step rate / Lab-
based_Step rate
TgForce_Z / Lab-based_
AVLR&IVLR
TgForce_X / Lab-
based_PeakBrakingForce
TgForce_3D / Lab-based_
AVLR&IVLR&PeakBrakingForce
AVLR: average vertical loading rate; IVLR: instantaneous vertical loading
rate; VertDispCM: vertical displacement of the center of mass.
and lateral malleoli, medial and lateral femoral epicondyles,
landmarks for a standing calibration trial (anterior and
posterior tip of shoe, first and fifth metatarsal heads, medial
and lateral malleoli, medial and lateral femoral epicondyles,
anterior superior iliac spine, iliac crest) prior to movement
data collection (Fig. 1). All markers were placed by the same
investigator who was an experienced physical therapist.
Simultaneously to three-dimensional motion analysis, and
after calibration, the ground reaction forces were collected at
1000 Hz using an instrumented treadmill with force plates
(Bertec, Columbus, OH, USA).
Fig. 1. Marker and triad placement for calibration.
D. Data Analysis
1) Wearable Sensors
Data were extracted from the different applications.
Running gait parameters associated with running injuries and
running performance were extracted (ground reaction force
[vertical and antero-posterior], step rate, foot strike pattern and
vertical displacement of the center of mass). Characteristics of
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Journal
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the extracted data for each wearable system are described in
Table 2.
2) Lab-based System
Concurrent or criterion validity of the wearable sensors
was evaluated using a gold standard laboratory-based
instrumented treadmill and motion capture system. Data
analyses were performed off-line using TheMotionMonitor
(Chicago, IL, USA) and a custom software written in Matlab
(MathWorks Inc., MA, USA). Three-dimensional hip, knee,
and ankle joint kinematics were calculated. Raw ground
reaction force data were filtered with a 50 Hz fourth order low
pass Butterworth filter. The vertical ground reaction force data
were then used to identify initial contact and toe off events
during running (20 N threshold).
Average and instantaneous vertical loading rates
(AVLR/IVLR) were calculated as described in Tenforde et al.
[27] to allow calculation in mid/forefoot strikers (no impact
peak). Briefly, the point of interest was determined as the first
point, above 75% of the participant’s body weight (bw) for
which the instantaneous slope of the vertical ground reaction
force was below 15 bw/s. AVLR was then calculated as the
average slope in the largest continuous region in the 20–80%
point of interest force region for which the slope was above
15% bw/s. IVLR was calculated as the peak slope between 20
and 100% of the force at the point of interest.
The step rate was calculated as the number of steps per
minute. The foot strike angle was calculated to determine the
foot strike pattern [28]. The foot strike angle was defined as
the foot angle at impact minus foot angle during static
calibration. Foot strike pattern was determined as followed:
midfoot strike = -1.68°< FSA < 8.08°, rearfoot strike = FSA >
8.08°, and forefoot strike = FSA < -1.68° [28].
Peak braking force was defined as the maximal posterior
force observed from foot strike to toe-off. Vertical
displacement of the center of mass was defined as the
difference between the highest and lowest position of the
center of mass for each stride. All variables were averaged
over one minute.
3) Statistical Analysis
Statistical analyses were conducted using the R software
(version R.2.7.2.; R Foundation for Statistical Computing,
Vienna, Austria). Pearson’s correlation coefficients were
calculated to determine the association between continuous
variables extracted by each wearable sensor and the lab-based
system. Associations were classified as negligible (0.0-0.3),
low (0.31-0.5), moderate (0.51- 0.7), good (0.71- 0.9), or
excellent (0.91-1.0) [29]. Good to excellent correlations were
considered clinically meaningful. Bland-Altman plots were
also computed when the variable collected by the wearable
sensor and the lab-based system were the same (step rate and
vertical displacement of the center of mass). Bias and 95%
limits of agreement were used to determine the accuracy of the
metrics provided by the wearable sensors.
Zoi provided a percent of rear-, mid-, and fore-foot strike
during the run. This format of data did not allow any statistical
analysis to test the validity of this metric.
To compare discrete and continuous variables, a one-way
ANOVA was performed (e.g. the three categories of
MilestonePod_Rate-of-impact to AVLR and IVLR). For all
tests, an alpha level of 0.05 was used for statistical
significance.
III. RESULTS
Data collected from all 32 participants were used for
RunScribeTM. However, due to technical issues with other
wearable sensors, data of 31 participants were analyzed for
MilestonePod and TgForce, 30 for Zoi and 25 for Moov
NowTM, respectively.
A. Step rate
Step rate measured by Moov NowTM, MilestonePod,
RunScribeTM, Zoi and TgForce showed excellent correlations
to the step rate obtained from the lab-based system (all r>0.96,
all p<0.001) (Table 3). According to Bland-Altman plots, bias
ranged from +0.93 to +4.51 and 95% limits of agreement from
±0.92 to ±6.13 steps per minute, respectively (Table 3).
B. Vertical Displacement of the Center of Mass
Zoi_Bouncing showed a good correlation to the vertical
displacement of the center of mass obtained with the lab-based
system (r=0.81, p<0.001) (Table 3). According to the Bland-
Altman plot, bias is -1.78cm and 95% limits of agreement
±1.50cm, respectively (Fig. 2, Table 3).
Fig. 2. Bland-Altman plots for vertical displacement of the
center of mass during running (cm). Difference
(Zoi_Bouncing - lab-based) versus average of values
measured by the lab-based system and Zoi_Bouncing.
Bold dotted line represents bias and dotted lines 95% limits
of agreement.
C. Foot Strike Pattern
Foot strike classification from MilestonePod
(MilestonePod_Footstrike [scored 1 to 3, and defined by the
manufacturer as heel, mid, toe, respectively]) could
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significantly discriminate foot strike patterns when compared
with foot strike angles obtained from the lab-based system
(one-way ANOVA, p<0.001) (Fig. 3).
Fig. 3. MilestonePod_Footstrike (scored 1 to 3, and
defined by the manufacturer as heel, mid, toe,
respectively) according to foot strike angle (FSA: midfoot
strike = -1.68° < FSA < 8.08°, rearfoot strike = FSA >
8.08°, and forefoot strike = FSA < -1.68°) obtained from
the lab-based system. Grey dots represent a discrepancy
between data from Milestone-Pod and the lab-based
system. One-way ANOVA, p<0.001.
RunScribe_Foostrike was not correlated to the foot strike
angle obtained from the lab-based system (r=0.090, p=0.688)
(Table 3).
D. Vertical Ground Reaction Force
Rate of impact classification from MilestonePod
(MilestonePod_Rate of impact [scored 1 to 3, and defined by
the manufacturer as low, medium and high rate of impact,
respectively]) could not be determined from the lab-based
system measures of AVLR and IVLR (one-way ANOVA,
both p>0.05) (Fig. 4).
RunScribe_Schock and RunScribe_Impact-Gs were not
correlated to either AVLR (r=-0.421, p=0.992 and r=-0.133,
p=0.767, respectively) or IVLR (r=-0.312, p=0.959 and r=-
0.106, p=0.720, respectively) obtained from the lab-based
system (Table 3).
A low correlation was found between Moov-
Now_Impact and IVLR (r=0.374, p=0.033), but not AVLR
(r=0.289, p=0.080) obtained from the lab-based system (Table
3).
TgForce_Z showed a moderate correlation to AVLR
(r=0.681, p<0.001) and a good correlation to IVLR (r=0.758,
p<0.001) obtained from the lab-based system (Fig. 5 and
Table 3).
E. Antero-posterior Ground Reaction Force
RunScribe_Braking-Gs was not significantly correlated
to the peak braking force obtained from the lab-based system
(r=-0.376, p=0.983) (Table 3).
Fig. 4. MilestonePod_Rate-of-impact (scored 1 to 3, and
defined by the manufacturer as low, medium and high rate
of impact, respectively) according to both A) average
vertical loading rate (AVLR) and B) instantaneous vertical
loading rate (IVLR) obtained from the lab-based system.
Grey dots represent a discrepancy between data from
Milestone-Pod and the lab-based system. One-way
ANOVA, p>0.05.
Fig. 5. Comparison of instantaneous vertical loading rate
(IVLR) from the lab-based system and TgForce_Z
(r=0.758, p<0.001). bw: body weight
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TgForce_X showed a low correlation (r=-0.470,
p=0.004) and TgForce_3D a moderate correlation (r=-0.532,
p=0.001) to peak braking force (Fig. 6 and Table 3).
Fig. 6. Peak braking force from the lab-based system according
to TgForce_3D (r=-0.532, p=0.001). bw: body weight
IV. DISCUSSION
The first aim of this study was to assess the concurrent
validity of several wearable sensors in measuring vertical
ground reaction force (vertical loading rates) during running
against a laboratory-based instrumented treadmill and motion
capture system. The second aim was to assess the concurrent
or criterion validity of the same wearable sensors at estimating
antero-posterior ground reaction force (peak braking force),
measuring step rate and vertical displacement of the center of
mass, and determining foot strike pattern. Five commercially
available wearable sensors were tested: MilestonePod, Moov
NowTM, TgForce, Zoi and RunScribeTM. Overall, measures
provided by the wearable sensors ranged from not correlated
to strongly correlated to the measurement from our lab-based
system, thereby partially confirming our hypotheses.
The five sensors provided different metrics that were
thought to represent vertical ground reaction forces. When
compared to actual vertical loading rates (average and
instantaneous), a gait parameter associated with running injury
and running injury and running injury prevention [8]–[10], the
correlations with these metrics ranged from none to high. This
confirmed our hypothesis that only devices attached to the
tibia (TgForce and Moov NowTM) showed significant
correlations with lab-based vertical loading rates. However,
TgForce was the only wearable sensor to be valid (r>0.70) in
estimating IVLR. In Hennig et al.’s study [30], peak tibial
acceleration was very strongly correlated to IVLR (r=0.98)
during running. The 3-gram accelerometer used in their study
was bone mounted on the distal medial end of the tibia. In
2003, Laughton et al. [31] showed significant correlations
between vertical loading rates and peak tibial acceleration
using a skin mounted accelerometer on the distal medial end
of the tibia (3.28 grams) during running. They also found an
influence of foot strike pattern with higher correlations in
forefoot strikers than rearfoot strikers for AVLR. More
recently, Ngoh et al. [32] validated the use of a neural network
TABLE 3
SUMMARY OF RESULTS FOR THE FIVE TESTED
WEARABLE SENSORS.
Lab-based Moov NowTM
S
tep rate
I
mp
a
ct
Step rate r=0.976, p<0.001
(
2.26±1.98
spm
)
AVLR
r=0.289,
p=0.080
IVLR
r=0.374,
p=0.033
Lab-based Milestone-Pod
S
tep r
ate
S
tep rate
r=
0.9
9
7, p<0.001
(
1.
59±1.44
spm
)
Lab-based
RunScribeTM
Step
r
ate
Shock Impact-
GS
Braking
-
GS
F
oot
st
r
ike
Step rate
r=0.999,
p<0.001
(1.12±0.
92
spm
)
AVLR
r= -0.421,
p
=0.992
r= -0.133,
p=0.767
IVLR
r= -0.312,
p=0.
959
r= -0.106,
p=0.720
PeakBraking
Force
r=-0.376,
p=0.983
Footstrike
Angle
r=0.090,
p=0.688
Lab-based Zoi
S
tep ra
te
Impact
Braking
Bouncing
Step rate
r=0.998,
p<0.001
(0.93±1.28
spm
)
AVLR
r=0.256,
p=0.086
IVLR
r=0.243,
p=0
.098
PeakBraking
Force
r= -0.202,
p=0.858
VertDisp
CM
r=0.813,
p<0.001
(
-
1
.
78
±
1.50
c
m
)
Lab-based TgForce
S
tep rate
Z
X
3
D
Step rate
r=0.955,
p<0.001
(4.5±6.13
spm
)
AVLR
r=0.681,
p<0.001
IVLR
r=0.758,
p<0.001
r=0.371,
p=0.020
PeakBraking
F
orce
r= -0.470,
p=0.004
r= -0.532,
p=0.001
Pearson’s correlation coefficient with the lab-based system, p-value, and
Bland-Altman bias and 95% limits of agreement for the five tested wearable
sensors. AVLR: average vertical loading rate; IVLR: instantaneous vertical
loading rate; spm: steps per minute.
model and accelerometer for estimating vertical ground
reaction force (cross-correlation coefficient > 0.99) during
running with a shoe mounted device in rearfoot strikers.
Location (e.g. shoe mounted), vibrations (attachment system
[e.g. shoelaces] and sensor weight [> 10g]) of the device and
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Journal
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foot strike pattern alter acceleration profile during running
[26], [31], [33]. These interferences may be compensated by
advanced data processing to provide valid estimates of ground
reaction forces [32] but might also explain the reported
differences between the different wearable sensors tested in
this study.
In agreement with our hypothesis, healthcare
professionals, coaches and runners can rely on step rate
measurements provided by all wearable sensors tested in this
study. Step rate is a common measure of running gait analysis.
It has been associated with both running injury and running
economy [12], [16]. Step rate calculation is based on foot
strike detection that is a simple measure to achieve with an
inertial measurement unit. Overall, wearable sensors slightly
over-estimated step rate on average by 2.08 with a mean error
(at 95%) of ±2.35 steps per minute (for a step rate measured at
170 by the lab-based system, wearables sensors will show on
average a step rate from 169.73 to 174.43 steps per minute).
For other running gait characteristics measured by the
wearable sensors (metrics related to antero-posterior ground
reaction force, vertical displacement of the center of mass and
foot strike pattern), concurrent or criterion validity strongly
depended of both the metrics and device.
To provide recommendations for potential buyers
(clinicians, coaches and runners), results for each wearable
sensor will be separately discussed below.
A. MilestonePod
MilestonePod is valid and accurate to measure step rate
during running. A potential important parameter to monitor
during running is the foot strike pattern as a non-rearfoot
strike has been associated with performance [17]–[19]. From
our results, this device seems to discriminate between a rear-,
mid- and forefoot strike pattern. Caution is warranted with this
result considering the low proportion of mid- and fore-foot
strikers in our sample (5 and 2 participants, respectively).
Further research is therefore required to confirm the validity of
this metrics. MilestonePod was not found to be valid in
estimating vertical loading rates. Therefore, potential buyers
may consider this sensor if their objective is to monitor step
rate and foot strike pattern only.
B. Moov NowTM
Moov NowT M is valid and accurate to measure step rate
during running. However, our results do not show that Moov
NowTM is valid to assess vertical ground reaction force during
running. Despite a tibial placement, it is possible that more
advanced signal processing is required to improve vertical
loading rate’s related metrics. Moov NowTM should be
considered if step rate is the only parameter to monitor.
C. TgForce
TgForce is valid to measure step rate during running
although this device is the least accurate of the five tested
sensors for this parameter. Interestingly, TgForce is the only
device providing metrics that were correlated to both vertical
and antero-posterior ground reaction forces. It uses
manufacturer proprietary algorithms with specific
combinations of data from accelerometers, gyroscopes, and
magnetometers. Unfortunately, the information about specific
models is proprietary and could not be shared with our
research team. TgForce is valid to estimate IVLR and the
coefficient of correlation was almost clinically meaningful for
AVLR (r=0.68). It is likely that both device location (distal
and medial tibial end) and attachment, and data processing
influence TgForce estimates of vertical loading rates during
running. However, the correlation coefficient for peak braking
force was too weak to consider TgForce valid for this metrics.
Between the five sensors tested in this study, potential buyers
aiming to monitor VLR should choose TgForce (TgForce_Z)
to get the best estimates (with a tight attachment).
D. Zoi
Zoi is valid and accurate to measure step rate and vertical
displacement of the center of mass during running. Vertical
displacement of the center of mass has been associated with
running economy [15] and may therefore be important to
monitor for runners seeking performance improvement. In
terms of ground reaction force, Zoi is not valid to estimate
vertical loading rates nor peak braking force. As discussed by
Wundersitz et al. [34], location of the device (chest) away
from the site of interest (foot contact) is likely to
introduce/amplify ground reaction force estimation errors. For
foot strike pattern, Zoi provides a percent of rear-, mid-, and
fore-foot strike during the run, however assessing the validity
of this measure with a statistical test was not feasible.
Potential buyers could consider Zoi if the objective is to
monitor step rate and vertical displacement of the center of
mass.
E. RunScribeTM
RunScribeTM is valid and accurate to measure step rate
during running. A recent study by Garcia-Pinillos et al. [35]
regarding the validity of RunScribeTM for measuring step rate
during running against a high-speed video analysis system
supports our results. Moreover, the authors found, in
agreement to our findings, that RunScribeTM slightly
overestimated the step rate. This sensor also provides a
measure of foot strike pattern on an interrupted scale from 0
(rearfoot strike) to 15 (forefoot strike). However, this metrics
is not correlated to the foot strike angle obtained from our lab-
based system. None of the metrics provided by RunScribeTM
(Impact Gs, Shock and Braking Gs) are valid to estimate both
vertical and antero-posterior ground reaction forces.
RunScribeTM should be considered by potential buyers only
when step rate is the variable of interest.
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
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F. Strengths and Limitations of the Study
The main strength of this work was to test the
concurrent/criterion validity of multiple commercially
available wearable sensors for runners in the same study, by
an independently funded research team (all wearable sensors
were either bought or borrowed and returned to
manufacturers). Moreover, this is the first independent study
to test the validity of vertical and antero-posterior ground
reaction forces during running against a lab-based system.
The wearable sensors tested in this study were not
designed for research purposes, making it impossible to
automatically synch them with our lab-based system.
Therefore, we had to synchronize systems manually. Since we
used average metrics from one minute of running, we are
confident that the synchronization error is very small.
In our sample, 5 participants were mid-foot strikers and 2
were fore-foot strikers according to our lab-based system. This
small number of non-rearfoot strikers was therefore a
limitation to determine the validity of the wearable sensors to
discriminate between a rear-, mid- and fore-foot strike pattern.
More research is therefore required with more non-rearfoot
strikers.
V. CONCLUSION
Wearable sensors are attractive products to gather in-
field data on running gait. Concurrent or criterion validity of
MilestonePod, Moov NowTM, TgForce, Zoi and RunScribeTM
was assessed in this study against a laboratory-based
instrumented treadmill and motion capture system. Estimation
of ground reaction force (vertical and antero-posterior),
measurement of step rate and vertical displacement of the
center of mass, and foot strike pattern detection during
running were analyzed. Overall, runners, clinicians and
coaches must keep in mind that only a limited number of the
metrics provided by these commercially available wearable
sensors are actually valid. Our results showed that step rate
measurement is valid for all wearable sensors. TgForce was
the only device providing valid metrics of instantaneous
vertical loading rate. Only MilestonePod seemed to be valid to
discriminate between a rear-, mid- and fore-foot strike pattern.
Only Zoi was valid to estimate vertical displacement of the
center of mass. None of the wearable sensors was valid for
estimating peak braking force during running. Potential buyers
should therefore be aware of such limitations of wearable
sensors for monitoring running gait and choose their wearable
sensor according to the metrics they want to monitor.
ACKNOWLEDGMENT
We would like to thank all runners who participated in this
study.
REFERENCES
[1] L. C. Hespanhol Junior, J. D. Pillay, W. van
Mechelen, and E. Verhagen, “Meta-Analyses of the
Effects of Habitual Running on Indices of Health in
Physically Inactive Adults,” Sports Med. Auckl. Nz,
vol. 45, no. 10, pp. 1455–1468, 2015.
[2] C. L. Hamill, I. E. Clarke, E. G. Frederick, L. J.
Goodyear, and E. T. Howley, “EFFECTS OF
GRADE RUNNING ON KINEMATICS AND
IMPACT FORCE,” Med. Sci. Sports Exerc., vol. 16,
no. 2, p. 184, Apr. 1984.
[3] B. C. Heiderscheit, “Gait retraining for runners: in
search of the ideal,” J. Orthop. Sports Phys. Ther.,
vol. 41, no. 12, pp. 909–910, Dec. 2011.
[4] D. E. Lieberman, “What we can learn about
running from barefoot running: an evolutionary
medical perspective,” Exerc. Sport Sci. Rev., vol.
40, no. 2, pp. 63–72, Apr. 2012.
[5] C. Napier, C. K. Cochrane, J. E. Taunton, and M.
A. Hunt, “Gait modifications to change lower
extremity gait biomechanics in runners: a
systematic review,” Br. J. Sports Med., vol. 49, no.
21, pp. 1382–1388, Nov. 2015.
[6] C. D. Bowersock, R. W. Willy, P. DeVita, and J. D.
Willson, “Reduced step length reduces knee joint
contact forces during running following anterior
cruciate ligament reconstruction but does not alter
inter-limb asymmetry,” Clin. Biomech. Bristol Avon,
vol. 43, pp. 79–85, Feb. 2017.
[7] I. S. Moore, “Is There an Economical Running
Technique? A Review of Modifiable Biomechanical
Factors Affecting Running Economy,” Sports Med.
Auckl. Nz, vol. 46, pp. 793–807, 2016.
[8] A. A. Zadpoor and A. A. Nikooyan, “The
relationship between lower-extremity stress
fractures and the ground reaction force: a
systematic review,” Clin. Biomech. Bristol Avon,
vol. 26, no. 1, pp. 23–28, Jan. 2011.
[9] H. van der Worp, J. W. Vrielink, and S. W.
Bredeweg, “Do runners who suffer injuries have
higher vertical ground reaction forces than those
who remain injury-free? A systematic review and
meta-analysis,” Br. J. Sports Med., vol. 50, no. 8,
pp. 450–457, Apr. 2016.
[10] Z. Y. S. Chan et al., “Gait Retraining for the
Reduction of Injury Occurrence in Novice Distance
Runners: 1-Year Follow-up of a Randomized
Controlled Trial,” Am. J. Sports Med., vol. 46, no.
2, pp. 388–395, Feb. 2018.
[11] C. Napier, C. L. MacLean, J. Maurer, J. E.
Taunton, and M. A. Hunt, “Kinetic risk factors of
running-related injuries in female recreational
runners,” Scand. J. Med. Sci. Sports, vol. 28, no.
10, pp. 2164–2172, Oct. 2018.
[12] L. E. Luedke, B. C. Heiderscheit, D. S. B. Williams,
and M. J. Rauh, “Influence of Step Rate on Shin
Injury and Anterior Knee Pain in High School
Runners,” Med. Sci. Sports Exerc., vol. 48, no. 7,
pp. 1244–1250, 2016.
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2020.2982568, IEEE Sensors
Journal
3 IEEE SENSORS JOURNAL, VOL. XX, NO. XX, MONTH X, XXXX
[13] A. I. Daoud, G. J. Geissler, F. Wang, J. Saretsky,
Y. A. Daoud, and D. E. Lieberman, “Foot strike and
injury rates in endurance runners: a retrospective
study.,” Med. Sci. Sports Exerc., vol. 44, no. 7, pp.
1325–1334, Jul. 2012.
[14] D. E. Lieberman, A. G. Warrener, J. Wang, and E.
R. Castillo, “Effects of stride frequency and foot
position at landing on braking force, hip torque,
impact peak force and the metabolic cost of
running in humans,” J. Exp. Biol., vol. 218, no. Pt
21, pp. 3406–3414, Nov. 2015.
[15] K. Halvorsen, M. Eriksson, and L. Gullstrand,
“Acute effects of reducing vertical displacement
and step frequency on running economy,” J.
Strength Cond. Res., vol. 26, no. 8, pp. 2065–
2070, Aug. 2012.
[16] T. J. Quinn, S. L. Dempsey, D. P. LaRoche, A. M.
Mackenzie, and S. B. Cook, “Step Frequency
Training Improves Running Economy in Well-
Trained Female Runners,” J. Strength Cond. Res.,
Jul. 2019.
[17] H. Hasegawa, T. Yamauchi, and W. J. Kraemer,
“Foot strike patterns of runners at the 15-km point
during an elite-level half marathon,” J. Strength
Cond. Res., vol. 21, no. 3, pp. 888–893, Aug.
2007.
[18] M. E. Kasmer, X.-C. Liu, K. G. Roberts, and J. M.
Valadao, “The Relationship of Foot Strike Pattern,
Shoe Type, and Performance in a 50-km Trail
Race,” J. Strength Cond. Res., vol. 30, no. 6, pp.
1633–1637, 2016.
[19] M. E. Kasmer, X.-C. Liu, K. G. Roberts, and J. M.
Valadao, “Foot-strike pattern and performance in a
marathon,” Int. J. Sports Physiol. Perform., vol. 8,
no. 3, pp. 286–292, May 2013.
[20] N. Chambon, N. Delattre, N. Guéguen, E. Berton,
and G. Rao, “Shoe drop has opposite influence on
running pattern when running overground or on a
treadmill,” Eur. J. Appl. Physiol., vol. 115, no. 5, pp.
911–918, May 2015.
[21] C. Napier, J.-F. Esculier, and M. A. Hunt, “Gait
retraining: out of the lab and onto the streets with
the benefit of wearables,” Br. J. Sports Med., vol.
51, no. 23, pp. 1642–1643, Dec. 2017.
[22] R. W. Willy, “Innovations and pitfalls in the use of
wearable devices in the prevention and
rehabilitation of running related injuries,” Phys.
Ther. Sport, vol. 29, no. Supplement C, pp. 26–33,
Jan. 2018.
[23] L. C. Benson, C. A. Clermont, E. Bošnjak, and R.
Ferber, “The use of wearable devices for walking
and running gait analysis outside of the lab: A
systematic review,” Gait Posture, vol. 63, pp. 124–
138, Jun. 2018.
[24] J. M. Peake, G. Kerr, and J. P. Sullivan, “A Critical
Review of Consumer Wearables, Mobile
Applications, and Equipment for Providing
Biofeedback, Monitoring Stress, and Sleep in
Physically Active Populations,” Front. Physiol., vol.
9, p. 743, 2018.
[25] A. G. Lucas-Cuevas, A. Encarnación-Martínez, A.
Camacho-García, S. Llana-Belloch, and P. Pérez-
Soriano, “The location of the tibial accelerometer
does influence impact acceleration parameters
during running,” J. Sports Sci., vol. 35, no. 17, pp.
1734–1738, Sep. 2017.
[26] K. R. Sheerin, D. Reid, and T. F. Besier, “The
measurement of tibial acceleration in runners—A
review of the factors that can affect tibial
acceleration during running and evidence-based
guidelines for its use,” Gait Posture, vol. 67, pp.
12–24, Jan. 2019.
[27] A. S. Tenforde, M. C. Ruder, S. T. Jamison, P. P.
Singh, and I. S. Davis, “Is symmetry of loading
improved for injured runners during novice barefoot
running?,” Gait Posture, vol. 62, pp. 317–320, Mar.
2018.
[28] A. R. Altman and I. S. Davis, “A kinematic method
for footstrike pattern detection in barefoot and shod
runners,” Gait Posture, vol. 35, no. 2, pp. 298–300,
Feb. 2012.
[29] M. M. Mukaka, “Statistics corner: A guide to
appropriate use of correlation coefficient in medical
research,” Malawi Med. J. J. Med. Assoc. Malawi,
vol. 24, no. 3, pp. 69–71, Sep. 2012.
[30] E. M. Hennig, T. L. Milani, and M. A. Lafortune,
“Use of Ground Reaction Force Parameters in
Predicting Peak Tibial Accelerations in Running,” J.
Appl. Biomech., vol. 9, no. 4, pp. 306–314, Nov.
1993.
[31] C. A. Laughton, I. M. Davis, and J. Hamill, “Effect
of Strike Pattern and Orthotic Intervention on Tibial
Shock during Running,” J. Appl. Biomech., vol. 19,
no. 2, pp. 153–168, May 2003.
[32] K. J.-H. Ngoh, D. Gouwanda, A. A. Gopalai, and Y.
Z. Chong, “Estimation of vertical ground reaction
force during running using neural network model
and uniaxial accelerometer,” J. Biomech., vol. 76,
pp. 269–273, Jul. 2018.
[33] M. Norris, R. Anderson, and I. C. Kenny, “Method
analysis of accelerometers and gyroscopes in
running gait: A systematic review,” Proc. Inst.
Mech. Eng. Part P J. Sports Eng. Technol., vol.
228, no. 1, pp. 3–15, Mar. 2014.
[34] D. W. T. Wundersitz, K. J. Netto, B. Aisbett, and P.
B. Gastin, “Validity of an upper-body-mounted
accelerometer to measure peak vertical and
resultant force during running and change-of-
direction tasks,” Sports Biomech., vol. 12, no. 4,
pp. 403–412, Nov. 2013.
[35] F. García-Pinillos et al., “Agreement between the
spatiotemporal gait parameters from two different
wearable devices and high-speed video analysis,”
PLoS ONE, vol. 14, no. 9, Sep. 2019.
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3 IEEE SENSORS JOURNAL, VOL. XX, NO. XX, MONTH X, XXXX
APPENDIX
Appendix 1: Specifications of the tested wearable sensors
Wearable sensor Device placement
(according to
manufacturer
instructions)
Power
supply
Start-stop Real-time
view
Smartphone
needed
during
running
?
Results view Data
exportation
Weight
(g)
Price
($US)
Moov NowTM
(v5.2.4809.252)
‘’outside of the ankle
and the loop end of
the band forward’’
CR 2032 Manual - Step rate
- Impact
Yes - Step rate (mean,
max)
- Impact (mean)
No 15.1 59.95
Milestone Pod
(v3.4.10)
Shoelaces
CR 2032 Auto No No - Foot strike pattern
(%heel, mid, toe)
- Step rate (mean,
max)
- Rate of impact
(%low, mid, high)
Yes
Score for each
minute:
- Step rate
- Rate of
impact (low,
mid, high)
- Foostrike
(heel, mid,
toe)
13.0 29.95
RunScribeTM
(v2.7.1)
Heel Mount
Rechargeable
battery
Auto /
Manual
- Impact Gs
- Braking Gs
- Footstrike
No - Step rate (mean)
- Shock
(mean)(combination
of impact and braking
score)
- Impact Gs (mean)
- Braking Gs (mean)
- Foot strike type
(mean)(from 0 [rear-
foot strike] to 15
[fore
-
foot strike])
Yes (score by
step)
15.0
(each
device)
249.00
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3 IEEE SENSORS JOURNAL, VOL. XX, NO. XX, MONTH X, XXXX
Zoi
Runteq (2012-
2016)
One pod on a chest
strap. One pod on the
shoelaces.
Rechargeable
battery
Manual - Step rate
(shoelaces)
- Impact
(chest)
- Bouncing
(chest)
- Braking
(
shoelaces
)
No - Step rate (mean)
- Braking (mean)
- Impact (mean)
- Bouncing (mean)
- Foot strike type
(%ball, middle, heel)
No 12.0
(each
device)
149.00
TgForce, Beta
version
(v2.0.0.10)
Medial end of tibia Rechargeable
battery
Manual,
Beta
version
- Step rate
- Max
vertical
acceleration
(Z)
- Max
transverse
acceleration
(Y)
- Max
sagittal
acceleration
(X)
- Max vector
acceleration
(3D)
Beta version - Step rate
- Max vertical
acceleration (Z)
- Max transverse
acceleration (Y)
- Max sagittal
acceleration (X)
- Max vector
acceleration (3D)
- by step 10.0 189.00
... RunScribe™ (Scribe Labs Inc., USA) is a wearable sensor that provides various spatiotemporal, kinematic and kinetic outcome metrics during running. Acceptable validity has been confirmed for contact time, pronation velocity, stride cycle time (Koldenhoven & Hertel, 2018) and step rate (De Fontenay et al., 2020), but not pronation excursion (Koldenhoven & Hertel, 2018), vertical loading rate and peak braking forces (De Fontenay et al., 2020). Note that the sensor was placed on the heel in the aforementioned studies, while it has also been shown that RunScribe provides a valid assessment of tibial acceleration if attached to the tibia (Brayne et al., 2018). ...
... RunScribe™ (Scribe Labs Inc., USA) is a wearable sensor that provides various spatiotemporal, kinematic and kinetic outcome metrics during running. Acceptable validity has been confirmed for contact time, pronation velocity, stride cycle time (Koldenhoven & Hertel, 2018) and step rate (De Fontenay et al., 2020), but not pronation excursion (Koldenhoven & Hertel, 2018), vertical loading rate and peak braking forces (De Fontenay et al., 2020). Note that the sensor was placed on the heel in the aforementioned studies, while it has also been shown that RunScribe provides a valid assessment of tibial acceleration if attached to the tibia (Brayne et al., 2018). ...
... Studies have shown that GRF can be reduced with biofeedback training methods (Crowell & Davis, 2011;Wood & Kipp, 2014) and by increasing the step rate (Schubert et al., 2014). RunScribe sensors could be useful to check if such reductions translate to outdoor conditions, however, the shock variables were reported to have poor validity (De Fontenay et al., 2020). Another study showed moderate correlations between GRF outcomes between gold standard methods and RunScribe (r = 0.40-0.62), ...
Article
The aim of this study was to investigate the reliability of running biomechanics assessment with a wearable commercial sensor (RunScribeTM). Participants performed multiple 200-m runs over sand, grass and asphalt ground at the estimated 5-km tempo, with an additional trial with 21-km tempo at the asphalt. Intra-session reliability was excellent for all variables at 5-km pace (intra-class coefficient correlation (ICC) asphalt: 0.90–0.99; macadam: 0.94–1.00; grass: 0.92–1.00), except for shock (good; ICC = 0.83), and contact time and total power output (moderate; ICC = 0.68–0.71). Coefficient of variation (CV) were mostly acceptable in all conditions, except for horizontal ground reaction force (GRF) rate in asphalt 5-km pace trial (CV = 24.5 %), power (CV = 14.3 %) and foot strike type (CV = 30.9 %) in 21-km pace trial, and horizontal GRF rate grass trial (CV = 15.7 %). Inter-session reliability was high or excellent for the majority of the outcomes (ICC≥0.85). Total power output (ICC = 0.56–0.65) and shock (ICC = 0.67–0.75) showed only moderate reliability across all conditions. Power (CV = 12.5–13.8 %), foot strike type (CV = 14.9–29.4 %) and horizontal ground reaction force rate (CV = 12.4–36.4 %) showed unacceptable CV.
... In terms of the population, it has been divided it into four categories, namely competitive runners (n = 111) [32,33,36,37,51,52], experienced runners (n = 28) [43,53], amateur runners (n = 200) [33,41,42,45,47,54,55] and nonrunners (n = 202) [12, 34, 35, 38-40, 44, 46, 48-50]. The most common IMU systems used were the Xsens system (n = 3) [12,34,53] and RunScribe ™ system (n = 3) [38,42,45]. Using two (n = 9) [32, 35, 40-42, 44, 45, 51, 54] or one (n = 8) [33,34,36,37,43,46,47,52] IMU was the most preferable, and some studies used five (n = 1) [49], seven (n = 3) [39,48,50], eight (n = 1) [50] or seventeen (n = 2) [12,53] IMUs. ...
... Using two (n = 9) [32, 35, 40-42, 44, 45, 51, 54] or one (n = 8) [33,34,36,37,43,46,47,52] IMU was the most preferable, and some studies used five (n = 1) [49], seven (n = 3) [39,48,50], eight (n = 1) [50] or seventeen (n = 2) [12,53] IMUs. In addition, studies installed IMUs in diverse sites, including dorsum of the foot [12, 32, 34, 38-40, 42, 44, 45, 49, 50, 53, 55], ankle [38,48,51,55], heel [38,47,55], shank [12,35,38,39,44,49,50,53], knee [48], thigh [12,35,39,50,53], hip [46,48], waist [36,37,43,53], sacrum [12,49,50], chest [38,41], sternum [12,52,53], back [33,39,41], upper arm [12,53], lower arm [12,53], hand [12,53], shoulder [12,53], head [12,53] and shoes midsole [54,55]. The most common sampling frequencies used in assessing running were 200 Hz (n = 6) [33,41,45,49,54,55] and 500 Hz [36,37,40,42,43] (n = 5; range: 50-1000 Hz). ...
... Using two (n = 9) [32, 35, 40-42, 44, 45, 51, 54] or one (n = 8) [33,34,36,37,43,46,47,52] IMU was the most preferable, and some studies used five (n = 1) [49], seven (n = 3) [39,48,50], eight (n = 1) [50] or seventeen (n = 2) [12,53] IMUs. In addition, studies installed IMUs in diverse sites, including dorsum of the foot [12, 32, 34, 38-40, 42, 44, 45, 49, 50, 53, 55], ankle [38,48,51,55], heel [38,47,55], shank [12,35,38,39,44,49,50,53], knee [48], thigh [12,35,39,50,53], hip [46,48], waist [36,37,43,53], sacrum [12,49,50], chest [38,41], sternum [12,52,53], back [33,39,41], upper arm [12,53], lower arm [12,53], hand [12,53], shoulder [12,53], head [12,53] and shoes midsole [54,55]. The most common sampling frequencies used in assessing running were 200 Hz (n = 6) [33,41,45,49,54,55] and 500 Hz [36,37,40,42,43] (n = 5; range: 50-1000 Hz). ...
Article
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Background Inertial measurement units (IMUs) are useful in monitoring running and alerting running-related injuries in various sports settings. However, the quantitative summaries of the validity and reliability of the measurements from IMUs during running are still lacking. The purpose of this review was to investigate the concurrent validity and test–retest reliability of IMUs for measuring gait spatiotemporal outcomes and lower extremity kinematics of health adults during running. Methods PubMed, CINAHL, Embase, Scopus and Web of Science electronic databases were searched from inception until September 2021. The inclusion criteria were as follows: (1) evaluated the validity or reliability of measurements from IMUs, (2) measured specific kinematic outcomes, (3) compared measurements using IMUs with those obtained using reference systems, (4) collected data during running, (5) assessed human beings and (6) were published in English. Eligible articles were reviewed using a modified quality assessment. A meta-analysis was performed to assess the pooled correlation coefficients of validity and reliability. Results Twenty-five articles were included in the systematic review, and data from 12 were pooled for meta-analysis. The methodological quality of studies ranged from low to moderate. Concurrent validity is excellent for stride length (intraclass correlation coefficient (ICC) (95% confidence interval (CI)) = 0.937 (0.859, 0.972), p < 0.001), step frequency (ICC (95% CI) = 0.926 (0.896, 0.948), r (95% CI) = 0.989 (0.957, 0.997), p < 0.001) and ankle angle in the sagittal plane ( r (95% CI) = 0.939 (0.544, 0.993), p = 0.002), moderate to excellent for stance time (ICC (95% CI) = 0.664 (0.354, 0.845), r (95% CI) = 0.811 (0.701, 0.881), p < 0.001) and good for running speed (ICC (95% CI) = 0.848 (0.523, 0.958), p = 0.0003). The summary Fisher's Z value of flight time was not statistically significant ( p = 0.13). Similarly, the stance time showed excellent test–retest reliability (ICC (95% CI) = 0.954 (0.903, 0.978), p < 0.001) and step frequency showed good test–retest reliability (ICC (95% CI) = 0.896 (0.837, 0.933), p < 0.001). Conclusions Findings in the current review support IMUs measurement of running gait spatiotemporal parameters, but IMUs measurement of running kinematics on lower extremity joints needs to be reported with caution in healthy adults. Trial Registration : PROSPERO Registration Number: CRD42021279395.
... After this assessment, it is possible to establish the threshold of impact and cadence that will be used in the programs. Both parameters will be acquired with the accelerometer Tgforce (v2.0.0.10) [20,41]. Also, these parameters will be collected immediately and six-months after the protocol to see how the participants have adapted to the proposed running-pattern. ...
... The accelerometer Tgforce (v2.0.0.10) will be taped to the anteromedial aspect of the subject's distal tibia [20]. The study of Pairot de Fontenay and colleagues [41] assessed the concurrent validity of popular, commercially available wearable sensors in estimating vertical ground reaction force during running, and found that the Tgforce was the only wearable sensor to be valid in estimating instantaneous vertical loading rate. A screen positioned in front of the treadmill will show a graph of the acceleration of the tibia in real time captured by the accelerometer. ...
... The use of other strategies such as a mirror in front of the treadmill [51], metronomes [44,45] and accelerometers [41] are recommended to diminish costs, however, do not resolve the time and applicability problem if do not made in an partially supervised format and outside the lab. The protocol that will be performed by group A requires a wearable device that can be given to the participant and pre-adjusted by the therapist. ...
Article
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Patellofemoral pain (PFP) is one of the most prevalent injuries in runners. Unfortunately, a substantial part of injured athletes do not recover fully from PFP in the long-term. Although previous studies have shown positive effects of gait retraining in this condition, retraining protocols often lack clinical applicability because they are time-consuming, costly for patients and require a treadmill. The primary objective of this study will be to compare the effects of two different two-week partially supervised gait retraining programs, with a control intervention; on pain, function and lower limb kinematics of runners with PFP. It will be a single-blind randomized clinical trial with six-month follow-up. The study will be composed of three groups: a group focusing on impact (group A), a group focusing on cadence (group B), and a control group that will not perform any intervention (group C). The primary outcome measure will be pain assessed using the Visual Analog Pain scale during running. Secondary outcomes will include pain during daily activities (usual), symptoms assessed using the Patellofemoral Disorders Scale and lower limb running kinematics in the frontal (contralateral pelvic drop; hip adduction) and sagittal planes (foot inclination; tibia inclination; ankle dorsiflexion; knee flexion) assessed using the MyoResearch 3.14—MyoVideo (Noraxon U.S.A. Inc.). The study outcomes will be evaluated before (t0), immediately after (t2), and six months (t24) after starting the protocol. Our hypothesis is that both partially supervised gait retraining programs will be more effective in reducing pain, improving symptoms, and modifying lower limb kinematics during running compared with the control group, and that the positive effects from these programs will persist for six months. Also, we believe that one gait retraining group will not be superior to the other. Results from this study will help improve care in runners with PFP, while maximizing clinical applicability as well as time and cost-effectiveness.
... Distally-placed accelerometers may also more closely represent the accelerations experienced by the foot/ankle (Sheerin et al., 2019). However, positive associations between impact loading (average vertical loading rate; AVLR) and PPA of shoe-mounted IMUs have been poor, especially when attached to the heel of the shoe (Cheung et al., 2019;Pairot de Fontenay et al., 2020). ...
... with the measurement of tibial PPA across a range of running speeds (Brayne et al., 2018). The RunScribe sensor mounted on the heel is not a valid surrogate for either the average or instantaneous vertical loading rates (Pairot de Fontenay et al., 2020), indicating that choice of sensor location can have important implications. However, current guidelines from RunScribe recommend placing it on the dorsum of the shoe, where it clips into a cradle that is securely mounted to the shoelaces. ...
... The relationship between AVLR and PPA from the shoemounted IMUs (IMeasureU-Shoe: r = 0.49 overall; RunScribe Impact: r = 0.55 overall; RunScribe Shock: r = 0.47 overall) was not only significant, but higher than in previous studies that fixed the IMU to the heel instead of the laces (Cheung et al., 2019;Pairot de Fontenay et al., 2020). This suggests that the lace-mount might be a better location to act as a proxy for GRF loading rates when compared with the heel. ...
Article
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INTRODUCTION: Most running-related injuries are believed to be caused by abrupt changes in training load, compounded by biomechanical movement patterns. Wearable technology has made it possible for runners to quantify biomechanical loads (e.g. peak positive acceleration; PPA) using commercially available inertial measurement units (IMUs). However, few devices have established criterion validity. The aim of this study was to assess the validity of two commercially available IMUs during running. Secondary aims were to determine the effect of footwear, running speed, and IMU location on PPA. MATERIALS AND METHODS: Healthy runners underwent a biomechanical running analysis on an instrumented treadmill. Participants ran at their preferred speed in three footwear conditions (neutral, minimalist, maximalist), and at three speeds (preferred, +10%, -10%) in the neutral running shoes. Four IMUs were affixed at the distal tibia (IMeasureU-Tibia), shoelaces (RunScribe and IMeasureU-Shoe), and insole (Plantiga) of the right shoe. Pearson correlations were calculated for average vertical loading rate (AVLR) and PPA at each IMU location. RESULTS: The AVLR had a high positive association with PPA (IMeasureU-Tibia) in the neutral and maximalist (r = 0.70 to 0.72; p  0.001) shoes and in all running speed conditions (r = 0.71 to 0.83; p  0.001), but low positive association in the minimalist (r = 0.47; p < 0.05) footwear condition. Conversely, the relationship between AVLR and PPA (Plantiga) was high in the minimalist (r = 0.75; p  0.001) condition and moderate in the neutral (r = 0.50; p < 0.05) and maximalist (r = 0.57; p < 0.01) footwear. The RunScribe metrics demonstrated low to moderate positive associations (r = 0.40 to 0.62; p < 0.05) with AVLR across most footwear and speed conditions. DISCUSSION: Our findings indicate that the commercially available Plantiga IMU is comparable to a tibia-mounted IMU when acting as a surrogate for AVLR. However, these results vary between different levels of footwear and running speeds. The shoe-mounted RunScribe IMU exhibited slightly lower positive associations with AVLR. In general, the relationship with AVLR improved for the RunScribe sensor at slower speeds and improved for the Plantiga and tibia-mounted IMeasureU sensors at faster speeds.
... As soon as it is the users themselves who equip themselves to monitor their state of health, outside the medical devices, we leave the traditional framework of medical practice. These measurements carried out outside of a supervision raise several series of concerns especially as wearables are becoming more and more efficient in collecting, with increased accuracy, a number of biomechanical parameters (foot strike pattern, stride length, step rate, etc.) [102] through which to quantify gait and from which applications can derive meaning. These advances in technology make the data produced sometimes as sensitive as medical data. ...
Thesis
With the development of the Internet of Things (IoT), smartphones and sensors are now able to provide information about the user's activity and even their physiology. This has led to a growing interest from the scientific community, particularly in the field of e-health, with applications in the monitoring of patients undergoing rehabilitation in order to offer more personalised follow-up. However, in addition to guiding the rehabilitation process, the generation and transmission of IoT data is also vulnerable to privacy breaches. Indeed, the complex processing chain of the IoT application in healthcare multiplies the risk of privacy threats throughout the life cycle of IoT data, including collection, transmission and storage, by an adversary who can retrieve the data and re-identify or reveal sensitive patient information. This thesis focuses on the following questions: Is the data collected sufficiently protected so that no one can misuse it to re-identify the owner or infer sensitive information? Is the protected data still accurate enough for healthcare applications such as rehabilitation? Achieving balance between data utility and privacy protection is an important challenge that we explore in this thesis from different angles. More specifically, the first part focuses on the problem of data anonymisation through minimisation, while the second part focuses on preventing the inference of sensitive attributes through a Generative Adversarial Network to sanitise sensor data and an approach exploiting private layers in Federated Learning.
... Impact loading (measured using GRF) and peak accelerations from shoe-mounted sensors have typically demonstrated lowto-moderate associations (Cheung et al., 2019;de Pairot, 2020;, but our recent validation study found moderate-to-high associations between impact loading and peak accelerations measured with an insole-embedded IMU . While poor fixation can lead to increased noise from lace-or wheel-mounted sensors, the location of an insole-embedded sensor has the advantage of being easily and consistently fixated. ...
Article
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Introduction Running-related injuries (RRIs) occur from a combination of training load errors and aberrant biomechanics. Impact loading, measured by peak acceleration, is an important measure of running biomechanics that is related to RRI. Foot strike patterns may moderate the magnitude of impact load in runners. The effect of foot strike pattern on peak acceleration has been measured using tibia-mounted inertial measurement units (IMUs), but not commercially available insole-embedded IMUs. The aim of this study was to compare the peak acceleration signal associated with rearfoot (RFS), midfoot (MFS), and forefoot (FFS) strike patterns when measured with an insole-embedded IMU. Materials and Methods Healthy runners ran on a treadmill for 1 min at three different speeds with their habitual foot strike pattern. An insole-embedded IMU was placed inside standardized neutral cushioned shoes to measure the peak resultant, vertical, and anteroposterior accelerations at impact. The Foot strike pattern was determined by two experienced observers and evaluated using high-speed video. Linear effect mixed-effect models were used to quantify the relationship between foot strike pattern and peak resultant, vertical, and anteroposterior acceleration. Results A total of 81% of the 187 participants exhibited an RFS pattern. An RFS pattern was associated with a higher peak resultant (0.29 SDs; p = 0.029) and vertical (1.19 SD; p < 0.001) acceleration when compared with an FFS running pattern, when controlling for speed and limb, respectively. However, an MFS was associated with the highest peak accelerations in the resultant direction (0.91 SD vs. FFS; p = 0.002 and 0.17 SD vs. RFS; p = 0.091). An FFS pattern was associated with the lowest peak accelerations in both the resultant and vertical directions. An RFS was also associated with a significantly greater peak acceleration in the anteroposterior direction (0.28 SD; p = 0.033) than an FFS pattern, while there was no difference between MFS and FFS patterns. Conclusion Our findings indicate that runners should be grouped by RFS, MFS, and FFS when comparing peak acceleration, rather than the common practice of grouping MFS and FFS together as non-RFS runners. Future studies should aim to determine the risk of RRI associated with peak accelerations from an insole-embedded IMU to understand whether the small observed differences in this study are clinically meaningful.
Article
Purpose: Force plates can be used to monitor landing asymmetries during rehabilitation, but they are not widely available. Accelerometer-based wearable technology may be a more feasible solution. The purpose of this article was to determine the agreement between impact accelerations measured with force plates and accelerometer-derived measures of (1) centre of mass (COM) acceleration and (2) tibial acceleration asymmetries during bilateral landings. Method: Participants completed three countermovement jumps (CMJ) and three squat jumps (SJ) on dual force plates with triaxial accelerometers attached to each tibia and lower back, near the COM. Bland and Altman 95% limits of agreement (95% LOA) were calculated. Results: 19 adults ( n = 11; 58% women, n = 8; 42% men) participated in the study. The mean differences between impact and COM accelerations were 0.24g (95% LOA; –1.34 g to 1.82 g) and 0.38 g (95% LOA; −1.15 g to 1.91 g) for the CMJ and SJ, respectively. The mean differences between the impact and tibial acceleration-based lower limb asymmetries in the CMJ and SJ were −6% (95% LOA; −32% to 19%) and 0% (95% LOA; −45% to 45%), respectively. Conclusions: Our findings show acceptable agreement between impact acceleration and accelerometer-based COM acceleration and lack of agreement between impact accelerations and accelerometer-based tibial acceleration asymmetries. COM acceleration could be used to quantify landing impacts during rehabilitation, but we do not consider the accelerometer-based asymmetry measures to be a suitable alternative for force plate-based measures. Future work should focus on determining normative values for lower extremity asymmetries during landing tasks.
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This study aimed to validate an array-based inertial measurement unit to measure cricket fast bowling kinematics as a first step in assessing feasibility for tele-sport-and-exercise medicine. We concurrently captured shoulder girdle relative to the pelvis, trunk lateral flexion, and knee flexion angles at front foot contact of eight cricket medium-fast bowlers using inertial measurement unit and optical motion capture. We used one sample t-tests and 95% limits of agreement (LOA) to determine the mean difference between the two systems and Smallest Worth-while Change statistic to determine whether any differences were meaningful. A statistically significant ( p < 0.001) but small mean difference of −4.7° ± 8.6° (95% Confidence Interval (CI) [−3.1° to −6.4°], LOA [−22.2 to 12.7], SWC 3.9°) in shoulder girdle relative to the pelvis angle was found between the systems. There were no statistically significant differences between the two systems in trunk lateral flexion and knee flexion with the mean differences being 0.1° ± 10.8° (95% CI [−1.9° to 2.2°], LOA [−22.5 to 22.7], SWC 1.2°) and 1.6° ± 10.1° (95% CI [−0.2° to 3.3°], LOA [−19.2 to 22.3], SWC 1.9°) respectively. The inertial measurement unit-based system tested allows for accurate measurement of specific cricket fast bowling kinematics and could be used in determining injury risk in the context of tele-sport-and-exercise-medicine.
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Inertial measurement units (IMUs) can be used to monitor running biomechanics in real-world settings, but IMUs are often used within a laboratory. The purpose of this scoping review was to describe how IMUs are used to record running biomechanics in both laboratory and real-world conditions. We included peer-reviewed journal articles that used IMUs to assess gait quality during running. We extracted data on running conditions (indoor/outdoor, surface, speed, and distance), device type and location, metrics, participants, and purpose and study design. A total of 231 studies were included. Most (72%) studies were conducted indoors; and in 67% of all studies, the analyzed distance was only one step or stride or <200 m. The most common device type and location combination was a triaxial accelerometer on the shank (18% of device and location combinations). The most common analyzed metric was vertical/axial magnitude, which was reported in 64% of all studies. Most studies (56%) included recreational runners. For the past 20 years, studies using IMUs to record running biomechanics have mainly been conducted indoors, on a treadmill, at prescribed speeds, and over small distances. We suggest that future studies should move out of the lab to less controlled and more real-world environments.
Conference Paper
Running gait assessment for shoe type recommendation to avoid injury often takes place within commercial premises. That is not representative of a natural running environment and may influence normal/usual running characteristics. Typically, assessments are costly and performed by an untrained biomechanist or physiotherapist. Thus, use of a low-cost assessment of running gait to recommend shoe type is warranted. Indeed, the recent impact of COVID has heightened the need for a shift toward remote assessment in general due to social-distancing guidelines and restriction of movement to bespoke assessment facilities. Mymo is a Bluetooth-enabled, inertial measurement unit (IMU) wearable worn on the foot. The wearable transmits inertial data via a smartphone application to the Cloud, where algorithms work to recommend a running shoe based upon the users/runner's pronation and foot-strike location/pattern. Here, an additional algorithm is presented to quantify ground contact time and swing/flight time within the Mymo platform to further inform the assessment of a runner's gait. A large cohort of healthy adult and adolescents (n=203, 91M:112F) were recruited to run on a treadmill while wearing the Mymo wearable. Validity of the inertial-based algorithm to quantify ground contact time was established through manual labelling of reference standard ground truth video data, with a presented accuracy between 96.6-98.7% across the two classes with respect to each foot.Clinical Relevance-This establishes the validity of a ground contact and swing times for runner with a low-cost IoT wearable.
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This study aimed to evaluate the concurrent validity of two different inertial measurement units for measuring spatiotemporal parameters during running on a treadmill, by comparing data with a high-speed video analysis (VA) at 1,000 Hz. Forty-nine endurance runners performed a running protocol on a treadmill at comfortable velocity (i.e., 3.25 ± 0.36 m.s-1). Those wearable devices (i.e., Stryd™ and RunScribe™ systems) were compared to a high-speed VA, as a reference system for measuring spatiotemporal parameters (i.e. contact time [CT], flight time [FT], step frequency [SF] and step length [SL]) during running at comfortable velocity. The pairwise comparison revealed that the Stryd™ system underestimated CT (5.2%, p < 0.001) and overestimated FT (15.1%, p < 0.001) compared to the VA; whereas the RunScribe™ system underestimated CT (2.3%, p = 0.009). No significant differences were observed in SF and SL between the wearable devices and VA. The intra class correlation coefficient (ICC) revealed an almost perfect association between both systems and high-speed VA (ICC > 0.81). The Bland-Altman plots revealed heteroscedasticity of error (r2 = 0.166) for the CT from the Stryd™ system, whereas no heteroscedasticity of error (r2 < 0.1) was revealed in the rest of parameters. In conclusion, the results obtained suggest that both foot pods are valid tools for measuring spatiotemporal parameters during running on a treadmill at comfortable velocity. If the limits of agreement of both systems are considered in respect to high-speed VA, the RunScribe™ seems to be a more accurate system for measuring temporal parameters and SL than the Stryd™ system.
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Background: Impact loading in runners, assessed by the measurement of tibial acceleration, has attracted substantial research attention. Due to potential injury links, particularly tibial fatigue fractures, tibial acceleration is also used as a clinical monitoring metric. There are contributing factors and potential limitations that must be considered before widespread implementation. Aim: The objective of this review is to update current knowledge of the measurement of tibial acceleration in runners and to provide recommendations for those intending on using this measurement device in research or clinical practice. Methods: Literature relating to the measurement of tibial acceleration in steady-state running was searched. A narrative approach synthesised the information from papers written in English. A range of literature was identified documenting the selection and placement of accelerometers, the analysis of data, and the effects of intrinsic and extrinsic factors. Results and discussion: Tibial acceleration is a proxy measurement for the impact forces experienced at the tibia commonly used by clinicians and researchers. There is an assumption that this measure is related to bone stress and strain, however this is yet to be proven. Multi-axis devices should be secured firmly to the tibia to limit movement relative to the underlying bone and enable quantification of all components of acceleration. Additional frequency analyses could be useful to provide a more thorough characterisation of the signal. Conclusions: Tibial accelerations are clearly affected by running technique, running velocity, lower extremity stiffness, as well as surface and footwear compliance. The interrelationships between muscle pre-activation and fatigue, stiffness, effective mass and tibial acceleration still require further investigation, as well as how changes in these variables impact on injury risk.
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Background: The increasing popularity of distance running has been accompanied by an increase in running-related injuries, such that up to 85% of novice runners incur an injury in a given year. Previous studies have used a gait retraining program to successfully lower impact loading, which has been associated with many running ailments. However, softer footfalls may not necessarily prevent running injury. Purpose: To examine vertical loading rates before and after a gait retraining program and assess the effectiveness of the program in reducing the occurrence of running-related injury across a 12-month observation period. Study design: Randomized controlled trial; Level of evidence, 1. Methods: A total of 320 novice runners from the local running club completed this study. All the participants underwent a baseline running biomechanics evaluation on an instrumented treadmill with their usual running shoes at 8 and 12 km/h. Participants were then randomly assigned to either the gait retraining group or the control group. In the gait retraining group (n = 166), participants received 2 weeks of gait retraining with real-time visual feedback. In the control group (n = 154), participants received treadmill running exercise but without visual feedback on their performance. The training time was identical between the 2 groups. Participants' running mechanics were reassessed after the training, and their 12-month posttraining injury profiles were tracked by use of an online surveillance platform. Results: A significant reduction was found in the vertical loading rates at both testing speeds in the gait retraining group ( P < .001, Cohen's d > 0.99), whereas the loading rates were either similar or slightly increased in the control group after training ( P = .001 to 0.461, Cohen's d = 0.03 to -0.14). At 12-month follow-up, the occurrence of running-related musculoskeletal injury was 16% and 38% in the gait retraining and control groups, respectively. The hazard ratio between gait retraining and control groups was 0.38 (95% CI, 0.25-0.59), indicating a 62% lower injury risk in gait-retrained runners compared with controls. Conclusion: A 2-week gait retraining program is effective in lowering impact loading in novice runners. More important, the occurrence of injury is 62% lower after 2 weeks of running gait modification. Registration: HKUCTR-1996 (University of Hong Kong Clinical Trials Registry).
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Quinn, TJ, Dempsey, SL, LaRoche, DP, Mackenzie, AM, and Cook, SB. Step frequency training improves running economy in well-trained female runners. J Strength Cond Res XX(X): 000-000, 2019-The purpose was to determine whether a short training program (15 minutes for 10 days) to increase step frequency to 180 steps per min would elicit improvements in running economy (RE). Experimental (n = 11) and control (n = 11) female subjects reported to the laboratory for 12 consecutive days and completed 2 RE tests at 3.4 and 3.8 m·s (day 1 and 12), followed by a maximal oxygen uptake test (day 1 only), and experimental subjects completed a 10-day training program to increase step frequency (days 2-11). Control subjects completed the same runs without step frequency training. The training program consisted of running at 180 steps per minutes for 15 minutes at a self-selected velocity. A repeated-measures multivariate analysis of variance was used to test for differences. Oxygen consumption was significantly lower at each testing velocity for experimental but not control after the 10-day training program. The average drop in oxygen consumption across both speeds was approximately 11.0% (p < 0.05; mean ηp = 0.28). These lower oxygen consumptions were achieved at greater (7.0%) self-selected step frequencies (p < 0.01; mean ηp = 0.78), shorter (3.7%) step lengths (p < 0.05; mean ηp = 0.74), and lower (5.1%) heart rates (p < 0.05; mean ηp = 0.31) for experimental but not control. Training to run at a faster step cadence may be a viable technique to improve RE.
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Wearable technology has been viewed as one of the plausible alternatives to capture human motion in an unconstrained environment, especially during running. However, existing methods require kinematic and kinetic measurements of human body segments and can be complicated. This paper investigates the use of neural network model (NN) and accelerometer to estimate vertical ground reaction force (VGRF). An experimental study was conducted to collect sufficient samples for training, validation and testing. The estimated results were compared with VGRF measured using an instrumented treadmill. The estimates yielded an average root mean square error of less than 0.017 of the body weight (BW) and a cross-correlation coefficient greater than 0.99. The results also demonstrated that NN could estimate impact force and active force with average errors ranging between 0.10 and 0.18 of BW at different running speeds. Using NN and uniaxial accelerometer can (1) simplify the estimation of VGRF, (2) reduce the computational requirement and (3) reduce the necessity of multiple wearable sensors to obtain relevant parameters.
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Our objective was to prospectively investigate the association of kinetic variables with running‐related injury (RRI) risk. Seventy‐four healthy female recreational runners ran on an instrumented treadmill while 3D kinetic and kinematic data were collected. Kinetic outcomes were vertical impact transient, average vertical loading rate, instantaneous vertical loading rate, active peak, vertical impulse, and peak braking force (PBF). Participants followed a 15‐week half‐marathon training program. Exposure time (hours of running) was calculated from start of program until onset of injury, loss to follow‐up, or end of program. After converting kinetic variables from continuous to ordinal variables based on tertiles, Cox proportional hazard models with competing risks were fit for each variable independently, before analysis in a forward stepwise multivariable model. Sixty‐five participants were included in the final analysis, with a 33.8% injury rate. PBF was the only kinetic variable that was a significant predictor of RRI. Runners in the highest tertile (PBF <‐0.27 BW) were injured at 5.08 times the rate of those in the middle tertile and 7.98 times the rate of those in the lowest tertile. When analyzed in the multivariable model, no kinetic variables made a significant contribution to predicting injury beyond what had already been accounted for by PBF alone. Findings from this study suggest PBF is associated with a significantly higher injury hazard ratio in female recreational runners and should be considered as a target for gait retraining interventions. This article is protected by copyright. All rights reserved.
Article
Background: Quantitative gait analysis is essential for evaluating walking and running patterns for markers of pathology, injury, or other gait characteristics. It is expected that the portability, affordability, and applicability of wearable devices to many different populations will have contributed advancements in understanding the real-world gait patterns of walkers and runners. Therefore, the purpose of this systematic review was to identify how wearable devices are being used for gait analysis in out-of-lab settings. Methods: A systematic search was conducted in the following scientific databases: PubMed, Medline, CINAHL, EMBASE, and SportDiscus. Each of the included articles was assessed using a custom quality assessment. Information was extracted from each included article regarding the participants, protocol, sensor(s), and analysis. Results: A total of 61 articles were reviewed: 47 involved gait analysis during walking, 13 involved gait analysis during running, and one involved both walking and running. Most studies performed adequately on measures of reporting, and external and internal validity, but did not provide a sufficient description of power. Small, unobtrusive wearable devices have been used in retrospective studies, producing unique measures of gait quality. Walking, but not running, studies have begun to use wearable devices for gait analysis among large numbers of participants in their natural environment. Conclusions: Despite the advantages provided by the portability and accessibility of wearable devices, more studies monitoring gait over long periods of time, among large numbers of participants, and in natural walking and running environments are needed to analyze real-world gait patterns, and would facilitate prospective, subject-specific, and subgroup investigations. The development of wearables-specific metrics for gait analysis provide insights regarding the quality of gait that cannot be determined using traditional components of in-lab gait analyses. However, guidelines for the usability of wearable devices and the validity of wearables-based measurements of gait quality need to be established.