sensors
Article
Validity and Reliability of an Instrumented Treadmill with an
Accelerometry System for Assessment of Spatio-Temporal
Parameters and Impact Transmission
Alberto Encarnación-Martínez 1,*
, Pedro Pérez-Soriano 1
, Roberto Sanchis-Sanchis 1,2
,
Antonio García-Gallart 3
and Rafael Berenguer-Vidal 4


Citation: Encarnación-Martínez, A.;
Pérez-Soriano, P.; Sanchis-Sanchis, R.;
García-Gallart, A.; Berenguer-Vidal, R.
Validity and Reliability of an
Instrumented Treadmill with an
Accelerometry System for
Assessment of Spatio-Temporal
Parameters and Impact Transmission.
Sensors 2021, 21, 1758. https://
doi.org/10.3390/s21051758
Academic Editor: Ernesto De
La Cruz-Sánchez
Received: 6 February 2021
Accepted: 27 February 2021
Published: 4 March 2021
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1
Research Group in Sports Biomechanics (GIBD), Department of Physical Education and Sports,
University of Valencia, 46010 Valencia, Spain; pedro.perez-soriano@uv.es (P.P.-S.);
roberto.sanchis@uv.es (R.S.-S.)
2
Physical Education and Sport, University of Alicante, 03690 San Vicente del Raspeig, Spain
3
The Civil Guard, Secretary of State for Security, Ministry of the Interior, 28010 Madrid, Spain;
garciagallart@gmail.com
4
Grupo de Investigación en Telecomunicaciones Avanzadas (GRITA), Catholic University of Murcia,
30107 Guadalupe, Spain; rberenguer@ucam.edu
*
Correspondence: alberto.encarnacion@uv.es
Abstract: Running retraining programs focused on concurrent feedback of acceleration impacts
have been demonstrated to be a good strategy to reduce running-related injuries (RRI), as well
as to improve running economy and reduce acceleration impacts and injury running incidence.
Traditionally, impacts have been registered by mean of accelerometers attached directly to the
athletes, which is inaccessible to the entire population, because it requires laboratory conditions. This
study investigated the validity and reliability of a new device integrated directly into the treadmill,
compared to a traditional acceleration impact system. Thirty healthy athletes with no history of RRI
were tested on two separate days over the instrumented treadmill (AccTrea) and simultaneously
with an acceleration impact system attached to the participant (AccAthl). AccTrea was demonstrated
to be a valid and reliable tool for measuring spatio-temporal parameters like step length (validity
intraclass correlation coefﬁcient (ICC) = 0.94; reliability ICC = 0.92), step time (validity ICC = 0.95;
reliability ICC = 0.96), and step frequency (validity ICC = 0.95; reliability ICC = 0.96) during running.
Peak acceleration impact variables showed a high reliability for the left (reliability ICC = 0.88) and
right leg (reliability ICC = 0.85), and peak impact asymmetry showed a modest validity (ICC = 0.55).
These results indicated that the AccTrea system is a valid and reliable way to assess spatio-temporal
variables, and a reliable tool for measuring acceleration impacts during running.
Keywords: impact acceleration; spatio-temporal; instrumented treadmill; running; retraining
1. Introduction
Running is one of the most popular recreational activities [1–3]. Its success may be
because it is an aerobic activity that improves health and longevity, prevents diseases, and
is very effective for getting ﬁt [1–3]. Against the numerous beneﬁts of running, injuries in
this activity have a high incidence as almost half of runners are injured every year [1]. The
annual incidence of lower-limbs injuries ranges from 19.4% to 79.3% [1], or even 92.4% [2] in
long-distance runners. Most injuries are caused by the overuse of certain structures, [1,2,4]
and the knee is the most common place of injury [2,4], ranging from 7.2% to 50% [2]. Thus,
injuries can lead to a temporary or permanent interruption of exercise and even inability to
work, leading to the need for medical treatment, where direct costs may exceed 1300 € [5].
Scientiﬁcally related to running injuries [6–8], an impact is generated with each foot
contact with the ﬂoor that produces stress up to 1.5 to 2.5 times the body weight [8], and
it is transmitted and absorbed by the whole body [9–11]. These impacts are attenuated
Sensors 2021, 21, 1758. https://doi.org/10.3390/s21051758
https://www.mdpi.com/journal/sensors
Sensors 2021, 21, 1758
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internally by passive structures such as bones, cartilage, and ligaments, and by active
movements such as joint angular displacements and eccentric muscle actions, in addition to
external components such as footwear or surfaces [11]. The impacts during running have
been broadly studied, and accelerometry is the technique most used to register this mechan-
ical stress in sports activities [6–8,11]. This technique is based on the placement of low-mass
accelerometers (uniaxial or triaxial), mostly on the tibia and front of the head to register in
“g” or gravities (1 g = 9.8 m/s2) the acceleration/deceleration of body segments to calculate
the magnitude and attenuation of impact [8–11]. It has been found that after a prolonged
running fatigue protocol, while tibial accelerations increase [6–8], head accelerations re-
main stable [7,8,11], which means that impact absorption also increases [6,8,12]. Therefore,
it is necessary to adopt measures to reduce these stress levels on the musculoskeletal system
during running and their negative effects [4].
The running surface, as an external component, plays a major role in impact atten-
uation [11,13]. Although overground running is the surface preferred for recreational
runners [14], running on a treadmill is a very popular activity in gyms, in therapeutic
activities, rehabilitation, training, or athletic performance testing [13,15]. It has been shown
that running on a treadmill can modify running biomechanics compared with overground
running [16]. These kinematic modiﬁcations during treadmill running favors a running
technique characterized by a higher level of security [17] as the magnitude of the impact
is lower [13,17] and the risk of stress injuries is lower on treadmills in comparison with
overground running [18]. Around 5000 impacts can occur during a typical 30 min running
practice [8]. Thus, an excessively high impact level, due to a poor running technique or a
reduction in attenuation ability as the fatigue progresses, has been related to an increased
risk of injury [6–8].
Despite the potential beneﬁts associated with running on a treadmill, the spatial and
sensory constraints imposed by treadmills alter temporal and neuromuscular control in
comparison with the overground condition [19]. Nevertheless, some research focused on
analyzing the effects of biofeedback or auditory or visual information on some modiﬁable
factors, such as running technique, that could reduce the severity of impacts [20–23]. These
authors showed that by providing visual [21] or auditory [22] information through a screen
about the impact levels received during treadmill running, athletes were able to make small
modiﬁcations in their running technique autonomously to lower the impact peak [21,22],
and their running technique became more efﬁcient or economical [20]. Therefore, the
implementation of biofeedback is an effective measure to reduce impacts and improve
running economy [20–22].
It is important to highlight that all the studies that analyze impacts during running
using accelerometry place the sensors directly on the athletes’ body, and the biofeedback
system is used as an external element to the instruments used to carry out the activity.
Similarly, to analyze spatio-temporal variables during running, other systems based on
contact platforms [24] or optoelectronic technology [25] have been previously used. How-
ever, these systems allow just a limited number of strides or require expensive technology,
making them inaccessible to the general population.
Some research works have used instrumented treadmills with force-plates or pressure
sensors that allow measurement of the pressure produced by the runner on the lower board
of the treadmill [26,27]. Force-plates present interesting advantages in motion and gait
analysis [28], although the substantial cost of this instrumentation reduces the possibility
of its use outside the laboratory on a large scale. On the other hand, despite the numerous
advantages of using accelerometers for impact analysis described above, as far as we
know to date [29–31], there are no treadmills that integrate acceleration sensors into their
own system.
Accelerometers are today a proven and low-cost technology used for displacement
estimation [32] in a wide range of applications, such as electrohydraulic systems [33],
architecture, civil engineering [34,35], seismology [36], or even astronomy [37]. In all these
applications, the accelerometers are rigidly attached to the element whose displacement is
Sensors 2021, 21, 1758
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to be monitored, and with an analysis of the accelerometry signals, the motion and other
parameters of interest can be estimated. For this reason, we proposed the placement of
accelerometry sensors directly on a treadmill, which will allow us to similarly estimate
the movement on the treadmill and thus analyze the movement of the runner when using
the treadmill.
Thus, our aims were: (a) To implement and validate an accelerometry system, placed
directly in the treadmill and integrated into the software; (b) to compare the impact and
space–time data during running obtained from the accelerometry system integrated in
the treadmill with the data extracted from the accelerometry system placed directly on
the athlete’s body. We hypothesize that: (a) The accelerometry system integrated in the
treadmill is a valid and reliable tool for measuring impacts and space–time parameters
during running; (b) the accelerometry system integrated in the treadmill offers similar data
to those provided by an accelerometry system placed directly on the athlete’s body.
2. Materials and Methods
2.1. Participants
This study was approved by the institution’s Human Research Ethics Committee
(registry number: 6775). Thirty recreational athletes, ten women and twenty men, were
recruited from local Athletics recreational teams, from March to April 2019, and were tested
twice. Both tests were completed within 2 weeks and at least 24 h apart. Inclusion criteria
were: To be physically active (to run a minimum of twice a week in the last year, do 2 h and
30 min a week of moderate-intensity, or 1 h and 15 min a week of vigorous-intensity aerobic
physical activity), to have no history of lower body injuries within the last six months,
to not be taking medication that hinders stability during the running, and to not suffer
musculoskeletal disorders, heart failure, or neurological disorders that could affect normal
locomotion. Athletes were excluded if they have had signiﬁcant illness, injury, or surgery
within the previous six months, and if they were overweight or obese (BMI < 24.9 kg/m2).
All participants provided informed consent before their inclusion in the study. The baseline
characteristics are shown in Table 1.
Table 1. Baseline characteristics of the thirty participants, values are means ± SD.
Characteristics (M ± SD)
Female (n = 10)
Male (n = 20)
Age, y
24.4 ± 6.1
27.2 ± 7.5
Weight, kg
55.8 ± 4.0
73.3 ± 8.0
Height, cm
161.3 ± 4.3
175.6 ± 5.1
BMI, kg/m2
21.4 ± 1.3
23.7 ± 2.3
M = mean, SD = standard deviation, BMI: Body mass index.
2.2. Experimental Setups
Acceleration impact data during running were recorded using a wireless triaxial
accelerometry system (AcelSystem, Blautic, Spain; dimensions: 40 mm × 22 mm × 12 mm)
adjusted to the athletes (AccAthl), at a sampling ratio of 415 Hz, a measuring range of up
to ±16 g, and a total mass of 2.5 g. Simultaneously, a system consisting of a set of four
triaxial MPU-9250 accelerometry sensors (TDK InvenSense, San José, CA, USA) embedded
in the treadmill (AccTrea) was used. These four accelerometers were set at a sampling
frequency of 250 Hz and with a range up to ±8 g, appropriate for the expected measurement
values [29–31].
For every participant, a lightweight triaxial accelerometer was placed on the distal
and anteromedial portion of each tibia with the vertical axis of each accelerometer aligned
to be parallel to the long axis of the shank [38], as the location of the tibial accelerometer
does inﬂuence the acceleration signal [38]. The skin was previously prepared and the
accelerometers were adjusted with elastic belts as recommended by Encarnación-Martínez,
García-Gallart, Gallardo, Sánchez-Sáez, and Sánchez-Sánchez [9] (Figure 1). The treadmill
Sensors 2021, 21, 1758
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accelerometry system was encased inside the treadmill (EVOT1, Bodytone International
Sports, Murcia, Spain), and comprised three parts: A group of triple-axis Micro Electro-
Mechanical System (MEMS) accelerometers, a data acquisition unit, and a processing unit.
Appendix A details the operation and connection between these constituent elements of
AccTrea. Both AccAthl and AccTrea systems were triggered simultaneously to collect the
impact acceleration data.
Sensors 2021, 21, x FOR PEER REVIEW 
4 of 17 
 
 
to be parallel to the long axis of the shank [38], as the location of the tibial accelerometer 
does influence the acceleration signal [38]. The skin was previously prepared and the ac-
celerometers were adjusted with elastic belts as recommended by Encarnación-Martínez, 
García-Gallart, Gallardo, Sánchez-Sáez, and Sánchez-Sánchez [9] (Figure 1). The treadmill 
accelerometry system was encased inside the treadmill (EVOT1, Bodytone International 
Sports, Murcia, Spain), and comprised three parts: A group of triple-axis Micro Electro-
Mechanical System (MEMS) accelerometers, a data acquisition unit, and a processing unit. 
Appendix A details the operation and connection between these constituent elements of 
AccTrea. Both AccAthl and AccTrea systems were triggered simultaneously to collect the 
impact acceleration data. 
 
Figure 1. Graphical schematic of the body-worn accelerometer fixed to athletes: (A) Accelerometry system (AcelSystem, 
Blautic); (B) accelerometer fixation on distal and anteromedial portion of each tibia; (C) final experimental setup; and (D) 
graphical representation of the accelerometer signal and the experimental setup. 
Participants performed two running tests on different days. The first session in-
tended to assess the validity of the accelerometry system implemented in the treadmill 
(AccTrea) versus an accelerometry system adjusted to the athletes (AccAthl), and the sec-
ond session intended to test the reliability. Both running accelerations’ measurement ses-
sions were undertaken in the biomechanics lab at the same environmental conditions and 
at similar times of the day. All participants used the heel–toe running style and wore their 
own running shoes (the same for all two tests). After the informed consent, participants 
performed a free 5 min warm-up until they were familiar with the testing treadmill con-
dition [39]. Next, the participants were instrumented with the accelerometers and the run-
ning tests were performed. They ran for 5 min at 10 km/h and 0% slope in order not to 
affect the parameters evaluated [40], and acceleration impacts and spatio-temporal pa-
rameters were collected by the AccTrea and the AccAthl systems in two sets of 10 s during 
the last minute taken in each measurement session. Rate of Perceived Exertion (RPE) [41] 
was also registered after the warm-up and after each of the running test. 
The vertical component (z-coordinate) of the accelerometry signals has been proven 
to be most important for the assessment of acceleration impacts and injury stroke inci-
dence [42]. Therefore, in both AccAthl and AccTrea, the vertical component of all accel-
erometers was gathered for analysis. 
Data from the AccAthl system were analyzed using the Matlab program (Math-
Works, MA, USA), custom-made. The accelerometers were previously calibrated by the 
manufacturer. The acceleration signal from each of the sensors was first filtered (Butter-
worth, second-order, low-pass, cut-off frequency = 50 Hz) [43]. The signal was then seg-
mented by calculating the signal period (using the autocorrelation) and locating the points 
of interest (maximum, minimum, etc.) for each step. The positive peak tibial acceleration 
was measured for each leg in g (1 g = 9.82 m/s2), as well as the asymmetry between the 
Figure 1. Graphical schematic of the body-worn accelerometer ﬁxed to athletes: (A) Accelerometry system (AcelSystem,
Blautic); (B) accelerometer ﬁxation on distal and anteromedial portion of each tibia; (C) ﬁnal experimental setup; and
(D) graphical representation of the accelerometer signal and the experimental setup.
Participants performed two running tests on different days. The ﬁrst session intended
to assess the validity of the accelerometry system implemented in the treadmill (AccTrea)
versus an accelerometry system adjusted to the athletes (AccAthl), and the second session
intended to test the reliability. Both running accelerations’ measurement sessions were un-
dertaken in the biomechanics lab at the same environmental conditions and at similar times
of the day. All participants used the heel–toe running style and wore their own running
shoes (the same for all two tests). After the informed consent, participants performed a
free 5 min warm-up until they were familiar with the testing treadmill condition [39]. Next,
the participants were instrumented with the accelerometers and the running tests were per-
formed. They ran for 5 min at 10 km/h and 0% slope in order not to affect the parameters
evaluated [40], and acceleration impacts and spatio-temporal parameters were collected by
the AccTrea and the AccAthl systems in two sets of 10 s during the last minute taken in
each measurement session. Rate of Perceived Exertion (RPE) [41] was also registered after
the warm-up and after each of the running test.
The vertical component (z-coordinate) of the accelerometry signals has been proven
to be most important for the assessment of acceleration impacts and injury stroke inci-
dence [42]. Therefore, in both AccAthl and AccTrea, the vertical component of all ac-
celerometers was gathered for analysis.
Data from the AccAthl system were analyzed using the Matlab program (MathWorks,
MA, USA), custom-made. The accelerometers were previously calibrated by the manu-
facturer. The acceleration signal from each of the sensors was ﬁrst ﬁltered (Butterworth,
second-order, low-pass, cut-off frequency = 50 Hz) [43]. The signal was then segmented
by calculating the signal period (using the autocorrelation) and locating the points of
interest (maximum, minimum, etc.) for each step. The positive peak tibial acceleration was
measured for each leg in g (1 g = 9.82 m/s2), as well as the asymmetry between the legs,
calculated as the relative difference between both peaks (right leg impact minus left leg
impact) expressed as a percentage (%).
On the other hand, as detailed in Appendix A, AccTrea incorporated four MPU-9250
sensors. According to the manufacturer’s speciﬁcations, these devices included a motion
processing unit with low-pass ﬁlters and an EEPROM for on-chip factory calibration of
Sensors 2021, 21, 1758
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the sensor. Thus, factory-trimmed scale factors eliminated the need for external active
components and end-user calibration. Nevertheless, a calibration routine was performed
at sensor initialization on the data acquisition unit to offset the bias of gravity [44].
The AccTrea system allowed us to measure the acceleration of the table of the treadmill
due to the runner impacts at each sensor position (see Figure A1). The difference in
amplitude and phase between the signals from the different sensors made it possible
to automatically detect the accelerations produced by each leg. Then, like AccAhtl, the
asymmetry between the legs was also calculated from the signals of these sensors.
Finally, the accelerometry data from both AccAhtl and AccTrea approaches were
analyzed using Matlab (R2015a with Signal Processing Toolbox, MathWorks Inc., Natick,
MA, USA), providing a set of spatio-temporal parameters such as step time (ms), step
length (m), and step frequency (spm), that allowed a comparison of the two approaches.
Appendix B details the algorithms used for calculating these parameters.
2.3. Statistics
Prior to the validity and reliability tests, a chi-square test was performed to determine
whether there were differences between males and females. The agreement between the
two systems was reviewed by a Bland–Altman plot for each of the variables analyzed.
The differences between the two systems (AccTrea–AccAthl) in each variable were plotted
against the mean results [45]. Reliability was contrasted by means of a two-way, random-
effects, single-measure (median of the two trials) intraclass correlation coefﬁcients (ICC(2,1))
model. In conjunction with the ICC values, standard error of measurement (SEM) and
minimum detectable change (MDC) values were calculated to assess the concurrent validity
between the AccTrea and the AccAthl, as well as the within-device test–retest reliability
and measurement error over the two testing sessions for all outcome measures [46]. Point
estimates of the ICCs were interpreted as follows: Excellent (0.75–1), modest (0.4–0.74),
or poor (0–0.39) [47]. All statistical analyses were conducted using the Statistical Package
for the Social Sciences (SPSS Inc. Version 26.0, Chicago, IL, U.S.A.). The MDC, which is
otherwise known as the reliable change index score, was calculated using the equations
reported previously by Jacobson and Truax [48]. It is expressed as the percentage test–retest
change in impact acceleration or spatio-temporal parameter required to ﬁnd a signiﬁcant
difference at an alpha level of 0.05 based on the Day 1 mean value.
3. Results
3.1. Gender Differences
The results of the chi-square test showed no statistically signiﬁcant differences (mean
bilateral asymptotic signiﬁcance 0.411) regarding gender for any of the variables analyzed.
Therefore, during this study, all subsequent statistical analyses were conducted jointly,
including men and women, as a single sample for each of the groups.
3.2. Perceived Exertion
Regarding the perceived exertion, no differences were found between sessions for any
of the study groups (Table 2).
Table 2. Rate of perceived exertion (RPE) differences between sessions at warm-up and the run-
ning test.
Day 1
Day 2
p Value 1
Warm-up (M ± SD)
9.0 ± 1.9
8.8 ± 2.0
0.68
Running test (M ± SD)
9.8 ± 1.6
9.5 ± 1.8
0.46
1 RPE differences between days (t-test).
Sensors 2021, 21, 1758
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3.3. Bland–Altman Plots
All participants successfully completed the two days’ sessions. The Bland–Altman
plots for the step length, step time, step frequency, and peak acceleration impact asymmetry
are provided in Figure 2. There was a small relationship between the difference and the
mean for all the spatio-temporal variables. Speciﬁcally, step length and time were slightly
lower in the AccAthl system compared to AccTrea, and as a result, the step frequency
variable was higher in the AccAthl system. The acceleration impact asymmetry did not
show any obvious relationship between systems.
 
Table 2. Rate of perceived exertion (RPE) differences between sessions at warm-up and the run-
ning test. 
 
Day 1 
Day 2 
p Value 1 
Warm-up (M ± SD) 
9.0 ± 1.9 
8.8 ± 2.0 
0.68 
Running test (M ± SD) 
9.8 ± 1.6 
9.5 ± 1.8 
0.46 
1 RPE differences between days (t-test). 
3.3. Bland–Altman Plots 
All participants successfully completed the two days’ sessions. The Bland–Altman 
plots for the step length, step time, step frequency, and peak acceleration impact asym-
metry are provided in Figure 2. There was a small relationship between the difference and 
the mean for all the spatio-temporal variables. Specifically, step length and time were 
slightly lower in the AccAthl system compared to AccTrea, and as a result, the step fre-
quency variable was higher in the AccAthl system. The acceleration impact asymmetry 
did not show any obvious relationship between systems. 
 
Figure 2. Bland–Altman plots representing comparisons between the AccTrea system and the AccAthl system for four of 
the variables analyzed: (A) Step length; (B) step time (duration); (C) step frequency; and (D) peak acceleration impact 
asymmetry. The mean line represents the mean difference between the devices, with the upper and lower dashed lines 
representing the 95% limits of agreement (LOAs). 
3.4. Validity and Reliability 
The results for the step length, step time, step frequency, left leg peak acceleration 
impact, right leg peak acceleration impact, and peak acceleration impact asymmetry var-
iables are provided in Table 3. The step length and step time were lower in the AccAthl 
system compared with AccTrea. Step frequency, left leg peak acceleration impact, and 
right leg peak acceleration impact variables showed a bias toward higher values in the 
tests performed on the AccAthl. Inconsistent results were found for peak acceleration im-
pact asymmetry variables. 
In general, both systems showed excellent test–retest reliability (Table 3), with only 
the peak acceleration impact asymmetry values’ performance on the AccTrea (ICC = 0.36) 
failing to reach an ICC value of 0.75, considered as an excellent value. Concurrent validity 
Figure 2. Bland–Altman plots representing comparisons between the AccTrea system and the AccAthl system for four
of the variables analyzed: (A) Step length; (B) step time (duration); (C) step frequency; and (D) peak acceleration impact
asymmetry. The mean line represents the mean difference between the devices, with the upper and lower dashed lines
representing the 95% limits of agreement (LOAs).
3.4. Validity and Reliability
The results for the step length, step time, step frequency, left leg peak acceleration
impact, right leg peak acceleration impact, and peak acceleration impact asymmetry
variables are provided in Table 3. The step length and step time were lower in the AccAthl
system compared with AccTrea. Step frequency, left leg peak acceleration impact, and right
leg peak acceleration impact variables showed a bias toward higher values in the tests
performed on the AccAthl. Inconsistent results were found for peak acceleration impact
asymmetry variables.
In general, both systems showed excellent test–retest reliability (Table 3), with only
the peak acceleration impact asymmetry values’ performance on the AccTrea (ICC = 0.36)
failing to reach an ICC value of 0.75, considered as an excellent value. Concurrent validity
was shown to be consistently excellent across spaciotemporal variables and testing sessions
(ICC = 0.94–0.98), but not in acceleration impact variables for every testing session (ICC
= −0.01–0.55). The SEM for the spaciotemporal variables ranged from 0.92 to 1.31% in
the AccTrea system, and from 1.19 to 1.29% in the AccAthl system. For impact variables,
the SEM ranged from 10.1 to 358% in the AccTrea system and from 12.25 to 297% in the
AccAthl system.
Sensors 2021, 21, 1758
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Table 3. Validity and reliability of an instrumented treadmill with an accelerometry system for assessment of spatio-temporal
parameters and impact transmission.
AccTrea
AccAthl
Mean Diff (95%CI)
ICC (95%CI)
Step Length (m)
Day 1 (M ± SD)
1.04 ± 0.05
1.01 ± 0.04
0.04 (0.03/0.05)
0.94 (0.87/0.97)
Day 2 (M ± SD)
1.03 ± 0.04
1.00 ± 0.05
0.04 (0.03/0.05)
0.95 (0.89/0.98)
Mean Diff (95%CI)
0.002 (−0.005/0.010)
0.011 (−0.002/0.023)
ICC (95%CI)
0.92 (0.82/0.96)
0.88 (0.73/0.95)
SEM (% SEM)
0.01 (1.31)
0.01 (1.29)
MDC (%)
0.04
0.04
Step Time (ms)
Day 1 (M ± SD)
374.8 ± 16.8
363.1 ± 13.9
12.5 (9.7/15.3)
0.94 (0.87/0.97)
Day 2 (M ± SD)
371.8 ± 15.2
359.7 ± 17.1
13.0 (10.1/15.9)
0.95 (0.89/0.98)
Mean Diff (95%CI)
0.88 (−1.75/3.51)
3.85 (−0.60/8.29)
ICC (95%CI)
0.96 (0.90/0.98)
0.89 (0.73/0.95)
SEM (% SEM)
3.55 (0.95)
4.7 (1.29)
MDC (%)
9.85
13.03
Step Frequency (spm)
Day 1 (M ± SD)
160.5 ± 7.2
166.1 ± 6.6
−5.59 (−6.7/−4.5)
0.95 (0.90/0.97)
Day 2 (M ± SD)
161.3 ± 6.7
167.1 ± 8.0
−5.94 (−7.2/−4.7)
0.95 (0.89/0.98)
Mean Diff (95%CI)
−0.17 (−1.24/0.89)
−0.80 (−2.9/1.3)
ICC (95%CI)
0.96 (0.91/0.98)
0.91 (0.82/0.93)
SEM (% SEM)
1.48 (0.92)
1.97 (1.19)
MDC (%)
4.12
5.47
Left Leg Peak Impact (g)
Day 1 (M ± SD)
0.72 ± 0.21
3.76 ± 1.37
−3.04 (−3.53/−2.56)
0.09 (−0.86/0.56)
Day 2 (M ± SD)
0.72 ± 0.22
3.93 ± 1.30
−3.21 (−3.69/−2.73)
0.08 (−0.93/0.56)
Mean Diff (95%CI)
0.001 (−0.052/0.053)
−0.181 (−0.514/0.152)
ICC (95%CI)
0.88 (0.75/0.94)
0.88 (0.74/0.94)
SEM (% SEM)
0.07 (10.05)
0.48 (12.25)
MDC (%)
0.20
1.34
Right Leg Peak Impact (g)
Day 1 (M ± SD)
0.73 ± 0.20
3.91 ± 1.62
−3.18 (−3.76/−2.59)
0.01 (−1.04/0.52)
Day 2 (M ± SD)
0.76 ± 0.18
3.97 ± 1.71
−3.21 (−3.85/−2.56)
−0.01 (−1.13/0.52)
Mean Diff (95%CI)
−0.03 (−0.08/0.02)
−0.05 (−0.42/0.32)
ICC (95%CI)
0.85 (0.69/0.93)
0.90 (0.80/0.95)
SEM (% SEM)
0.08 (10.20)
0.50 (12.64)
MDC (%)
0.21
1.39
Peak Impact Asymmetry (%)
Day 1 (M ± SD)
−2.80 ± 12.53
−1.29 ± 14.49
−1.51(15.11/2.67)
0.55 (0.07/0.78)
Day 2 (M ± SD)
−2.75 ± 9.79
2.44 ± 17.94
−6.16 (18.34/3.47)
0.28 (−0.55/0.67)
Mean Diff (95%CI)
0.75 (14.16/2.63)
−3.82 (14.94/2.77)
ICC (95%CI)
0.36 (−0.37/0.70)
0.75 (0.46/0.88)
SEM (% SEM)
10.04 (−358.66)
7.28 (297.729)
MDC (%)
27.82
20.17
AccTrea: Treadmill system; AccAthl: Athlete system; M: Mean; SD: Standard deviation; CI: Conﬁdence interval; ICC: Intraclass correlation
coefﬁcient; Diff: Difference; SEM: Standard error of the measurement; MDC: Minimum detectable change, expressed as a percentage of the
Day 1 mean value.
The MDC in all variables ranged from 0.04 to 27.8% for the AccTrea system and from
0.04 to 20.2% for the AccAthl system. The MDCs were reasonably high for both devices
only in the peak acceleration impact asymmetry variable (27.8% at AccTrea and 20.2% at
AccAthl). With respect to the other variables (spaciotemporal and impacts), the MDCs
were lower for both systems, with the AccAthl MDC values higher than the AccTrea values
in all values.
Sensors 2021, 21, 1758
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4. Discussion
Validity and reliability of spatio-temporal and impact transmission variables during
running are important in biomechanical analysis under laboratory conditions.
Treadmills are becoming popular between recreational runners [15]. Oxygen uptake,
heartrate, and perceived effort are similar between submaximal treadmill and overground
running [15]. However, running on a treadmill provides greater control over environmental
variables such as temperature, wind speed, or relative humidity [15]. Treadmills also
offer control over running velocity and surface gradient [15], and generate changes in
biomechanics parameters like step length, contact time, and stride frequency compared
with overground running [17].
These kinematics modiﬁcations favor the reduction in impact acceleration magni-
tude [13,17], axial compression strains in tibia [18], and plantar load [13,49] in comparison
with overground running. It causes runners to adopt a safer running style [17].
In addition, running retraining programs, focused on reducing the severity of impacts
that are related to running injuries, have demonstrated good results by means of intro-
ducing biofeedback systems (auditory or visual information) during training sessions on
the treadmill. Previous studies have shown that runners were able to reduce impacts and
improve running economy thanks to the concurrent information about the severity of the
impacts received from accelerometers placed directly on their body [20–22].
The control of environmental and performance factors, along with the kinematic
modiﬁcations offered by the treadmills, can make it a safer activity than overground
running. Introducing auditory or visual biofeedback information from the treadmill could
allow the control of impact acceleration and make that system accessible for all types of
runners, both professional and recreational.
Our results partially conﬁrmed the hypothesis raised in the study, that the Acc-
Trea system integrated in the treadmill is a valid and reliable tool for measuring spatio-
temporal parameters like step length (validity ICC 95%CI = 0.87/0.97; reliability ICC
95%CI = 0.82/0.96), step time (validity ICC 95%CI = 0.87/0.97; reliability ICC 95%CI =
0.90/0.98), and step frequency (validity ICC 95%CI = 0.90/0.97; reliability ICC 95%CI
= 0.91/0.98) during running on a treadmill compared to the AccAthl system under the
same speed condition. Nevertheless, peak acceleration impact variables measured during
running showed a high reliability for the left leg (reliability ICC 95%CI = 0.75/0.94) and
right leg (reliability ICC95%CI = 0.69/0.93), but not a high validity (Table 2). On the other
hand, peak acceleration impact asymmetry showed a modest validity (ICC = 0.55) but a
poor reliability (Table 2).
Prior to our study, other systems that measure spatio-temporal variables during run-
ning have been validated. These systems were initially based on contact platforms [24], but
they allowed the analysis of just a limited number of strides, in addition to the possibility
of altering the running gait. Other systems based on optoelectronics technology were also
validated [25] to measure the spatio-temporal variables without altering the natural run-
ning pattern [50], but the drawback of these systems was that they need to install different
extremely sensitive instruments whose technology is relatively expensive compared to the
technology of the AccTrea system, analyzed in the present study.
The spatio-temporal variables analyzed in our study have shown intraclass correlation
coefﬁcients (ICC > 0.946) close to those obtained by Ogueta-Alday, Morante, Rodríguez-
Marroyo, and García-López [50] when they validated a new method to measure contact
time and ﬂight time during treadmill running (SportJump System Pro, V2.0., León, Spain)
(ICC > 0.993). It should be noted that in the present study, other variables have been
analyzed than those evaluated in the SportJump System Pro, as the objective of the AccTrea
system was to provide concurrent feedback to runners in order to modify step length,
frequency, and time to improve their running economy.
The excellent validity and reliability results for the spatio-temporal variables, together
with the technology used, make the AccTrea system a low-cost and high-reliability system,
nonexistent until now.
Sensors 2021, 21, 1758
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The results of peak acceleration impact asymmetries are considered modest (ICC = 0.55)
for validity between systems and poor (ICC = 0.36) for the within-device test–retest reli-
ability for the AccTrea system. Both acceleration impact peaks of the left leg (ICC = 0.88)
and right leg (ICC = 0.85) obtained a high degree of reliability of the AccTrea system
between days, which was not the case for validity between systems (AccTrea and AccAthl),
considered as poor (ICC = 0.01).
Symmetry/asymmetry in running is very difﬁcult to keep within the same values
between different sessions as there are many factors that affect running technique [51]. It is
a personal technical factor subject to the variability in the dynamic complex systems, an
aspect that makes the standardization of the results difﬁcult [51]. The values obtained in
the present study were relatively low (little asymmetry on impacts between legs), which
could justify the poor reliability results of the system between days.
The poor validity results between systems obtained in the acceleration impact peak
variables could be related to the fact that the AccTrea system, compared to the AccAthl,
presents elements that could favor the reduction or loss of acceleration and impact dis-
sipation. These elements could be classiﬁed as elements typical of the runner, such as
the cushioning of the shoes [52]; or elements of the system itself (AccTrea), such as the
treadmill, the table, or the protection of the accelerometers, that avoid their displacement
and make them register lower values [53].
The Bland–Altman plots demonstrated low mean differences and wide limits of
agreement (LoAs) of 95%, except for the step frequency variable, with a mean difference
between systems of ±5 ppm. These differences could be explained because step time is
also slightly lower in the AccAthl system, possibly associated with acceleration losses of
the system previously mentioned.
Regarding the system, there are currently no studies with which the results obtained
from the acceleration impact variables can be compared. There are also no systems on the
market that can directly or indirectly measure the acceleration impact variables without
instrumenting the athlete and with the technology used inserted directly into the treadmill.
Previous studies that have analyzed the effect of immediate biofeedback, via audi-
tory [22] or visual [21], during running have shown that the maximum impact peak was
signiﬁcantly reduced [21,22], improving running economy [20]. Recent studies have shown
that the effects of an intervention, applying instant feedback, can last up to a year after the
intervention, notably improving the reduction of impacts and reducing the percentage of
injured athletes [54].
However, all these biofeedback systems used in previous studies have required ath-
letes to be instrumented with expensive systems and under laboratory conditions, making
the use of this type of system impractical on a recurring basis by the general population. The
implementation of a biofeedback system, such as the one analyzed in this study, represents
a step forward to make impact reductions and running economy improvements accessible
to the entire population [23] thanks to its low cost and the unneeded instrumentation
of athletes.
The results of this study determined that the system has moderate validity for the
scientiﬁc measurement of the acceleration impact variables (ICC = 0.55), but it can also be
transferred to the sports world, being a valid approximation like the contributions that
already exist in the market in the measurement of other variables.
5. Conclusions
AccTrea is a reliable and valid tool for athletes to be informed, in a concurrent way, of
their biomechanical responses in relation to spatio-temporal variables (step length, step
time, and step frequency) during running on an instrumented treadmill. On the other hand,
the limitations found in the placement of the accelerometers under the treadmill, which in
turn, are great advantages of the system by not having to instrument the athletes, make
AccTrea a reliable system measuring running impacts. While peak acceleration impact
asymmetry variables presented a modest validity between systems. As a noninvasive
Sensors 2021, 21, 1758
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biofeedback system for running biomechanical response, AccTrea demonstrates potential
as a commercial system of easy access to the general population, with high reliability
in spatio-temporal variables and peak acceleration impacts. MEMS sensor technology
coupled with a data acquisition unit and a processing unit connected with the treadmill can
provide accurate and objective data to improve running mechanics or to allow personal
trainers to select running exercises in order to change running mechanics.
6. Patents
European patent application with reference EP3735900A1 and entitled “Treadmill for
sport training” in May 2019.
U.S. patent application with reference US20200353309A1 and entitled “Ergometric
treadmill for sport training” in May 2019.
Chinese patent application with reference CN111905333A and entitled “Force measur-
ing running machine for sports training” in May 2019.
Author Contributions: Conceptualization, A.E.-M., P.P.-S. and R.B.-V.; methodology, A.E.-M. and
A.G.-G.; software, A.E.-M., R.B.-V., A.G.-G. and P.P.-S.; validation, A.E.-M., R.B.-V., A.G.-G., R.S.-S.,
and P.P.-S.; formal analysis, A.E.-M., A.G.-G., R.S.-S., and P.P.-S.; investigation, A.E.-M., R.B.-V.,
A.G.-G., R.S.-S., and P.P.-S.; resources, A.E.-M., R.B.-V., A.G.-G. and P.P.-S.; data curation, A.E.-M. and
R.B.-V.; writing—original draft preparation, A.E.-M., R.B.-V., R.S.-S., and A.G.-G.; writing—review
and editing, A.E.-M., R.B.-V., R.S.-S., and A.G.-G.; visualization, A.E.-M., R.B.-V., R.S.-S., and A.G.-G.;
supervision, A.E.-M.; project administration, A.E.-M., R.B.-V., A.G.-G. and P.P.-S.; funding acquisition,
A.E.-M. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by Bodytone International Sport, S.L., grant number CFE-
BODYTONE-03-18.
Institutional Review Board Statement: The study was conducted according to the guidelines of the
Declaration of Helsinki and approved by the Institutional Review Board of the University of Valencia
(protocol number: 6775, date 2018).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Acknowledgments: Authors want to thank Inmaculada Aparicio Aparicio for her support in the
study conceptualization and her help during data collection.
Conﬂicts of Interest: The authors declare no conﬂict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
Appendix A. Treadmill Accelerometry System (AccTrea)
The proposed measurement system comprises three main parts: A set of triple-axis
MEMS accelerometers (MEMS-As), a data acquisition unit (DAU), and a processing unit
(PU). All components are encased inside the treadmill. Thus, the system does not require
external instrumentation. The MEMS-As send the data set to the DAU, which performs the
ﬁrst stage of ﬁltering and conditioning of the data. This pre-processed data set is then used
by the PU to estimate the parameters under interest as detailed in Section 2.
Two different MEMS-A settings can be used in the system. The ﬁrst approach employs
two accelerometers, which are located on the front of the running belt, near the landing
zone of the runner. The second approach uses a four-accelerometers setting, where two
more accelerometry sensors are placed at the back of the belt. In either approach, all
accelerometers are ﬁrmly attached to the treadmill board by means of a speciﬁcally designed
holder in order to maximize the capture of the vibrations produced by the runner. The V120
optical tracking system (Optitrack V120:Trio, NaturalPoint, Inc., Corvallis, OR, USA) has
been used to determine the optimal position of the sensors to maximize the measurement
of these oscillations. Figure A1 shows the placement of the sensors of the treadmill along
with the coordinate system used in the device.
Sensors 2021, 21, 1758
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MPU-9250 accelerometry sensors (TDK InvenSense, San José, CA, USA) are used in
both settings. These sensors provide digital-output triple-axis accelerometry data with
a programmable full-scale range between ±2 g and ±16 g. This feature is particularly
interesting as it allows both low-acceleration values, such as when walking on the treadmill,
and large values, such as in fast running, to be measured accurately.
MPU-9250 accelerometers use 16-bit analog-to-digital converters (ADCs), which pro-
vide enough bit resolution for the subsequent parameter calculation. This allows us to set
the scale range to ±8 g, providing a relatively small quantiﬁcation error and, at the same
time, a wide reading range to avoid data clipping. Note that this requirement is important
because the acceleration values of the lateral (x-axis) and front-rear (y-axis) directions
are much lower than the vertical one (z-axis). Although only the z-axis component is
analyzed in this work, other accelerometry components may be used in future work. For
this reason, the acquisition and storage of all accelerometry components are implemented
in this approach.
ors 2021, 21, x FOR PEER REVIEW 
12 of 17 
 
Figure A1. Location of accelerometers (MEMS-A), data acquisition unit (DAU), and processing 
unit (PU) on the treadmill (AccTrea): (a) Four-accelerometer approach; (b) two-accelerometer ap-
proach. 
The MPU-9250 devices include user-programmable digital filters for noise reduction. 
Although a low-pass filter with a cut-off frequency of 50 Hz is commonly used in many 
similar applications [38], no in-board filtering has been set in the device for this approach. 
As shown in Appendix B, the critical information for the calculation of the parameters 
under analysis involves the time intervals between acceleration peaks. As any low-pass 
filter smooths these peaks [43], thereby reducing the accuracy of the parameter calcula-
tion, the raw data from the sensors are used for further processing and analysis in this 
approach. Note, however, that prior to any measurement, a calibration is performed on 
the DAU to compensate for gravity bias [44]. 
For the connection between the MEMS-A and the DAU, the I2C bus is chosen [55]. 
This protocol allows the transmission of data of all sensors using a single bus, minimizing 
wiring and system complexity. Each sensor is set-up in a specific address, allowing all 
accelerometry signals to be transmitted using only two wires. 
The choice of sampling frequency of the analog-to-digital converter of the sensors is 
l
l
A l
l
bl
h
h
h
l
l
f h
Figure A1. Location of accelerometers (MEMS-A), data acquisition unit (DAU), and processing unit (PU) on the treadmill
(AccTrea): (a) Four-accelerometer approach; (b) two-accelerometer approach.
The MPU-9250 devices include user-programmable digital ﬁlters for noise reduction.
Although a low-pass ﬁlter with a cut-off frequency of 50 Hz is commonly used in many
similar applications [38], no in-board ﬁltering has been set in the device for this approach.
As shown in Appendix B, the critical information for the calculation of the parameters
under analysis involves the time intervals between acceleration peaks. As any low-pass
ﬁlter smooths these peaks [43], thereby reducing the accuracy of the parameter calculation,
the raw data from the sensors are used for further processing and analysis in this approach.
Note, however, that prior to any measurement, a calibration is performed on the DAU to
compensate for gravity bias [44].
For the connection between the MEMS-A and the DAU, the I2C bus is chosen [55].
This protocol allows the transmission of data of all sensors using a single bus, minimizing
Sensors 2021, 21, 1758
12 of 16
wiring and system complexity. Each sensor is set-up in a speciﬁc address, allowing all
accelerometry signals to be transmitted using only two wires.
The choice of sampling frequency of the analog-to-digital converter of the sensors is
also a critical issue. A large sampling rate enables a high accuracy in the calculation of
the parameters. Nevertheless, it limits the use of multiple sensors with a single I2C bus
due to its bandwidth restrictions. A sampling frequency of 250 Hz has been chosen as
the compromise value, as it is large enough to obtain sufﬁcient accuracy in the required
parameters, while allowing real-time transmission of raw data from up to four sensors.
The data are collected by the DAU, powered by a ATmega2560 microcontroller (Mi-
crochip Technology Incorporated, Chandler, AZ, USA). This unit performs the following
tasks: (1) Initialization and calibration of the sensors before each training; (2) time synchro-
nization of the signals to be sent as a matrix to the subsequent PU; and (3) monitoring with
automatic restart in case of reading or transmission failure.
This data set is transmitted via a USB connection to the PU of the treadmill. This unit
is responsible for calculating the parameters listed in Section 2, i.e., positive peak tibial
acceleration, asymmetry, step time, step length, and step frequency. In addition, a frequency
analysis is carried out, which will allow future work to carry out harmonic analysis,
among others. Both Appendix B and patents [29–31] detail the process of calculating
these parameters. Figure A2 depicts the connection diagram between the MEMS-A, DAU,
and PU.
Sensors 2021, 21, x FOR PEER REVIEW 
13 of 17 
 
tasks: (1) Initialization and calibration of the sensors before each training; (2) time syn-
chronization of the signals to be sent as a matrix to the subsequent PU; and (3) monitoring 
with automatic restart in case of reading or transmission failure. 
This data set is transmitted via a USB connection to the PU of the treadmill. This unit 
is responsible for calculating the parameters listed in Section 2, i.e., positive peak tibial 
acceleration, asymmetry, step time, step length, and step frequency. In addition, a fre-
quency analysis is carried out, which will allow future work to carry out harmonic analy-
sis, among others. Both Appendix B and patents [29–31] detail the process of calculating 
these parameters. Figure A2 depicts the connection diagram between the MEMS-A, DAU, 
and PU. 
 
Figure A2. Block diagram of the AccTre system: MEMS-A connected to the DAU via an I2C bus; DAU linked to the PU via 
an USB connection; graphical user interface for biofeedback to the runner on the treadmill touch screen; transmission of 
processed data to a cloud server and access to the data set through a mobile device. 
Appendix B. Procedure for Calculating Spatio-Temporal Parameters 
The procedure for calculating these parameters is based on Pérez-Soriano and Encar-
nación-Martínez [56], although it has been adapted for each of the approaches analyzed 
in this work. Note that in this study, two accelerometers have been used in both systems. 
In AccAthl, the sensors are placed on the distal and anteromedial portion of each tibia [38] 
while in AccTrea, they are located on the front of the running belt [38] (Figure A1). 
It is important to note that although both systems measure the acceleration produced 
by both legs, the way in which both sets of data are collected is completely different. Each 
AccAthl accelerometer is attached to one of the legs. Thus, the acceleration measured on 
each leg is clearly recorded on its corresponding accelerometer while the signal caused by 
the opposite leg is noticeably lower. By contrast, as the accelerometers included in Ac-
cTrea are both attached to the rigid board of the treadmill, both accelerometers collect the 
vibration produced by both legs. Their signals vary only subtly in amplitude depending 
on the proximity of each sensor to the landing zone of each leg. Nevertheless, this slight 
difference is sufficient to determine the parameters of interest. Figure A3 illustrates the 
acceleration levels recorded by each system for the same measurement session. 
Figure A2. Block diagram of the AccTre system: MEMS-A connected to the DAU via an I2C bus; DAU linked to the PU via
an USB connection; graphical user interface for biofeedback to the runner on the treadmill touch screen; transmission of
processed data to a cloud server and access to the data set through a mobile device.
Appendix B. Procedure for Calculating Spatio-Temporal Parameters
The procedure for calculating these parameters is based on Pérez-Soriano and Encar-
nación-Martínez [56], although it has been adapted for each of the approaches analyzed in
this work. Note that in this study, two accelerometers have been used in both systems. In
AccAthl, the sensors are placed on the distal and anteromedial portion of each tibia [38]
while in AccTrea, they are located on the front of the running belt [38] (Figure A1).
It is important to note that although both systems measure the acceleration produced
by both legs, the way in which both sets of data are collected is completely different. Each
AccAthl accelerometer is attached to one of the legs. Thus, the acceleration measured on
each leg is clearly recorded on its corresponding accelerometer while the signal caused
by the opposite leg is noticeably lower. By contrast, as the accelerometers included in
AccTrea are both attached to the rigid board of the treadmill, both accelerometers collect the
vibration produced by both legs. Their signals vary only subtly in amplitude depending
Sensors 2021, 21, 1758
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on the proximity of each sensor to the landing zone of each leg. Nevertheless, this slight
difference is sufﬁcient to determine the parameters of interest. Figure A3 illustrates the
acceleration levels recorded by each system for the same measurement session.
Sensors 2021, 21, x FOR PEER REVIEW 
14 of 17 
 
 
Figure A3. Comparison of the accelerometry signals from the system AccAthl (a) and the two-accelerometer approach 
AccTrea (b). The solid and dashed line display the accelerations corresponding to the left and right legs, respectively. The 
left- and right-pointing triangles mark the acceleration peaks of the left and right legs, respectively. 
The first step in the calculation of the parameters is to determine the accelerometry 
signal peaks for both legs in each approach, including their temporal location (s) and their 
peak amplitude value (g). To obtain the location ൛݈஺௧௛,௅ሾ݊௅ሿ, ݈஺௧௛,ோሾ݊ோሿൟ and amplitude 
൛݌஺௧௛,௅ሾ݊௅ሿ, ݌஺௧௛,ோሾ݊ோሿൟ of the peaks in AccAthl, the maximum negative acceleration values 
are detected. The variables ݊௅ and ݊ோ denote the step number for the left and right leg, 
respectively. These values correspond to the time when the runner lands on each leg. To 
avoid false-negative detection, constraints on the minimum distance between peaks and 
on their prominence values are applied. 
From the data coming from AccTrea, all the peaks of both accelerometers are detected 
first. In contrast to the previous approach, the values of the maximum positive accelera-
tion are considered here, as it has been empirically proven that they provide better results 
for the analysis due to the vibration of the table. 
Note that, as shown in Figure A3b, the contribution of both legs is recorded in both 
accelerometry signals. Therefore, the correspondence of the detected peaks to each leg 
must be estimated. This is done by averaging the values of the odd and even peaks of each 
signal and assigning the higher values to the closest sensor. Once this is accomplished, a 
procedure similar to AccAthl is applied to obtain the location ൛்݈௥௘௔,௅ሾ݊௅ሿ, ்݈௥௘௔,ோሾ݊ோሿൟ and 
amplitude ൛݌்௥௘௔,௅ሾ݊௅ሿ, ݌்௥௘௔,ோሾ݊ோሿൟ of the peaks as depicted in Figure A3b. 
From these vectors ൛݌஺௧௛,௅ሾ݊௅ሿ, ݌஺௧௛,ோሾ݊ோሿൟ  and ൛݌்௥௘௔,௅ሾ݊௅ሿ, ݌்௥௘௔,ோሾ݊ோሿൟ , statistical 
values are calculated for the impacts of both legs, as well as for their asymmetry. Table 3 
shows these results for both approaches. 
The step locations are used to compute the step times using backward differences, 
Figure A3. Comparison of the accelerometry signals from the system AccAthl (a) and the two-accelerometer approach
AccTrea (b). The solid and dashed line display the accelerations corresponding to the left and right legs, respectively. The
left- and right-pointing triangles mark the acceleration peaks of the left and right legs, respectively.
The ﬁrst step in the calculation of the parameters is to determine the accelerometry
signal peaks for both legs in each approach, including their temporal location (s) and their
peak amplitude value (g). To obtain the location

lAth,L[nL], lAth,R[nR]
	
and amplitude

pAth,L[nL], pAth,R[nR]
	
of the peaks in AccAthl, the maximum negative acceleration values
are detected. The variables nL and nR denote the step number for the left and right leg,
respectively. These values correspond to the time when the runner lands on each leg. To
avoid false-negative detection, constraints on the minimum distance between peaks and
on their prominence values are applied.
From the data coming from AccTrea, all the peaks of both accelerometers are detected
ﬁrst. In contrast to the previous approach, the values of the maximum positive acceleration
are considered here, as it has been empirically proven that they provide better results for
the analysis due to the vibration of the table.
Note that, as shown in Figure A3b, the contribution of both legs is recorded in both
accelerometry signals. Therefore, the correspondence of the detected peaks to each leg
must be estimated. This is done by averaging the values of the odd and even peaks of each
signal and assigning the higher values to the closest sensor. Once this is accomplished, a
procedure similar to AccAthl is applied to obtain the location {lTrea,L[nL], lTrea,R[nR]} and
amplitude {pTrea,L[nL], pTrea,R[nR]} of the peaks as depicted in Figure A3b.
From these vectors

pAth,L[nL], pAth,R[nR]
	
and {pTrea,L[nL], pTrea,R[nR]}, statistical
values are calculated for the impacts of both legs, as well as for their asymmetry. Table 3
shows these results for both approaches.
Sensors 2021, 21, 1758
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The step locations are used to compute the step times using backward differences,
∆tAthl,L[nL] =
103
fs,Athl (lAth,L[nL] − lAth,L[nL − 1]) ms,
∆tAth,R[nR] =
103
fs,Athl (lAth,R[nR] − lAth,R[nR − 1]) ms,
∆tTrea,L[nL] =
103
fs,Trea (lTrea,L[nL] − lTrea,L[nL − 1]) ms,
∆tTrea,R[nR] =
103
fs,Trea (lTrea,R[nR] − lTrea,R[nR − 1]) ms,
(A1)
where ∆t{,◦}
h
n{◦}
i
(ms) denotes the step time for approach {}; leg {◦} and step number,
n{◦} and l{,◦}, respectively, are the peak locations (samples); fs,{} (samples per second)
stands for the sampling frequency for each approach; and {nL, nR} represents the left and
right step numbers, respectively.
The step length is simply calculated by multiplying the step time by the linear speed
of the belt, provided by the treadmill electronics,
∆l{,◦}
h
n{◦}
i
= v·10−3∆t{,◦}
h
n{◦}
i
m
(A2)
where ∆l{,◦}
h
n{◦}
i
(m) denote the step length for the step number n{◦}, ∆t{,◦}
h
n{◦}
i
are
the step times calculated in Equation (A1), and v (m/s) represents the linear speed of the
belt of the treadmill, which is identical in both approaches.
To determine the step frequency, the number of steps detected by each sensor is
counted and divided by the duration of the experiment,
s f{,◦} = 60N{,◦}/T spm
(A3)
where s f{,◦} (spm) denotes the step frequency for approach {} and leg {◦}, and T is the
duration of the experiment (T = 10 s). Once again, the statistical parameters of these
variables are shown in Table 3.
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Validity and Reliability of an Instrumented Treadmill with an Accelerometry System for Assessment of Spatio-Temporal Parameters and Impact Transmission.
Encarnación-Martínez, Alberto,Pérez-Soriano, Pedro,Sanchis-Sanchis, Roberto,García-Gallart, Antonio,Berenguer-Vidal, Rafael