sensors
Article
A Sensor Platform for Athletes’ Training Supervision:
A Proof of Concept Study
Alessandro Zompanti 1,*
, Anna Sabatini 1
, Marco Santonico 1, Simone Grasso 1
,
Antonio Gianfelici 2, Bruno Donatucci 2, Andrea Di Castro 2 and Giorgio Pennazza 1
1
Unit of Electronics for Sensor Systems, Department of Engineering Campus Bio-Medico University of Rome,
00128 Rome, Italy; a.sabatini@unicampus.it (A.S.); m.santonico@unicampus.it (M.S.);
s.grasso@unicampus.it (S.G.); g.pennazza@unicampus.it (G.P.)
2
Sport Medicine and Science Institute, CONI (Comitato Olimpico Nazionale Italiano), 00197 Rome, Italy;
antonio.gianfelici@gmail.com (A.G.); b.donatucci@coni.it (B.D.); dicastro.training@gmail.com (A.D.C.)
*
Correspondence: a.zompanti@unicampus.it; Tel.: +39-062-2541-9610
Received: 4 July 2019; Accepted: 6 September 2019; Published: 12 September 2019


Abstract: One of the basic needs of professional athletes is the real-time and non-invasive monitoring
of their activities. The use of these kind of data is necessary to develop strategies for speciﬁc tailored
training in order to improve performances. The sensor system presented in this work has the aim to
adopt a novel approach for the monitoring of physiological parameters, and athletes’ performances,
during their training. The anaerobic threshold is herein identiﬁed with the monitoring of the lactate
concentration and the respiratory parameters. The data collected by the sensor are used to build
a model using a supervised method (based on the partial least squares method, PLS) to predict the
values of the parameters of interest. The sensor is able to measure the lactate concentration from
a sample of saliva and it can estimate a respiratory parameter, such as maximal oxygen consumption,
maximal carbon dioxide production and respiratory rate from a sample of exhaled breath. The main
advantages of the device are the low power; the wireless communication; and the non-invasive
sampling method, which allow its use in a real context of sport practice.
Keywords: athletes’ performances; low power sensor; wireless system; lactate
1. Introduction
A gas sensor array for volatile ﬁngerprinting (dubbed e-nose) and e-tongue systems (based on
cyclic voltammetry analysis) are used in several applications.
E-nose is applied in several ﬁelds: from the medical practice for the early detection of respiratory
diseases [1], to the evaluation of food freshness [2] and the monitoring of the air quality [3]. The e-tongue
systems are used for voltammetric analysis in the discrimination of the quality of olive oil [4], wine [5],
water [6] and also physiological liquids [7].
Those systems have not been used, so far, for the evaluation of sport activities.
In sport activities the monitoring of the physiological parameters allows one to evaluate the
physical condition of the athletes. The current state of art of this research ﬁeld does not follow a unique
guideline, but several approaches are pursued and diﬀerent theories are reported to support the
eﬀectiveness of each parameter proposed for athletes’ monitoring [8].
The accepted and available approaches can be divided into two groups: the ﬁrst one based on
a self-report measurement of the physical eﬀort (e.g., Borg Scale), and the second one that consists
of the objective monitoring of certain physiological parameters. In the ﬁrst approach the athlete
has to score his physical eﬀort using a standard scale; the second method is performed via speciﬁc
measurement instruments. These two approaches can also be jointly applied. In the application of
Sensors 2019, 19, 3948; doi:10.3390/s19183948
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the second approach, the anaerobic threshold is often used. It is a well-known parameter for the
assessment of sportive performances, deﬁned by Mader as the 4 millimoles per litre concentration
of lactate [9]. There is also evidence about the use of other phisiological parameters, such as heart
rate [10], respiratory rate [11] and other respiratory parameters for the evaluation of the physical eﬀort.
However, in spite of the lack of a standard and shared methodology, lactate concentration is
the most used [12]. This fact is also evident by the availability of many diﬀerent instruments for the
measurement of lactate in the blood extracted by the ear lobe [12].
A key-point in the application of lactate evaluation is represented by the invasivity of the technique,
which is based on blood analysis. Here, the ability of a sensor device to estimate the lactate concentration
via the analysis of a saliva sample, which, of course, involves a non invasive collection procedure, is
demonstrated. A step up in the ﬁeld should be given by the simultaneuos application of non invasive
and easy-to-use sensors able to implement the measurements of many other physiological parameters.
Feasibility of this approach is granted by the design and development of dedicated electronic interfaces
for sensors’ interactions as a collaborative sensor system, via low-power strategies and low-noise
circuits for signal acquisition and treatment. The aims of this work are: (1) the demonstration of the
feasibility of a method to estimate lactate concentration in humans by the analysis of saliva, with a proof
of concenpt study showing the eﬀectiveness of the saliva sampling procedure and the relevance of
the instrumental output in the estimation of the lactate concentration; (2) the feasibility of a system
approach based on more than one sensors in order to enlarge the system’s accuracy and eﬀectiveness
in the monitoring of athletes’ training.
2. Materials and Methods
During the experiment, samples of saliva and of exhaled breath were collected. Saliva was
analyzed using the sensor array for liquid analysis of a sensor system named BIONOTE [13]. Exhaled
breath has been measured using a sensor array for volatile analysis of the BIONOTE system. Both these
systems, named BIONOTE-L and BIONOTE-V respectively, are brieﬂy described below. Further details
can be found in the references.
The BIONOTE-V used in this set-up is based on 20 MHz quartz microbalances, the module
is composed of 7 QMBs (Quartz MicroBalance) covered by anthocyanins extracted from diﬀerent
plant tissues, such as blue orthesia, red cabbage and red rose [13]. The breath is stored in a Tenax
cartridge [14], after collection performed via Pneumopipe® [15] and analyzed with a desorbing process
run consecutively at 50, 100, 150 and 200 ◦C. Between the desorbing steps, a ﬂow of nitrogen is
distributed inside the measurement chamber. The ﬁnal output for each measurement is an array of
28 numbers. These 28 numbers are the frequency shifts registered by each of the 7 QMBs for the four
temperatures of desorption. More technical information about the most relevant parameters of these
acoustic sensors can be found in literature [13,16,17].
The BIONOTE-L performs voltammetric analysis on the saliva samples, using 3 screen-printed
electrodes made of gold (working electrode), silver (reference electrode) and platinum (counter
electrode): the system has a low power, about 80 mW, 3.6 V, 22 mA and a size of about 12 cm2.
These features, the order of magnitude of the power and size, allow the use of the system in
wearable applications.
In the case of BIONOTE-L, Data, acquired with a microcontroller, are transmitted using a BLE
(Bluetooth Low Energy) module to an external device, like a smartphone or tablet. The input signal is
a triangular wave ranging between −1 and 1 V with a frequency of 0.01 Hz. That signal is generated
by the microcontroller and the electrode is driven by a custom electronic interface. In Figure 1 the
structure of the sensor used in this study is reported [18].
When the input signal is applied to the solution, a redox reaction is induced and the output signal
is measured in the working electrode. A current ﬂows from the counter electrode to the working
electrode and it is converted to a voltage value using a trans-impedance stage. The output of the sensor
is an array of 500 numbers.
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Figure 1. Device used in the experimental set-up: (a) BIONOTE-L (b) BIONOTE-V. 
The samples of saliva and of exhaled breath were collected during the performing of a modified 
Mader test. Twelve male triathlon athletes, trained in running, swimming and cycling, performed a 
running test on a treadmill designed for the evaluation of the athletes’ maximum effort. The study 
was in collaboration with the Italian National Olympic Committee (Comitato Olimpico Nazionale 
Italiano) Sport Medicine and Science Institute, CONI, Rome. The modified test is composed of 5 
minutes running at different increasing speeds (12, 13.5, 15 and 16.5 km/h) and each step is separated 
from the next one by 2 minutes’ pause. During the pauses saliva samples are collected using a 
sampling device called Salivette® [19]. The athletes were requested to gently chew the cotton roll for 
one minute to stimulate saliva production and then to put it in the plastic tube. The extraction process 
was performed with the centrifugation of the sampler for 2 minutes at 1000 RCF and room 
temperature. The extracted liquid volume of 0.5 mL could be frozen to allow post measurement, or 
directly diluted in deionized water and analyzed: standard disposable spectrometry cuvettes, with a 
volume of 4 mL, were used; in order to ensure that the electrodes were correctly dipped into the 
liquid solution, saliva samples were diluted into the minimum required volume of 3.75 mL. Before 
the analysis of each sample of saliva, a white sample of deionized water was measured. 
The exhaled breath was collected at the beginning and at the end of the test and it required the 
athletes to breathe normally for 3 minutes into the Pneumopipe [16]. The cartridge was stored at 4 °C 
until the analysis with the BIONOTE-V (Figure 2). 
Output data have been used for the elaboration of the models to predict several physiological 
parameters (blood lactate concentration, maximal oxygen production, maximal carbon dioxide 
production, respiratory ratio, etc.). Both supervised and unsupervised analyses were performed, 
specifically PCA (principal component analysis) and PLS-DA (partial least squares discriminant 
analysis), using the PLS Toolbox in the MATLAB environment. PCA and PLS-DA are two data 
analysis techniques normally used to treat and elaborate multidimensional data-sets. They represent 
Figure 1. Device used in the experimental set-up: (a) BIONOTE-L (b) BIONOTE-V.
The samples of saliva and of exhaled breath were collected during the performing of a modiﬁed
Mader test. Twelve male triathlon athletes, trained in running, swimming and cycling, performed
a running test on a treadmill designed for the evaluation of the athletes’ maximum eﬀort. The study was
in collaboration with the Italian National Olympic Committee (Comitato Olimpico Nazionale Italiano)
Sport Medicine and Science Institute, CONI, Rome. The modiﬁed test is composed of 5 minutes
running at diﬀerent increasing speeds (12, 13.5, 15 and 16.5 km/h) and each step is separated from the
next one by 2 minutes’ pause. During the pauses saliva samples are collected using a sampling device
called Salivette® [19]. The athletes were requested to gently chew the cotton roll for one minute to
stimulate saliva production and then to put it in the plastic tube. The extraction process was performed
with the centrifugation of the sampler for 2 minutes at 1000 RCF and room temperature. The extracted
liquid volume of 0.5 mL could be frozen to allow post measurement, or directly diluted in deionized
water and analyzed: standard disposable spectrometry cuvettes, with a volume of 4 mL, were used;
in order to ensure that the electrodes were correctly dipped into the liquid solution, saliva samples
were diluted into the minimum required volume of 3.75 mL. Before the analysis of each sample of
saliva, a white sample of deionized water was measured.
The exhaled breath was collected at the beginning and at the end of the test and it required the
athletes to breathe normally for 3 minutes into the Pneumopipe [16]. The cartridge was stored at 4 ◦C
until the analysis with the BIONOTE-V (Figure 2).
Output data have been used for the elaboration of the models to predict several physiological
parameters (blood lactate concentration, maximal oxygen production, maximal carbon dioxide
production, respiratory ratio, etc.). Both supervised and unsupervised analyses were performed,
speciﬁcally PCA (principal component analysis) and PLS-DA (partial least squares discriminant
analysis), using the PLS Toolbox in the MATLAB environment. PCA and PLS-DA are two data analysis
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techniques normally used to treat and elaborate multidimensional data-sets. They represent an optimal
solution to extract relevant information by the output of arrays of chemical sensors [20], by reducing
the number of variables via a linear combination of the original parameters in the direction able to
maximize information. The model calibration was performed using measurements collected during the
execution of the modiﬁed Mader test. Speciﬁcally the physiological values measured are: VO2—oxygen
uptake; VCO2—carbon dioxide production; Pet O2—oxygen partial pressure; Pet CO2—carbon dioxide
partial pressure; FR—respiratory rate; RQ—respiratory ratio (produced CO2/O2 uptake); VT—tidal
volume; Ti—duration of inspiration; and Te—duration of expiration. These parameters were real-time
monitored, during the whole duration of the test, using a Quark CPET platform (COSMED srl, [21]):
The Quark CPET is a state-of-the-art metabolic cart for gas exchange analysis (VO2, VCO2) either
during exercise testing or resting protocols. The platform is equipped with a paramagnetic sensor for
the O2, and an infrared sensor for the CO2. The athletes have to breathe into a sensor-embedded mask
connected to a workstation that is able to record and evaluate data (Figure 2).
Several PLS models were built analyzing the data obtained from exhaled breath samples and
saliva samples.
Two models were built using data obtained analyzing saliva samples with BIONOTE-L: the
models were built using only the output data obtained from the BIONOTE-L; therefore, a 500-column
matrix was used. The model was cross-validated using the leave-one-out method. The ﬁrst model
was built to predict lactate concentration in the whole range of variation for the parameter (from 0
to 10 mmol/L); the second model was built to measure the lactate in the range of 2-6 mmol/L. The
same goals were achieved with two models built using a data-set obtained from the data fusion
of data collected measuring both saliva samples (using BIONOTE-L) and exhaled breath samples
(using BIONOTE-V), thus a 528 column matrix was used. A linear normalization was performed on
the dataset, but no feature selection was performed. All the models were cross-validated using the
leave-one-out method.
The lactate values were measured via an earlobe blood sample collected by a doctor during
the pauses.
Sensors 2019, 19, x FOR PEER REVIEW 
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an optimal solution to extract relevant information by the output of arrays of chemical sensors [20], 
by reducing the number of variables via a linear combination of the original parameters in the 
direction able to maximize information. The model calibration was performed using measurements 
collected during the execution of the modified Mader test. Specifically the physiological values 
measured are: VO2—oxygen uptake; VCO2—carbon dioxide production; Pet O2—oxygen partial 
pressure; Pet CO2—carbon dioxide partial pressure; FR—respiratory rate; RQ—respiratory ratio 
(produced CO2/O2 uptake); VT—tidal volume; Ti—duration of inspiration; and Te—duration of 
expiration. These parameters were real-time monitored, during the whole duration of the test, using 
a Quark CPET platform (COSMED srl, [21]): The Quark CPET is a state-of-the-art metabolic cart for 
gas exchange analysis (VO2, VCO2) either during exercise testing or resting protocols. The platform 
is equipped with a paramagnetic sensor for the O2, and an infrared sensor for the CO2. The athletes 
have to breathe into a sensor-embedded mask connected to a workstation that is able to record and 
evaluate data (Figure 2). 
Several PLS models were built analyzing the data obtained from exhaled breath samples and 
saliva samples. 
Two models were built using data obtained analyzing saliva samples with BIONOTE-L: the 
models were built using only the output data obtained from the BIONOTE-L; therefore, a 500-column 
matrix was used. The model was cross-validated using the leave-one-out method. The first model 
was built to predict lactate concentration in the whole range of variation for the parameter (from 0 to 
10 mmol/L); the second model was built to measure the lactate in the range of 2-6 mmol/L. The same 
goals were achieved with two models built using a data-set obtained from the data fusion of data 
collected measuring both saliva samples (using BIONOTE-L) and exhaled breath samples (using 
BIONOTE-V), thus a 528 column matrix was used. A linear normalization was performed on the 
dataset, but no feature selection was performed. All the models were cross-validated using the leave-
one-out method. 
The lactate values were measured via an earlobe blood sample collected by a doctor during the 
pauses. 
 
Figure 2. The athlete is running on a treadmill; Respiratory parameters and heart rate are real-time 
monitored. During the pause between the steps, lactate from blood and saliva samples are evaluated; 
at the beginning and at the end of the test as well. An exhaled breath sample is also collected. 
3. Results 
Data collected from both BIONOTE-L and BIONOTE-V were analyzed using an unsupervised 
method, principal component analysis (PCA), in order to evaluate the method’s effectiveness. Then, 
a supervised analysis, the partial least squares discriminant analysis (PLS-DA), was used to build 
predictive models for the determination of saliva lactate levels and respiratory parameters. During 
the calibration process, measured blood lactate concentrations and respiratory parameters were used 
to build the models. 
 
 
Figure 2. The athlete is running on a treadmill; Respiratory parameters and heart rate are real-time
monitored. During the pause between the steps, lactate from blood and saliva samples are evaluated;
at the beginning and at the end of the test as well. An exhaled breath sample is also collected.
3. Results
Data collected from both BIONOTE-L and BIONOTE-V were analyzed using an unsupervised
method, principal component analysis (PCA), in order to evaluate the method’s eﬀectiveness. Then,
a supervised analysis, the partial least squares discriminant analysis (PLS-DA), was used to build
predictive models for the determination of saliva lactate levels and respiratory parameters. During the
calibration process, measured blood lactate concentrations and respiratory parameters were used to
build the models.
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3.1. PCA Analysis
Data obtained from BIONOTE-L, measuring saliva samples and white samples of deionized
water, were analyzed using PCA. The scope of this elaboration was to check if the procedure for saliva
collection and treatment is eﬀective: is the ﬁngerprint of a saliva sample obtained by its measurement
with the BIONOTE-L diﬀerent from another (similar) standard solution? If yes, as the PCA model
demonstrates, it could be possible to distinguish among diﬀerent saliva samples. Figure 3 shows the
results: on the plane representing the score-plot of the ﬁrst two principal components of the calculated
model, the information is mainly contained in PC1 (90.6% of the information); two clusters can be
distinguished, one for the saliva samples and the other for the white samples.
Sensors 2019, 19, x FOR PEER REVIEW 
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3.1. PCA Analysis 
Data obtained from BIONOTE-L, measuring saliva samples and white samples of deionized 
water, were analyzed using PCA. The scope of this elaboration was to check if the procedure for 
saliva collection and treatment is effective: is the fingerprint of a saliva sample obtained by its 
measurement with the BIONOTE-L different from another (similar) standard solution? If yes, as the 
PCA model demonstrates, it could be possible to distinguish among different saliva samples.  
Figure 3 shows the results: on the plane representing the score-plot of the first two principal 
components of the calculated model, the information is mainly contained in PC1 (90.6% of the 
information); two clusters can be distinguished, one for the saliva samples and the other for the white 
samples. 
 
Figure 3. Principal component analysis (PCA) analysis of saliva and white samples are evaluated 
using a PCA method. Here the plot score of the first two principal component is reported. It contains 
almost the 97% of the explained variance. Here, two clusters can be distinguished: the blue one is 
composed of white samples while the orange cluster is made of saliva samples. 
Thus, PCA analysis shows the effectiveness of the sampling method and the ability of the 
BIONOTE-L to discriminate saliva samples from white samples. 
3.2. PLS-DA Analysis 
Several PLS models were built analyzing the data obtained from exhaled breath samples and 
saliva samples. 
Two models were built using data obtained analyzing saliva samples with BIONOTE-L; their 
performances are reported in Table 1. The first model was built to predict lactate concentration in the 
whole range of variation of the parameter (from 0 to 10 mmol/L); it presents a RMSECV (root mean 
square in cross validation) error of 1.94 mmol/L. The second model was built to measure the lactate 
in the range of 2–6 mmol/L; this range is critical for the evaluation of the anaerobic threshold in order 
to improve the athletes’ performances. In this case, the RMSECV error decreases at 0.66 mmol/L. The 
score plots of the models are reported respectively in Figure 4a,b. 
Figure 3. Principal component analysis (PCA) analysis of saliva and white samples are evaluated using
a PCA method. Here the plot score of the ﬁrst two principal component is reported. It contains almost
the 97% of the explained variance. Here, two clusters can be distinguished: the blue one is composed
of white samples while the orange cluster is made of saliva samples.
Thus, PCA analysis shows the eﬀectiveness of the sampling method and the ability of the
BIONOTE-L to discriminate saliva samples from white samples.
3.2. PLS-DA Analysis
Several PLS models were built analyzing the data obtained from exhaled breath samples and
saliva samples.
Two models were built using data obtained analyzing saliva samples with BIONOTE-L; their
performances are reported in Table 1. The ﬁrst model was built to predict lactate concentration in
the whole range of variation of the parameter (from 0 to 10 mmol/L); it presents a RMSECV (root
mean square in cross validation) error of 1.94 mmol/L. The second model was built to measure the
lactate in the range of 2–6 mmol/L; this range is critical for the evaluation of the anaerobic threshold in
order to improve the athletes’ performances. In this case, the RMSECV error decreases at 0.66 mmol/L.
The score plots of the models are reported respectively in Figure 4a,b.
Table 1. Model parameters obtained from saliva analysis.
Range
Latent
Variables
RMSECV
Lactate
0–10 mmol/L
7
1.94 mmol/L
Lactate
2–6 mmol/L
35
0.66 mmol/L
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Figure 4. Score plot of (a) lactate in the range of 0–10 mmol/L; (b) lactate in the range of 2–6 mmol/L. 
Table 1. Model parameters obtained from saliva analysis. 
 
Range 
Latent Variables 
RMSECV 
Lactate 
0–10 mmol/L 
7 
1.94 mmol/L 
Lactate 
2–6 mmol/L 
35 
0.66 mmol/L 
Several models were built using data obtained analyzing exhaled breath samples with 
BIONOTE-V; six respiratory parameters can be predicted using these models. The errors are reported 
in Table 2. 
Table 2. Model parameters obtained from exhaled breath analysis. 
 
range 
LVs 
RMSECV 
VCO2 
3000–6000 mL/min 
3 
720 mL/min 
VO2 
3000–5500 mL/min 
2 
894.55 mL/min 
Pet O2 
100–125 mmHg 
3 
4.71 mmHg 
Pet CO2 
30–45 mmHg 
3 
2.49 mmHg 
ReRa 
0.95–1.2 [mL/min]/[mL/min] 
4 
0.04 [mL/min]/[mL/min] 
VT 
2–3 L 
3 
0.66 L 
Figure 5 shows the score plot of the parameters VCO2 and VO2. 
 
Figure 5. Score plot of: (a) VCO2; (b) VO2 using data from exhaled breath. 
Figure 4. Score plot of (a) lactate in the range of 0–10 mmol/L; (b) lactate in the range of 2–6 mmol/L.
Several models were built using data obtained analyzing exhaled breath samples with BIONOTE-V;
six respiratory parameters can be predicted using these models. The errors are reported in Table 2.
Table 2. Model parameters obtained from exhaled breath analysis.
Range
LVs
RMSECV
VCO2
3000–6000 mL/min
3
720 mL/min
VO2
3000–5500 mL/min
2
894.55 mL/min
Pet O2
100–125 mmHg
3
4.71 mmHg
Pet CO2
30–45 mmHg
3
2.49 mmHg
ReRa
0.95–1.2 [mL/min]/[mL/min]
4
0.04 [mL/min]/[mL/min]
VT
2–3 L
3
0.66 L
Figure 5 shows the score plot of the parameters VCO2 and VO2.
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Figure 4. Score plot of (a) lactate in the range of 0–10 mmol/L; (b) lactate in the range of 2–6 mmol/L. 
Table 1. Model parameters obtained from saliva analysis. 
 
Range 
Latent Variables 
RMSECV 
Lactate 
0–10 mmol/L 
7 
1.94 mmol/L 
Lactate 
2–6 mmol/L 
35 
0.66 mmol/L 
Several models were built using data obtained analyzing exhaled breath samples with 
BIONOTE-V; six respiratory parameters can be predicted using these models. The errors are reported 
in Table 2. 
Table 2. Model parameters obtained from exhaled breath analysis. 
 
range 
LVs 
RMSECV 
VCO2 
3000–6000 mL/min 
3 
720 mL/min 
VO2 
3000–5500 mL/min 
2 
894.55 mL/min 
Pet O2 
100–125 mmHg 
3 
4.71 mmHg 
Pet CO2 
30–45 mmHg 
3 
2.49 mmHg 
ReRa 
0.95–1.2 [mL/min]/[mL/min] 
4 
0.04 [mL/min]/[mL/min] 
VT 
2–3 L 
3 
0.66 L 
Figure 5 shows the score plot of the parameters VCO2 and VO2. 
 
Figure 5. Score plot of: (a) VCO2; (b) VO2 using data from exhaled breath. 
Figure 5. Score plot of: (a) VCO2; (b) VO2 using data from exhaled breath.
The same models were built using a data-set obtained from the data fusion of data collected
measuring both saliva samples (using BIONOTE-L) and exhaled breath samples (using BIONOTE-V).
The data-fusion models present RMSECV errors reported in Table 3.
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Table 3. Model parameters obtained using a fusion of saliva and exhaled breath data.
Range
LVs
RMSECV
VCO2
3000–6000 mL/min
2
1024 mL/min
VO2
3000–5500 mL/min
2
894 mL/min
Pet O2
100–125 mmHg
4
6.11 mmHg
Pet CO2
30–45 mmHg
3
2.46 mmHg
ReRa
0.95–1.2 [mL/min]/[mL/min]
3
0.06 [mL/min]/[mL/min]
VT
2–3 L
2
0.5 L
4. Discussion
The BIONOTE can be used for the detection of the anaerobic threshold and for the monitoring
the athletes’ performances. The physiological parameter can be predicted using diﬀerent models
developed in this work. The lactate concentration error is 1.94 mmol/L and it decreases in the range of
interest, 2–6 mmol/L, at 0.66 mmol/L. This value is larger than the error claimed by other commercial
devices, as shown in Table 4, but its advantage is that BIONOTE is non invasive and easy-to-use, so the
method used for the acquisition of the data does not need the involvement of expert medical staﬀ.
This promising result of saliva analysis also paves the way to a fruitful application for disease diagnosis,
as already demonstrated with other sensors [22]. The respiratory parameters’ models obtained with
the exhaled breath data show a higher error in cross validation compared to models obtained with
exhaled breath and saliva data. The device and the set-up can be improved and this could give
a reduction in the error in the model. Further work could be focused on developing an experimental
set-up to allow direct sampling from the mouth of the athlete, or adding a functionalized layer that
can improve the detection of the lactate for a speciﬁc application. By the way, it must be considered
that the current method for exhaled breath collection and desorption is mediated by an adsorbing
cartridge. The contribution of the exhaled breath ﬁngerprint given by BIONOTE-V is important for
athlete monitoring, but the exhaled breath sampling and measurement procedure should be further
developed in order to be integrated in a same device with the BIONOTE-L. Obviously this aspect is
beyond the scope of this paper. Besides, it is a useful reminder that the same wireless BLE solution
used for the BIONOTE-L has already been tested for the BIONOTE-V. The device’s size and low power
consumption allows the use of the device in an IoT (Internet of Things) system in which each athlete
can be monitored remotely by a trainer or a doctor which can evaluate his performance, which can also
be done by comparing it with respect to other sportsmen.
Table 4. Comparison chart of the proposed device and ﬁve commercial portable devices [12].
Manufacturer
Method
Analysis time
[s]
Accuracy [within
2–5 mmol/L] [19]
Invasiveness
BIONOTE-L
ESS Lab, UCBM, Italy
Eletrochemical
sensor
100
0.66
NO
Lactate Pro2
Arkray KDK, Japan
Aperometic
reagent
15
0.11
YES
Lactate Scout+
EKF Giagnostics,
Germany
Enzymatic
amperometric
10
0.09
YES
Nova Statsrip
Xpress
Nova Biomedical, USA
Electrochemical
biosensor
13
0.13
YES
Edge
Transatlenticv Science,
USA
Electrochemical
biosensor
45
0.14
YES
i-STAT
Abbott Laboratories, USA
Amperometric
280
0.45
YES
It is worth a remark that for the real application of this method based on saliva and exhaled breath
collection and measurement, two points have to be clariﬁed in future experiments: (1) the conﬁrmation
of the method’s eﬀectiveness for women as well (herein a male population has been tested); (2) testing
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the eﬀectiveness of this monitoring ‘action’ for supporting an improvement in the training procedures
of the athletes.
5. Conclusions
The BIONOTE sensor platform can be used for the detection of the anaerobic threshold and for
the monitoring the athletes’ performances, measuring blood lactate concentration from saliva samples
and respiratory parameters from exhaled breath samples.
Even if the device shows a measurement error higher than the error claimed by other commercial
devices, it has the great advantage to be non invasive and easy-to-use: so it can be used without the
needing of medical staﬀ. The contribution of exhaled breath ﬁngerprint is important to achieve a more
inclusive monitoring of the athletes’ performances: however further developments are required to
allow direct sampling of exhaled breath samples, without the mediation of adsorbing cartridges.
The device’s size and low power consumption allows the use of the sensor platform in an IoT
(Internet of Things) system in which each athlete can be monitored remotely by a trainer or a doctor
which can evaluate his performance, which can also be done by comparing it with respect to
other sportsmen.
Author Contributions: Conceptualization, G.P., M.S. and A.G.; methodology, M.S. and A.G.; software, A.Z.;
validation, A.S., S.G., A.D.C. and A.Z.; formal analysis G.P. and M.S.; investigation, G.P., M.S. and A.G.; resources,
A.D.C., B.D. and A.G.; data curation, A.S., S.G., and A.Z.; writing—original draft preparation, A.S. and A.Z.;
writing—review and editing, G.P., M.S. and A.G.
Funding: This research received no external funding.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
References
1.
Dragonieri, S.; Pennazza, G.; Carratu, P.; Resta, O. Electronic Nose Technology in Respiratory Diseases. Lung
2017, 195, 157–165. [CrossRef] [PubMed]
2.
Loutﬁ, A.; Coradeschi, S.; Mani, G.K.; Shankar, P.; Rayappan, J.B.B. Electronic noses for food quality: A review.
J. Food Eng. 2015, 144, 103–111. [CrossRef]
3.
Zampolli, S.; Elmi, I.; Ahmed, F.; Passini, M.; Cardinali, G.C.; Nicoletti, S.; Dori, L. An electronic nose based
on solid state sensor arrays for low-cost indoor air quality monitoring applications. Sens. Actuators B Chem.
2004, 101, 39–46. [CrossRef]
4.
Santonico, M.; Grasso, S.; Genova, F.; Zompanti, A.; Parente, F.; Pennazza, G. Unmasking of olive oil
adulteration via a multi-sensor platform. Sensors 2015, 15, 21660–21672. [CrossRef] [PubMed]
5.
Gutiérrez, J.M.; Moreno-Barón, L.; Pividori, M.I.; Alegret, S.; del Valle, M. A voltammetric electronic tongue
made of modiﬁed epoxy-graphite electrodes for the qualitative analysis of wine. Microchim. Acta 2010, 169,
261–268. [CrossRef]
6.
Santonico, M.; Parente, F.R.; Grasso, S.; Zompanti, A.; Ferri, G.; D’Amico, A.; Pennazza, G. Investigating
a single sensor ability in the characterisation of drinkable water: a pilot study. Water Environ. J. 2016, 30,
253–260. [CrossRef]
7.
Grasso, S.; Santonico, M.; Bisogno, T.; Pennazza, G.; Zompanti, A.; Sabatini, A.; Maccarrone, M. An Innovative
Liquid Biosensor for the Detection of Lipid Molecules Involved in Diseases of the Nervous System. Proceedings
2018, 2, 760. [CrossRef]
8.
Chen, K.Y.; Janz, K.F.; Zhu, W.; Brychta, R.J. Re-deﬁning the roles of sensors in objective physical activity
monitoring. Med. Sci. Sports Exerc. 2012, 44, S13–S23. [CrossRef] [PubMed]
9.
Heck, H.; Mader, A.; Hess, G.; Mücke, S.; Müller, R.; Hollmann, W. Justiﬁcation of the 4-mmol/L lactate
threshold. Int. J. Sports Med. 1985, 6, 117–130. [CrossRef] [PubMed]
10.
Achten, J.; Jeukendrup, A.E. Heart rate monitoring. Sports Med. 2003, 33, 517–538. [CrossRef] [PubMed]
11.
Nicolò, A.; Marcora, S.M.; Sacchetti, M. Respiratory frequency is strongly associated with perceived exertion
during time trials of diﬀerent duration. J. Sports Sci. 2016, 34, 1199–1206. [CrossRef] [PubMed]
12.
Bonaventura, J.M.; Sharpe, K.; Knight, E.; Fuller, K.L.; Tanner, R.K.; Gore, C.J. Reliability and accuracy of six
hand-held blood lactate analysers. J. Sports Sci. Med. 2015, 14, 203–214. [PubMed]
Sensors 2019, 19, 3948
9 of 9
13.
Santonico, M.; Pennazza, G.; Grasso, S.; D’Amico, A.; Bizzarri, M. Design and test of a biosensor-based
multisensorial system: a proof of concept study. Sensors 2013, 13, 16625–16640. [CrossRef]
14.
Tenax GR. Available online: https://www.sigmaaldrich.com/catalog/product/supelco/28272u (accessed on
11 September 2019).
15.
Pennazza, G.; Santonico, M.; D’Amico, A.; Incalzi, R.A.; Petriaggi, M. Pneumopipe—Auxiliary Device for
Collection and Sampling of Exhaled Air. European Patent No. 12425057.2, 25 September 2013.
16.
Pennazza, G.; Santonico, M.; Incalzi, R.A.; Scarlata, S.; Chiurco, D.; Vernile, C.; D’Amico, A. Measure chain for
exhaled breath collection and analysis: A novel approach suitable for frail respiratory patients. Sens. Actuators
B Chem. 2014, 204, 578–587. [CrossRef]
17.
Pennazza, G.; Santonico, M.; Zompanti, A.; Grasso, S.; D’Amico, A. Electronic Interface for a Gas Sensor
System Based on 32 MHz QCMs: Design and Calibration. IEEE Sens. J. 2017, 18, 1419–1426. [CrossRef]
18.
Pennazza, G.; Santonico, M.; Vollero, L.; Zompanti, A.; Sabatini, A.; Kumar, N.; Pini, I.; Solano, W.F.Q.;
Sarro, L.; D’Amico, A. Advances in the Electronics for Cyclic Voltammetry: The Case of Gas Detection by
Using Microfabricated Electrodes. Front Chem. 2018, 6, 327. [CrossRef]
19.
SARSTEDT. Available online: https://www.sarstedt.com/en/products/diagnostic/salivasputum/product/51.
1534/ (accessed on 11 September 2019).
20.
Natale, C.D.; Martinelli, E.; Pennazza, G.; Orsini, A.; Santonico, M. Data analysis for chemical sensor arrays.
In Advances in Sensing with Security Applications; Springer: Dordrecht, The Netherlands, 2006; pp. 147–169.
21.
Quark CPET: Research Grade Stationary System for Accurate and Reliable Metabolic Measurements. Available
online: https://www.cosmed.com/en/products/cardio-pulmonary-exercise-test/quark-cpet (accessed on
11 September 2019).
22.
Bottoni, U.; Tiriolo, R.; Pullano, S.A.; Dastoli, S.; Amoruso, G.F.; Nistico, S.P.; Fiorillo, A.S. Infrared saliva
analysis of psoriatic and diabetic patients: similarities in protein components. IEEE Trans. Biomed. Eng. 2015,
63, 379–384. [CrossRef]
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A Sensor Platform for Athletes' Training Supervision: A Proof of Concept Study.
Zompanti, Alessandro,Sabatini, Anna,Santonico, Marco,Grasso, Simone,Gianfelici, Antonio,Donatucci, Bruno,Di Castro, Andrea,Pennazza, Giorgio