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Original Paper
Force-Sensitive Mat for Vertical Jump Measurement to Assess
Lower Limb Strength:Validity and Reliability Study
Erik Vanegas1*, MSc; Yolocuauhtli Salazar2*, PhD; Raúl Igual1*, PhD; Inmaculada Plaza1*, PhD
1Electrical/Electronics Engineering and Communications Department, EUP Teruel, Universidad de Zaragoza, Teruel, Spain
2Tecnológico Nacional de México, IT Durango, Durango, Mexico
*all authors contributed equally
Corresponding Author:
Erik Vanegas, MSc
Electrical/Electronics Engineering and Communications Department, EUP Teruel
Universidad de Zaragoza
Atarazana 2
Teruel, 44003
Spain
Phone: 34 978618102
Email: erikvanegas599@gmail.com
Abstract
Background: Vertical jump height is widely used in health care and sports fields to assess muscle strength and power from
lower limb muscle groups. Different approaches have been proposed for vertical jump height measurement. Some commonly
used approaches need no sensor at all; however, these methods tend to overestimate the height reached by the subjects. There are
also novel systems using different kind of sensors like force-sensitive resistors, capacitive sensors, and inertial measurement
units, among others, to achieve more accurate measurements.
Objective: The objective of this study is twofold. The first objective is to validate the functioning of a developed low-cost
system able to measure vertical jump height. The second objective is to assess the effects on obtained measurements when the
sampling frequency of the system is modified.
Methods: The system developed in this study consists of a matrix of force-sensitive resistor sensors embedded in a mat with
electronics that allow a full scan of the mat. This mat detects pressure exerted on it. The system calculates the jump height by
using the flight-time formula, and the result is sent through Bluetooth to any mobile device or PC. Two different experiments
were performed. In the first experiment, a total of 38 volunteers participated with the objective of validating the performance of
the system against a high-speed camera used as reference (120 fps). In the second experiment, a total of 15 volunteers participated.
Raw data were obtained in order to assess the effects of different sampling frequencies on the performance of the system with
the same reference device. Different sampling frequencies were obtained by performing offline downsampling of the raw data.
In both experiments, countermovement jump and countermovement jump with arm swing techniques were performed.
Results: In the first experiment an overall mean relative error (MRE) of 1.98% and a mean absolute error of 0.38 cm were
obtained. Bland-Altman and correlation analyses were performed, obtaining a coefficient of determination equal to R2=.996. In
the second experiment, sampling frequencies of 200 Hz, 100 Hz, and 66.6 Hz show similar performance with MRE below 3%.
Slower sampling frequencies show an exponential increase in MRE. On both experiments, when dividing jump trials in different
heights reached, a decrease in MRE with higher height trials suggests that the precision of the proposed system increases as height
reached increases.
Conclusions: In the first experiment, we concluded that results between the proposed system and the reference are systematically
the same. In the second experiment, the relevance of a sufficiently high sampling frequency is emphasized, especially for jump
trials whose height is below 10 cm. For trials with heights above 30 cm, MRE decreases in general for all sampling frequencies,
suggesting that at higher heights reached, the impact of high sampling frequencies is lesser.
(JMIR Mhealth Uhealth 2021;9(4):e27336) doi: 10.2196/27336
KEYWORDS
vertical jump; mHealth; mobile health; force-sensitive resistor; lower limb strength; leg strength
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Introduction
Vertical jump height is one of the physical skills commonly
used to assess overall performance in human beings, and more
specifically, it is used to assess performance and muscle power
of the quadriceps, hamstrings, and gastrocnemius muscle groups
in the lower limbs [1,2]. Measurement of the performance of
this skill is commonly performed on athletes in sports like
basketball [3,4], football [5], netball [6], swimming [7], and
others. This skill performance can also provide important data
from people with no relevant sports past.
In the literature, there are many protocols to prove or validate
the proposed systems. Among the different kind of jumps
performed in those protocols, there are jumps with and without
countermovement [1,4,5,8-15], jumps with and without arm
swing [12,16], drop jumps [1,8,17], single and double leg jump
[6], continuous jumps [4,17], squat jumps [1,2,4,12], and loaded
squat jumps [7]. With any of these types of jumps, height
reached by the user can be analyzed, but the jumps most
commonly used in all related work are the countermovement
and squat jumps.
Kibele [15] and Moir [18] reported that irregularities detected
in measurements of any kind of jump execution may be linked
to changes in the posture of a subject during flight due to change
in the center of mass of the subject during the jump. Bui et al
[13] found that some common errors obtained during
measurement were caused by body movements like knee, hip,
and ankle bending during flight time and landing. Also,
Aragon-Vargas [19] states that ascending and descending phases
of flight time must be of the same length of time, but in his work
descending time was significantly longer, suggesting that
participants descended with their bodies partially crouched.
There are several techniques to measure jump height, each of
which uses a different kind of sensor or no sensors at all.
Methods like the Sargent jump [13] and Vertec device [6,9,10]
require no sensors and often are used as reference measurements.
However, these methods often show overestimation on jump
height, and this could be due to arm stretching performed
unconsciously by the user. Among systems developed in the
literature, different kind of sensors are used like force-sensitive
resistors (FSRs) [3,16], capacitive sensors [5], inertial
measurement units [2,4,8,10,17], electromyography sensors
[1,6], kinematic sensors [6], ultrasonic sensors [20],
microswitches [9], video cameras, [11] and optical sensors
[4,12].
The studies of Drazan et al [3] and Boukhenous and Attari [16]
are most closely related to our work, as both of their systems
also use resistive sensors. However, only one sensor is used for
the whole sensing area. Drazan et al [3] proposed a system based
on a single FSR sensor whose total sensing area is around 3
cm2, with an Arduino board as microcontroller. This system
calculated jump height through the flight-time formula, by
measuring the time the FSR sensor is not detecting any pressure.
In the work presented by Boukhenous and Attari [16], two
metallic strain gauges were placed in the center of a rigid
platform to measure the force applied by the ground. In this
case, vertical ground reaction forces were used to calculate jump
height. Rico et al [2] used pressure sensors located at the
forefoot of the user to calculate flight time during vertical jump
and compare it with data obtained from an inertial measurement
unit system. However, few or no specific number of subjects
are used in these studies, and no specific protocols or jump trials
are performed. Also, no reference systems are used to compare
the performance of the developed systems.
Camera-based systems are used as reference systems or as
proposed methods to validate. In some studies, the famous
motion capture system commonly used in videogames is used
as a reference device [5,17,21]. Other studies use a similar
method by tracking body markers placed strategically on the
body [4,14,21]. Balsalobre-Fernandez et al [11,22] have
analyzed the effectiveness and reliability of high-speed cameras
as methods to estimate vertical jump height. In these studies,
flight time of the subject is calculated by selecting the takeoff
and landing frames of the recorded videos of jump trials, and,
by applying the flight-time formula, jump height is obtained
[3,8,9,11,14,16,22].
Only a few studies perform a validation with a relatively high
number of subjects. Some studies that fulfill this criterion are
the ones presented by Nuzzo et al [10], Casartelli et al [4],
Glatthorn et al [12], Moir [18], and Aragon-Vargas [19].
However, these studies compare different commercially
available devices (contact mat, force plate, cameras, etc), and
no novel system is developed by the researchers. Bui et al [13]
fulfills the criterion and proposes a novel optical system whose
performance is compared against commercial devices. This
system calculates jump height through the flight-time formula.
This study presents a newly developed low-cost system for
measuring height reached by users during vertical jump
comprising a matrix of FSR sensors embedded on a mat. The
height of the vertical jump is calculated through the flight-time
formula [3,8,9,14,16]. One advantage this system offers against
other pressure-sensitive systems [2,3,16] is a higher real sensing
area, higher resolution, and higher precision, as this system
works with 256 FSR sensors distributed around the mat in 16
rows and 16 columns, in comparison with the other systems
that use a single sensor. The total sensing area, dimensions of
FSR sensors matrix, and each individual FSR sensor area can
be modified on different versions of the proposed mat. Also, as
this system is environmental, it needs no adjustment regardless
of the physical characteristics of the user like body type, weight,
height, or foot size [2]. Another advantage of this system is that
the calculated vertical jump height is directly sent to a PC or
mobile device of the health care professional’s choice, unlike
other methods that require postprocessing analysis, as in the
high-speed camera method. The main objective of this study is
to validate the reliability of the proposed system for future
clinical studies.
Methods
System Construction
The proposed system consists of 2 parts: a resistive
pressure-sensitive mat constructed with an FSR sensor array
and the electronic system. The mat is composed of 3 layers.
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One layer contains thin and flat copper wires distributed in a
column arrangement along a flexible 3D printed grid; another
layer is composed of the same copper wires but distributed in
a row arrangement on another flexible 3D printed grid. A third
layer is placed between the layers consisting of Velostat
material, a pressure-sensitive material that behaves as a resistor
whose value drops whenever pressure is exerted upon it. In this
way, variations on the resistance values on every intersection
of rows and columns when pressure is exerted over the mat can
be measured. Some typical characteristic problems with Velostat
material are repeatability, nonlinearity, and hysteresis [23,24].
For this application, however, these features are not relevant
because high precision is not needed; the mat only needs to be
able to detect a heavy body placed on it (average human body
weight). More information about the development of this mat
is documented on the work of Medrano et al [25].
Figure 1 shows the different layers of an FSR matrix with
smaller dimensions (4 rows and 4 columns). Figure 2shows an
example of the placement of the overlapped layers. The total
sensing area of the mat used for this study is 30×30 cm, with
16 rows and 16 columns, 1 cm width each. This way, the area
of each of the individual FSR sensors is equal to 1 cm2. In Figure
3, a developed mat is shown.
Figure 1. Different layers comprising a force-sensitive resistor matrix with smaller dimensions.
Figure 2. Example of a smaller size matrix and how layers are placed.
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Figure 3. Developed force-sensitive resistor sensor mat.
Due to the number of operations needed for a full scan of the
mat, a high frequency microprocessor must be used for data
processing, as the time complexity of these operations grows
in exponential order. The STM32F103C8T6 microprocessor
(STMicroelectronics) was selected due to its 72 MHz CPU
frequency, with which a sampling frequency of 200 Hz is
achieved. Other microprocessors with lower CPU frequencies
(like the ATmega328P, Microchip Technology Inc) would not
achieve the desired sampling frequency. Also two 16-1
multiplexers 74HC4067 are needed for an efficient scan process
of the whole mat. For data transmission, a Bluetooth HC-05
module is used. Bluetooth technology was selected due to its
ease of connection with different devices, especially with
smartphones and tablets, which offers health care professionals
the choice of an easy-to-transport monitoring device. Other
electronic elements included in the system are a TP4056
battery-charging module and a Lipo battery of 3.7 V and 150
mAh capacity, allowing continuous functioning of the system
for up to 2 hrs. A block diagram of the proposed system is
shown in Figure 4.
The algorithm used for this system consists of calculating the
summation of every FSR sensor of the mat. For each FSR
sensor, the voltage value obtained by the analog-to-digital
converter of the microcontroller is given in bits (from 0 to 4095),
and this resulting value is used for the calculations. A threshold
is used for the system to decide whether a person is standing
on the mat or not. To calculate an appropriate value for this
threshold, data were collected from 16 volunteers (5 female and
11 male), with an average weight of 74.81 (SD 15.25) kg and
foot size of 26.93 (SD 1.94) cm. The volunteers were asked to
stand on the mat barefoot in 4 different positions: with both feet
standing still and on their forefoot and with one foot standing
still and on their forefoot. Maximum values of pressure exerted
on FSR sensors were used as reference for normalization, and
the minimum value for activation of FSR sensors was considered
as no volunteer standing on the mat.
Using such criteria, on average when standing still over the mat
with both feet subjects activated 71.66% of the FSR sensors,
and when standing on their forefoot with both feet, 28.9% of
the FSR sensors were activated. When standing still and on their
forefoot with only one foot an activation of 40.37% and 18.40%
of the FSR sensors was registered, respectively. The minimum
value of FSR sensors activation is registered when standing on
one foot on their forefoot, with a value of 12.09%. All results
are summarized in Table 1. By taking these results into account,
and if it is assumed that volunteers may land first with one foot
on their forefoot after a jump, a proper threshold should be
proposed below the minimum value of FSR sensors activation.
For this study, a threshold of 9% of FSR sensors activation is
used. This threshold was chosen to be at three-quarters between
the zero FSR sensor activation and the minimum FSR sensor
activation recorded, to avoid any misreading from mechanical
oscillation. It is worth noting that the minimum recorded value
from sensor activation is an outlier. In future studies, the
possibility of adding a personalized threshold for every subject
could be assessed.
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Figure 4. Block diagram of the proposed system.
Table 1. Normalized force-sensitive resistor sensors activation registered from volunteers standing at different positions on the mat; standing still and
on their toes with both feet and standing still and on their toes with one foot.
One foot %Both feet %Sensor activation
ToesStandingToesStanding
18.4040.3728.9071.66Average
27.0061.4543.31100.00Maximum
12.0922.6423.7044.00Minimum
For every position sensor activation percentage, a correlation
analysis with the weight and foot size of the volunteers was
performed. Analysis suggests that sensor activation is only
moderately impacted by the weight of volunteers, and foot size
of volunteers has a low impact on sensor activation. In Table
2, correlation values for different positions analyzed on the mat
are shown.
To calculate the height reached by the user during the vertical
jump, when the subject jumps and there is no contact on the
mat, the system counts the elapsed time until the subject lands
on the mat again (flight time), and with the calculated time the
flight-time formula is used [3,8,9,14,16] to predict the height
reached. This formula is defined as: Height = gΔt2/8, where g
is the constant value of gravity force g=9.81 m/s2 and Δt is the
flight time obtained by the system. Once the height of the
vertical jump is obtained, this value is wirelessly sent via
Bluetooth to the monitoring device selected by the health care
professional.
Table 2. Pearson correlation coefficient values (R values) for different positions analyzed, calculated for weight and foot size of volunteers.
Foot sizeWeightPosition
Both feet
–.522–.684Standing
–.241–.411Forefoot
One foot
–.397–.447Standing
–.463–.394Forefoot
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Experimental Setup
Two different experiments were performed. The purpose of the
first experiment, in which 38 volunteers participated, was to
validate the reliability of the proposed system. For the second
experiment, the objective was to compare the effects of different
sampling frequencies when calculating the height reached on
the jumps, and 15 volunteers participated. The protocol used
for each experiment is the same. For the first experiment, data
are directly processed by the microcontroller and the predicted
value is sent to the selected monitoring station. In the second
experiment, raw data are obtained to perform an offline
downsampling to analyze the effects of different sampling
frequencies on the predicted result.
Researchers asked for assistance from a sports and fitness center
to recruit volunteers who attended the center regularly for
physical training. Researchers visited this center with all the
necessary equipment for the implementation of the protocols,
and installed it in an area specified by the managers of the
center. No specific physical attributes were required from the
volunteers, as these characteristics should not affect the
performance of the system. Researchers approached people at
the center, explained the purpose of the study, and politely asked
for their collaboration on the protocols if they were available
at any given moment. Before starting any trial, volunteers were
asked if they had any kind of injury that could affect their
physical integrity when performing the protocol, and if so, the
trials would not proceed. Every volunteer gave their written
consent for the performance of the proposed protocol. The
countermovement jump (CMJ) and countermovement jump
with arm swing (CMJAS) techniques were selected for this
protocol. These jumping techniques are commonly used as a
measure to assess the overall force and explosive power of the
lower body muscles on a person [26], and it is considered as
the most reliable jumping test for this purpose [27]. By adding
an arm swing to the CMJ, with the proper technique, the height
reached by the person is increased around one-third and up to
two-thirds [28-30], which increases the dynamic range of data
obtained.
In the proposed protocol, volunteers were asked to stand on a
marker placed on the center of the mat and perform 3
medium-to-maximal effort CMJs, with their hands fixed at the
waist, with 5 to 10 seconds rest between trials. This technique
is depicted in Figure 5. After these jumps, the volunteers were
asked to perform CMJAS this time, and following the same
scheme. This technique is depicted in Figure 6. As a reference
system, all trials were recorded on video with a high-speed
camera (120 fps). The camera was placed 1.3 m away from the
mat, perpendicular to the sagittal plane of the volunteer and 20
cm above the ground, held by a tripod. The setup for this
protocol is depicted in Figure 7.
To measure height reached by the subject with the video
reference, the takeoff and landing frames were selected manually
like in the studies of Balsalobre-Fernandez et al [11,22], and
height was calculated by using the elapsed time between the
frames using the flight-time formula.
Figure 5. Countermovement jump technique, step by step.
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Figure 6. Countermovement jump with arm swing technique, step by step.
Figure 7. Experimental setup used for proposed experiments.
Results
First Experiment: System Validation
For the first experiment, a total of 228 jumps (114 CMJs and
114 CMJASs) were performed for each, the proposed system
and the video reference. An example of the recorded jumps is
shown in Figure 8.
To analyze the proposed system performance, mean relative
error (MRE) and mean absolute error (MAE) were calculated
for the overall jump trials and for each technique, CMJ and
CMJAS. The MRE obtained from all 228 trials was 1.98%. For
CMJ and CMJAS, relative errors were 2.17% and 1.78%,
respectively. MAE obtained from all jump trials was 0.38 cm,
and for CMJ and CMJAS, the errors obtained were 0.34 cm and
0.42 cm, respectively. These results are summarized in Table
3.
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Figure 8. Two volunteers performing the proposed protocol showing the different phases of the jumps: takeoff frame, maximum-height frame, and
landing frame.
Table 3. Mean absolute error and mean relative error values for overall jump trials, only countermovement jump, and only countermovement jump
with arm swing trials.
MREb(%)MAEa(cm)Trials
1.980.38Overall
2.170.34CMJc
1.780.42CMJASd
aMAE: mean absolute error.
bMRE: mean relative error.
cCMJ: countermovement jump.
dCMJAS: countermovement jump with arm swing.
Correlation and Bland-Altman analyses were performed from
data obtained and are shown in Figure 9 and Figure 10,
respectively. Correlation analysis shows a coefficient of
determination of R2=.996. These analyses demonstrate that the
proposed system not only has a high correlation, but it shows
that the difference of the two paired measurements is really low,
which means that both methods produce systematically the same
results.
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Figure 9. Correlation graph comparing both measuring methods for the first experiment, showing a coefficient of determination of R2=.996.
Figure 10. Bland-Altman plot of both measuring methods: countermovement jump depicted by dark gray points and countermovement jump with arm
swing depicted by light gray points.
In Figure 11, the normalized MAE and MRE are shown for
different ranges of jump heights reached. By analyzing the
different ranges of height reached by the volunteers, which are
<10 cm, 10 to 20 cm, 20 to 30 cm, and >30 cm, MREs obtained
were 2.38%, 2.07%, 1.90%, and 1.54%, respectively. MAE
obtained were 0.18 cm, 0.31 cm, 0.46 cm, and 0.50 cm,
respectively. From this data, no significant difference can be
found. However, it can be noticed that MAE increases as jump
height increases, while MRE decreases.
Figure 12 shows the charts with distribution of the heights
reached by the volunteers when performing the jump trials. For
CMJ, no volunteer was able to surpass the 30 cm height.
However, by adding the arm swing, 22% of the volunteers
surpassed the 30 cm height. Also, for CMJAS trials, 47% of the
subjects reached a height ranging from 20 to 30 cm, compared
with CMJ, in which only 31% of the subjects reached this height.
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Figure 11. Normalized mean absolute error and mean relative error, divided in different ranges of height reached during vertical jump.
Figure 12. Distribution of different height ranges reached by the users. Overall trials, only countermovement jump trials, and only countermovement
jump with arm swing trials are shown.
Second Experiment: Sampling Frequencies
Comparison
In this experiment, the effects of different sampling frequencies
were analyzed. Raw data from the system was obtained for a
total of 90 jumps (45 CMJs and 45 CMJASs). An offline
emulation of different sampling frequencies was performed
through downsampling of this raw data. This means samples
are removed to emulate a slower sampling frequency. With this
method, and with the base sampling period of 5 ms from the
system, 200 Hz, 100 Hz, 66.6 Hz, 50 Hz, 40 Hz, 33.3 Hz, 28.5
Hz, 25 Hz, 22.2 Hz, and 20 Hz frequencies were emulated.
Similar to the first experiment, the error was calculated using
the high-speed camera as reference. For this analysis, only MRE
was obtained for each sampling frequency to assess which
frequencies are able to maintain a relative error below 5%.
Results show that sampling frequencies of 200 Hz, 100 Hz, and
66.6 Hz have similar performance, with relative errors of 1.88%,
2.22%, and 2.88%, respectively. However, the maximum error
among the 90 trials increases considerably between these
frequencies, with maximum errors of 5.27%, 7.02%, and 8.25%
for each respective frequency. Sampling frequencies of 50 Hz,
40 Hz, and 33.3 Hz also show good performance regarding the
relative error, which is maintained below 5% for the 3 cases,
but the maximum relative error found in these 3 frequencies is
considerably higher than the found in the previous set.
In Table 4, MRE and maximum and minimum relative errors
found among trials for the different sampling frequencies are
shown. With slower sampling frequencies, MRE increases
exponentially as shown in Figure 13, which suggests that
sampling frequencies equal to or below 28.5 Hz are not reliable
enough to maintain MRE below 5%. Also, sampling frequencies
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slower than 50 Hz and 33.3 Hz show maximum relative error
among trials higher than 10% and 20%, respectively.
Table 5shows how MRE is distributed in different ranges. Only
200 Hz and 100 Hz sampling frequencies are able to maintain
95% of their results within 5% of relative error. Also, sampling
frequencies slower than 50 Hz considerably increase the
percentage of relative errors found above 5%. These results
suggest that sampling frequencies of 200 Hz and 100 Hz are
the most reliable, frequencies of 66.6 Hz and 50 Hz have an
acceptable performance, and the remaining sampling frequencies
are unreliable for this specific application.
Figure 13. Mean relative error obtained for each proposed sampling frequency. As sampling frequency decreases, relative error increases exponentially.
Table 4. Mean relative error and maximum and minimum relative errors obtained from all 90 trials for each sampling frequency analyzed.
Sampling periods/frequenciesRelative
error
50 ms, 20
Hz
45 ms, 22.2
Hz
40 ms, 25
Hz
35 ms, 28.5
Hz
30 ms, 33.3
Hz
25 ms, 40
Hz
20 ms, 50
Hz
15 ms, 66.6
Hz
10 ms, 100
Hz
5 ms, 200
Hz
8.758.026.276.044.974.503.522.882.221.88
MREa
32.1128.3919.7321.3014.2514.259.738.257.025.27
MAXb
0.7300000.700000
MINc
aMRE: mean relative error.
bMAX: maximum relative error.
cMIN: minimum relative error.
Table 5. Percentage of trials whose relative error is within the ranges of 5% or less, higher than 5% and lower than 15%, and higher than 15%.
Sampling periods/frequenciesRelative error
50 ms, 20
Hz
45 ms, 22.2
Hz
40 ms, 25
Hz
35 ms, 28.5
Hz
30 ms, 33.3
Hz
25 ms, 40
Hz
20 ms, 50
Hz
15 ms, 66.6
Hz
10 ms,
100 Hz
5 ms, 200
Hz
27.7835.5652.2248.8957.7858.8976.6787.7894.4498.89
REa ≤5%
57.7854.4443.3347.7842.2241.1123.3312.225.561.11RE 5% to 15%
14.44104.443.33000000RE >15%
aRE: relative error.
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When MRE is obtained from different jump heights (≤10 cm,
10 to 20 cm, 20 to 30 cm, and >30 cm) at each sampling
frequency, the relevance of a proper sampling frequency when
calculating height reached for small jumps (<20 cm) is observed.
When using sampling frequencies slower than 50 Hz, MRE
obtained from these small jumps is always higher than 5%, and
for the slowest sampling frequency, MRE reaches a value of
21.50% for jump heights smaller than 10 cm. For higher jump
heights, an increase in MRE (>5%) is noticeable for sampling
frequencies slower than 40 Hz, reaching a value of up to 9.15%
for the slowest sampling frequency. A summary of these results
is shown in Table 6.
Table 6. Mean relative error obtained from each of the analyzed sampling frequencies for different ranges of height reached during the vertical jump.
Sampling periods/frequenciesJump height
50 ms, 20
Hz
45 ms, 22.2
Hz
40 ms, 25
Hz
35 ms, 28.5
Hz
30 ms, 33.3
Hz
25 ms, 40
Hz
20 ms, 50
Hz
15 ms, 66.6
Hz
10 ms, 100
Hz
5 ms, 200
Hz
21.5010.776.295.405.095.282.624.332.291.31<10 cm
8.429.658.607.555.455.214.292.892.402.2310-20 cm
9.156.966.125.065.294.623.553.022.311.9120-30 cm
6.857.324.345.834.173.632.922.501.951.60>30 cm
Discussion
Principal Findings
The vertical jump is a test commonly used by health care
professionals to assess strength in the lower limb muscles of a
subject. Although this test is widely used for strength assessment
among athletes, relevant information can be obtained from
people with no relevant sports background.
An important point to highlight about the system developed in
this study is its low price. The total for components used in
construction is approximately US $40. In comparison with
commercially available devices, this developed system is
significantly more affordable. Among the devices commonly
used on medical and sports fields to measure vertical jump
height are the vertical jump test mat (Gill Athletics) [31], Just
Jump system (Perform Better) [10,32], Vertec device (Gill
Athletics) [10,33], electronic vertical jump tester (Gill Athletics)
[34], Optojump testing (Perform Better) [12,35], and bilateral
force plate (Hawkin Dynamics) [18,36]. Our proposed system
will have to pass through different standards and certifications
(like ISO standards [37]) before it can be considered as a
standard medical device. Table 7 shows a comparison of prices
between commercially available devices and the system
proposed here. Prices of the commercially available devices are
listed as found at the moment of writing this article.
Table 7. Comparison of prices between commercially available devices and the system developed in this study.
Price $Device for vertical jump measurement
40Proposal from this study (estimated price of components)
360Vertical jump test mat (Gill Athletics) [31]
629Just Jump System (Perform Better) [32]
760Vertec device (Gill Athletics) [33]
2925Electronic vertical jump tester (Gill Athletics) [34]
3804Optojump testing (Perform Better) [35]
5000Bilateral force plate (Hawkin Dynamics) [36]
Throughout data capturing in both experiments, some important
points can be highlighted. Despite the advantages that the
proposed system and reference device offer, both have an
inherent error due to their sampling frequency (more specifically
due to their sampling period). The proposed system has a
sampling frequency of 200 Hz, and thus the sampling period is
5 ms. Likewise, the reference device has a sampling frequency
of 120 Hz and a sampling period of 8.3 ms. This means that
every sampling period each device updates its readings, which
implies an uncertainty of the sampling period between data
updates. In other words, there is an inherent uncertainty in the
system during the takeoff and landing phases of the jump, time
span that is used to calculate height reached. From both phases,
the proposed system has a total uncertainty of 10 ms, while the
reference device has a total uncertainty of 16.6 ms. This inherent
error is characteristic of electronic devices and directly related
to their sampling frequency. Nonelectronic methods for jump
height measuring lack of this inherent error, but as stated before,
these methods tend to overestimate obtained measurements and
are less precise.
Regarding the high-speed camera used as a reference device,
when the recorded videos were analyzed, the ease of selecting
the correct frames depended on the correct technique execution
of the volunteer: taking off from both forefeet at the same time
during the takeoff phase and landing with both forefeet at the
same time during the landing phase. This was the ideal technique
execution. On the other hand, some volunteers either took off
or landed with only one forefoot and not with the same foot in
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some occasions. In such cases, it was harder to select the takeoff
and landing frames. This is difficult for volunteers to control
without long-term training in the proper technique.
On the proposed protocol, the inclusion of two different jump
techniques proved to be useful in order to increase the dynamic
range of the data. The difference between the CMJ and CMJAS
was significant. The addition of an arm swing increased jump
height an average of 44.84% in the first experiment and 34.86%
in the second experiment.
Limitations
One of the main limitations of the developed system was its
sampling frequency. Although the microcontroller used had a
high CPU frequency, the sampling frequency was limited
because of the number of operations needed for a full scan of
the mat (16 rows and 16 columns, a scan of 256 individual cells)
and number of operations this implies. Another limitation was
the total sensing area of the system of 30×30 cm. Although no
volunteer reported discomfort, the total area limits the stance
of volunteers; in addition, the landing phase of every jump trial
must be performed in a controlled manner, so the volunteer
lands inside this area.
These limitations can be solved in future versions of the mat.
The design of the mat can be modified to increase its total
sensing area and the size of each row and column, so in this
way with fewer number of rows and columns the same sensing
area could be achieved, thus increasing the sampling frequency
of the system. However, this would diminish the resolution of
the system.
Conclusions
In this study, a novel low-cost system for measurement of the
jump height is proposed. Two experiments were
performed—one to validate the system and the other to assess
the effects of different sampling frequencies.
When evaluating the performance of the proposed system in
the first experiment, results show that with the proposed
sampling frequency of 200 Hz relative error for all of the 228
jump trials is maintained below 5%. In the second experiment,
with sampling frequencies of 200 Hz and 100 Hz, relative error
is maintained below 5% for 98.89% and 94.44% of the jump
trials, respectively.
The flight-time formula is a widely used, validated method to
calculate height reached during vertical jumps. A high-speed
camera as reference device has been used in related studies
along with the flight-time formula, proving to be a reliable tool.
Our first experiment showed through correlation and
Bland-Altman analyses that the proposed system and a
high-speed camera reference device produced systematically
similar results when calculating jump height.
Our second experiment concluded that 200 Hz and 100 Hz
sampling frequencies have similar performance, and both
frequencies are reliable when calculating jump height using the
flight-time formula. This implies that if access to hardware
capable of processing data at 200 Hz were limited, hardware
capable of processing data to at least 100 Hz could offer similar
results. However, if higher sampling frequencies are available,
they should be used.
These results demonstrate that the proposed system is as reliable
as a commercially available device, and the selected sampling
frequency of 200 Hz is reliable for obtaining relative errors
below 5% for at least 95% of the jump trials. The proposed
system offers an alternative for health care professionals to use
a mobile monitoring station of their choice, and its price is more
affordable than commercially available devices.
Acknowledgments
This research was funded by grant Programa Operativo FEDER Construyendo Europa desde Aragon T49_20R from the European
Union and Gobierno de Aragón, grant UZCUD2019-TEC-02 from the Universidad de Zaragoza and Centro Universitario de la
Defensa de Zaragoza, and grant 709365 from the Consejo Nacional de Ciencia y Tecnología.
Conflicts of Interest
None declared.
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Abbreviations
CMJ: countermovement jump
CMJAS: countermovement jump with arm swing
FSR: force-sensitive resistor
MAE: mean absolute error
MRE: mean relative error
Edited by L Buis; submitted 21.01.21; peer-reviewed by O Aragón Banderas, J Seitz; comments to author 02.03.21; revised version
received 10.03.21; accepted 19.03.21; published 09.04.21
Please cite as:
Vanegas E, Salazar Y, Igual R, Plaza I
Force-Sensitive Mat for Vertical Jump Measurement to Assess Lower Limb Strength: Validity and Reliability Study
JMIR Mhealth Uhealth 2021;9(4):e27336
URL: https://mhealth.jmir.org/2021/4/e27336
doi: 10.2196/27336
PMID:
©Erik Vanegas, Yolocuauhtli Salazar, Raúl Igual, Inmaculada Plaza. Originally published in JMIR mHealth and uHealth
(http://mhealth.jmir.org), 09.04.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution
License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any
medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic
information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must
be included.
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