Electronic Devices for Stress Detection in Academic Contexts during Conﬁnement Because of the COVID-19 Pandemic

: This article studies the development and implementation of different electronic devices for measuring signals during stress situations, speciﬁcally in academic contexts in a student group of the Engineering Department at the University of Pamplona (Colombia). For the research’s development, devices for measuring physiological signals were used through a Galvanic Skin Response (GSR), the electrical response of the heart by using an electrocardiogram (ECG), the electrical activity produced by the upper trapezius muscle (EMG), and the development of an electronic nose system (E-nose) as a pilot study for the detection and identiﬁcation of the Volatile Organic Compounds proﬁles emitted by the skin. The data gathering was taken during an online test (during the COVID-19 Pandemic), in which the aim was to measure the student’s stress state and then during the relaxation state after the exam period. Two algorithms were used for the data process, such as Linear Discriminant Analysis and Support Vector Machine through the Python software for the classiﬁcation and differentiation of the assessment, achieving 100% of classiﬁcation through GSR, 90% with the E-nose system proposed, 90% with the EMG system, and 88% success by using ECG, respectively. Throughout the tests, there was a ﬁve-minute time-lapse estimated for measuring, with signals acquired over a long time in a relaxation state where the sensor is set, keeping constant the shape of the wave without presenting novelties as long as the participant stays calm. The measures were acquired at an around 10 samples per second sampling rate for 5 min, the participant was told to avoid touching the electrode fastener and not to make movements with those ﬁngers. these were organized in an array where every signal was located in the rows and every signal ´ s data in the columns. This dataset was normalized using the “StandardScaler” function from the “ScikitLearn” library in Python programming language. This function can obtain the mean and scale the variance in a unitary way. In this stage, the LDA algorithm from the same library was applied, where the resulting LDA algorithm factors were used as training and validation by using the cross-validation method “k-folds”, with k = 5 for the Support Vector Machine (SVM) algorithm with the parameters correspondent to the linear kernel.


Introduction
Stress is considered as a physiological reaction in which different defense mechanisms interact during the confrontation of a situation or imminent threat that stimulates a fight or escape response of the body [1]. Lazarus and Folkman [2] define it as "A particular reaction between a person and his surroundings considered as threatening or that overpass the available resources and jeopardize his wellness". The academic stress comes out toward typical problems in the educational context, this can be a necessary and natural reaction for fulfilling the demands required and in which factors such as the academic overcharge, group projects, competitivity, lack of economic resources, and the deficient time organization take part [3]. Stress levels can increase in significant proportions in some students, especially during the exam period [4].
Additionally, academic stress is associated with different negative results on the person, including deficiencies in academic performance, the daily homework, as well as detriment in physical and mental health when the person is often involved in stressful situations [5,6]. Several types of research have been focused on the health changes related to academic stress, discovering results that suggest that the physical response to stress plays an important role in health and these must be considered in the design of Promotional Health Programs [7,8]. Mental health professionals think stress may help people to solve some problems, however, it makes people irritable, upset, and, in serious cases, stress keeps a close relation with diseases such as diabetes, depression, sleep disorders, heart illnesses, and gastric problems [9][10][11].
The quick spread of COVID-19 forced the closure of schools and universities all over the world, driving to a virtual form in the teaching method, continuing the educational Finally, we obtained informed consent by hand from the participants for the data acquisition and processing measures for the stress state during a virtual exam performance. For the relaxation state, some measurements were taken during the academic semester between March and June of 2020. The physiological measurements were acquired in the student's homes, following the necessary security actions like the use of overalls, masks, and disposable gloves.
It is necessary to mention that experiment and evaluation of the electronic devices developed in this research were proposed as a pilot study during the COVID-19 pandemic, therefore, the number of samples was not established or estimated as a target since the participation was limited to the availability of the students who were located in their current places of residence or elsewhere in the Pamplona city, making the sample collection much more efficient.

Galvanic Skin Response (GSR)
Skin conductance is one of the most used methods in psychophysiological research, it is also called skin electrical activity, and refers to each of the skin electrical properties as a response to the sweat secretion by sweat glands. Eccrine glands are mainly stimulated in response to emotional events such as stress, these glands are distributed over all of the body in low densities, with a larger concentration in the face, in palms of hands, soles of feet, and armpits, with the palms of hands being the preferred location for the GSR measurement [53]. Due to the existence of electrolytes in sweat, the electric resistance decreases, and the skin conductance increases-this response is directly associated with the sympathetic nervous system (SNS) that responds during emotionally stressful stimulus [58].
For taking the measurements, electrodes attached to a device were used, locating them on the index and middle finger on the participant's non-dominant hand (see Figure 1). Throughout the tests, there was a five-minute time-lapse estimated for measuring, with signals acquired over a long time in a relaxation state where the sensor is set, keeping constant the shape of the wave without presenting novelties as long as the participant stays calm. The measures were acquired at an around 10 samples per second sampling rate for 5 min, the participant was told to avoid touching the electrode fastener and not to make movements with those fingers. urement [53]. Due to the existence of electrolytes in sweat, the electric resistance de and the skin conductance increases-this response is directly associated with the thetic nervous system (SNS) that responds during emotionally stressful stimulus For taking the measurements, electrodes attached to a device were used, them on the index and middle finger on the participant's non-dominant hand (se 1). Throughout the tests, there was a five-minute time-lapse estimated for measuri signals acquired over a long time in a relaxation state where the sensor is set, constant the shape of the wave without presenting novelties as long as the pa stays calm. The measures were acquired at an around 10 samples per second s rate for 5 min, the participant was told to avoid touching the electrode fastener an make movements with those fingers.

Electronic Nose System (E-Nose)
An electronic nose system is based on the gas sensor's implementation with sensibility levels, located in a measuring camera; commonly, the electronic system bined with pattern recognition algorithms and artificial intelligence to find a chara profile that allows classifying VOCs. Usually, sensors based on metal oxide semi tors are among the most used due to the wide variety of compounds that it can de to its wide commercial diversification [59]. Figure 2 shows the measuring electronic circuit for the gas sensors. For the ge signal in each sensor, an Arduino Astar 32U4 card (Pololu, Las Vegas, NV, USA) "Pololu" company was connected to the circuit, it has 8 analog inputs with 10 bit tion. All used sensors share the same circuit configuration given by the manu however, the power consumption of every sensor is different according to their consumption since the sensor's matrix feeds from a direct voltage source of 5 V capacity. The sensor's voltage response is measured in the load resistance RL = 1

Electronic Nose System (E-Nose)
An electronic nose system is based on the gas sensor's implementation with different sensibility levels, located in a measuring camera; commonly, the electronic system is combined with pattern recognition algorithms and artificial intelligence to find a characteristic profile that allows classifying VOCs. Usually, sensors based on metal oxide semiconductors are among the most used due to the wide variety of compounds that it can detect and to its wide commercial diversification [59]. Figure 2 shows the measuring electronic circuit for the gas sensors. For the generated signal in each sensor, an Arduino Astar 32U4 card (Pololu, Las Vegas, NV, USA) made by "Pololu" company was connected to the circuit, it has 8 analog inputs with 10 bits resolution. All used sensors share the same circuit configuration given by the manufacturer; however, the power consumption of every sensor is different according to their electric consumption since the sensor's matrix feeds from a direct voltage source of 5 V with 2A capacity. The sensor's voltage response is measured in the load resistance RL = 1 kΩ. Metal Oxide (MOX) gas sensors manufactured by Hanwei and Figaro companies were used, where each sensor's information is shown in Table 2. The E-nose consists of a measuring chamber that comprises a sensor array that can detect the organic compounds which are controlled by pneumatic valves. The measuring chamber was made of stainless-   Table 2. The E-nose consists of a measuring chamber that comprises a sensor array that can detect the organic compounds which are controlled by pneumatic valves. The measuring chamber was made of stainlesssteel, which was connected to a vacuum pump with an independent power supply of 6 Volts Direct Current (VDC) and 500 mAh feedback, controlled from the Arduino device with a Bipolar Junction Transistor (BJT) (Ref: TIP41) transistor configured as a switch.  Figure 3 shows the vacuum pump feedback circuit to connect it with the Arduino card for supplying the necessary current. The VOC's were carried out to the measuring chamber through a piping circuit and using a vacuum pump. measuring chamber that comprises a sensor array that can detect the which are controlled by pneumatic valves. The measuring chamber wa steel, which was connected to a vacuum pump with an independent Volts Direct Current (VDC) and 500 mAh feedback, controlled from with a Bipolar Junction Transistor (BJT) (Ref: TIP41) transistor configu  Figure 3 shows the vacuum pump feedback circuit to connect i card for supplying the necessary current. The VOC's were carried ou chamber through a piping circuit and using a vacuum pump. The system controls the vacuum pump with the aim of limiting current to less than 15 mA, whereas the base resistance was calcu The system controls the vacuum pump with the aim of limiting the Arduino output current to less than 15 mA, whereas the base resistance R B was calculated with Equation (1). The above allows to extend the device's useful life and guarantee the transistor to keep in optimal working conditions.

Design
Moreover, the software was designed for data acquisition and visualization by the E-nose, which runs in a PC that sends and receives Arduino card information by serial communication. Figure 4 illustrates the E-nose scheme proposed for the response measuring and visualization of every gas sensor selected for detection of VOC's emitted by the skin, as possible stress indicators [44]. nose, which runs in a PC that sends and receives Arduino card information by serial communication. Figure 4 illustrates the E-nose scheme proposed for the response measuring and visualization of every gas sensor selected for detection of VOC's emitted by the skin, as possible stress indicators [44]. For getting a better VOC's concentration response, a metal funnel was located on the participant's forehead for 5 min, doing a pressure to avoid losing compounds on the outside. After 3 min passed since the funnel was located on the participant, a vacuum pump is activated for purging the measurement chamber, allowing the air passing of the environment. In this way, the heat dispersed by sensors is extracted, which could affect the measurement result. In this stage, valve 2 is closed and valve 1 is opened to purge the piping circuit. After 2 min of purging, valve 1 is closed for allowing the VOC's to pass through the measuring camera. Furthermore, the analogic Arduino outputs gather and send the information of every sensor via serial communication made by the PC, in which the skin sensor's behavior can be observed in real-time.

Electromyography System
The physiological variations on the muscle fiber membrane generate myoelectric signals that can be measured. Therefore, the technique used for measuring and analyzing these signals is known as electromyography. It is commonly performed through Ag-AgC1 electrodes that convert the muscle's ionic current into an electric current [41]. Generally, the amplitude voltage is within the range of ±5 mV and the frequency content ranges are from 6 Hz to 600 Hz, with a frequency dominant range from 20 Hz to 150 Hz [60].

Design
For this research, an instrumentation amplifier (AD620A) (Analog Devices, Wilmington, MA, USA) from the "Analog Devices" maker was used. This amplifier has a function called Common-Mode Rejection Ratio (CMRR) of high state from 120 dB to 130 dB with For getting a better VOC's concentration response, a metal funnel was located on the participant's forehead for 5 min, doing a pressure to avoid losing compounds on the outside. After 3 min passed since the funnel was located on the participant, a vacuum pump is activated for purging the measurement chamber, allowing the air passing of the environment. In this way, the heat dispersed by sensors is extracted, which could affect the measurement result. In this stage, valve 2 is closed and valve 1 is opened to purge the piping circuit. After 2 min of purging, valve 1 is closed for allowing the VOC's to pass through the measuring camera. Furthermore, the analogic Arduino outputs gather and send the information of every sensor via serial communication made by the PC, in which the skin sensor's behavior can be observed in real-time.

Electromyography System
The physiological variations on the muscle fiber membrane generate myoelectric signals that can be measured. Therefore, the technique used for measuring and analyzing these signals is known as electromyography. It is commonly performed through Ag-AgC1 electrodes that convert the muscle's ionic current into an electric current [41]. Generally, the amplitude voltage is within the range of ±5 mV and the frequency content ranges are from 6 Hz to 600 Hz, with a frequency dominant range from 20 Hz to 150 Hz [60].

Design
For this research, an instrumentation amplifier (AD620A) (Analog Devices, Wilmington, MA, USA) from the "Analog Devices" maker was used. This amplifier has a function called Common-Mode Rejection Ratio (CMRR) of high state from 120 dB to 130 dB with gain characteristics from 10 to 1000 and high impedance input: 10 GΩ, which can be applied in data gathering devices [61]. Equation (2) describes the gain calculation (G): where setting the R G = 100 Ω, an approximate gain equal to 495 was obtained. Furthermore, a filter stage was added for attenuating the noise induced by electromagnetic sources, movement, and even the participant's breathing [61]. Finally, the amplification stage was performed with a variable gain for the measure's taking. The acquisition measurement scheme can be observed in Figure 5. A Raspberry pi 3B+ card was used to control and acquire the EMG signals, coupling an ADS1015 module (Texas Instruments, Dallas, TX, USA) made by the "Texas Instruments" company. It has an Inter-Integrated Circuit (I2C) communication protocol, four analog inputs, and 3.3 kHz sample rates. = 49.4 kΩ + 1 (2 where setting the = 100 Ω, an approximate gain equal to 495 was obtained. Furthe more, a filter stage was added for attenuating the noise induced by electromagneti sources, movement, and even the participant's breathing [61]. Finally, the amplificatio stage was performed with a variable gain for the measure's taking. The acquisition measurement scheme can be observed in Figure 5. A Raspberry p 3B+ card was used to control and acquire the EMG signals, coupling an ADS1015 modul (Texas Instruments, Dallas, TX, USA) made by the "Texas Instruments" company. It ha an Inter-Integrated Circuit (I2C) communication protocol, four analog inputs, and 3.3 kH sample rates. For the EMG device, a filtering stage was used. It consists of two "Butterworth" typ filters of the second order, of −40 dB/decade. In consequence, when the set cutoff fre quency is overpassed 10 times, the filter output will be −40 dB concerning the input. Figur  6 shows the schematic circuit to eliminate the noise produced by the movement and th participant's breathing. A 30 Hz High-Pass Filter (HPF) in a voltage source configuratio controlled by voltage and a Low-Pass Filter with a 416 Hz set cutoff frequency were im plemented for the EMG system's development. The procedure for estimating each of the resistance values and filter circuit capacitor is described below.
Initially, a 416 Hz cutoff LPF frequency is established and an adequate value (prefe able commercial) is chosen for C1 between 100 pF and 0.1 uF, so that C1 = 10 nF, the valu For the EMG device, a filtering stage was used. It consists of two "Butterworth" type filters of the second order, of −40 dB/decade. In consequence, when the set cutoff frequency is overpassed 10 times, the filter output will be −40 dB concerning the input. Figure 6 shows the schematic circuit to eliminate the noise produced by the movement and the participant's breathing. A 30 Hz High-Pass Filter (HPF) in a voltage source configuration controlled by voltage and a Low-Pass Filter with a 416 Hz set cutoff frequency were implemented for the EMG system's development.
where setting the = 100 Ω, an approximate gain equal to 495 was obtained. Furthermore, a filter stage was added for attenuating the noise induced by electromagnetic sources, movement, and even the participant's breathing [61]. Finally, the amplification stage was performed with a variable gain for the measure's taking.
The acquisition measurement scheme can be observed in Figure 5. A Raspberry pi 3B+ card was used to control and acquire the EMG signals, coupling an ADS1015 module (Texas Instruments, Dallas, TX, USA) made by the "Texas Instruments" company. It has an Inter-Integrated Circuit (I2C) communication protocol, four analog inputs, and 3.3 kHz sample rates. For the EMG device, a filtering stage was used. It consists of two "Butterworth" type filters of the second order, of −40 dB/decade. In consequence, when the set cutoff frequency is overpassed 10 times, the filter output will be −40 dB concerning the input. Figure  6 shows the schematic circuit to eliminate the noise produced by the movement and the participant's breathing. A 30 Hz High-Pass Filter (HPF) in a voltage source configuration controlled by voltage and a Low-Pass Filter with a 416 Hz set cutoff frequency were implemented for the EMG system's development. The procedure for estimating each of the resistance values and filter circuit capacitors is described below.
Initially, a 416 Hz cutoff LPF frequency is established and an adequate value (preferable commercial) is chosen for C1 between 100 pF and 0.1 uF, so that C1 = 10 nF, the value The procedure for estimating each of the resistance values and filter circuit capacitors is described below.
Initially, a 416 Hz cutoff LPF frequency is established and an adequate value (preferable commercial) is chosen for C1 between 100 pF and 0.1 uF, so that C1 = 10 nF, the value of C2 = 2 × C1 value is set; finally, the value between R1 and R2 is given by Equation (3), where Wc is the cutoff frequency in radians/second. In this stage, the values between R3 and R4 must make the double of R1 [62]: Electronics 2021, 10, 301 9 of 23 Owing to the movement or sudden vibrations in the EMG system, participants' breathing can induce a Direct Current (DC) compound in the EMG gathered signal [61]; for suppressing it, the 30 Hz cutoff HPF frequency was set, calculating the filter parameters in this way: a C3 = C4 = 1 uF value is chosen. In this case, the R7 value is given by the Equation (4), where R6 = R7 ÷ 2 and the commercial closest resistance was 3.3 kΩ. Finally, for minimizing the deflected DC, the condition R5 = R7 is set [62].

Measurement Protocol
The electrode setting for EMG surface measuring was made by following the guidelines in the SENIAM regulation for the EMG measuring in upper trapezius muscle [63], as is shown in Figure 7. The electrodes were obtained from the "LifeCare" company, composed of an Ag/AgCI electrode with solid gel for improving conduction, snap connection where these electrodes are hypoallergenic.

= × = 27 kΩ
Owing to the movement or sudden vibrations in the EMG system breathing can induce a Direct Current (DC) compound in the EMG gathe for suppressing it, the 30 Hz cutoff HPF frequency was set, calculating the ters in this way: a C3 = C4 = 1 uF value is chosen. In this case, the R7 value Equation (4), where R6 = R7 ÷ 2 and the commercial closest resistance was for minimizing the deflected DC, the condition R5 = R7 is set [62].

Measurement Protocol
The electrode setting for EMG surface measuring was made by follow lines in the SENIAM regulation for the EMG measuring in upper trapeziu as is shown in Figure 7. The electrodes were obtained from the "LifeCare" c posed of an Ag/AgCI electrode with solid gel for improving conduction, sn where these electrodes are hypoallergenic. The data acquired was taken through a graphic interface over the "R ronment with Python language using the Tkinter library (see Figure 5) measures in each session were acquired, from stress and relaxation time 1000 samples per second sample rate. Likewise, a sample was acquired du tion of a voluntary movement (i.e., in the shoulder) in every student, for th a measurements group and data sorting. It is important to clarify that th tests were not performed on female students since they expressed it would able and risky during the COVID-19 pandemic. Thus, the above could take about the acquired measures by the other systems [64,65].

Heart Rate Variability
For the physiological signals' gathering, two channels were used, loca electrode, as is shown in Figure 8. For the signal ECG gathering, the AD module (Texas Instruments, Dallas, TX, USA) from "Texas Instruments" set. This module has 8 analog input channels, and it allows measuring the 12 derivations with a 24 bits resolution. The data acquired was taken through a graphic interface over the "Raspbian" environment with Python language using the Tkinter library (see Figure 5). In total, two measures in each session were acquired, from stress and relaxation time for 15 s with a 1000 samples per second sample rate. Likewise, a sample was acquired during the execution of a voluntary movement (i.e., in the shoulder) in every student, for then establishing a measurements group and data sorting. It is important to clarify that the EMG system tests were not performed on female students since they expressed it would be uncomfortable and risky during the COVID-19 pandemic. Thus, the above could take on uncertainty about the acquired measures by the other systems [64,65].

Heart Rate Variability
For the physiological signals' gathering, two channels were used, locating the clamp electrode, as is shown in Figure 8. For the signal ECG gathering, the ADS1298ECG-FE module (Texas Instruments, Dallas, TX, USA) from "Texas Instruments" company was set. This module has 8 analog input channels, and it allows measuring the ECG signal in 12 derivations with a 24 bits resolution. Electronics 2021, 10, x FOR PEER REVIEW 10 of 24 For the R points' location and setting in the ECG gathered signals, an algorithm was designed in Python, leaning on the derivative (see Equation (5)) of the ECG signal, which allows finding the precise localization of each R peak, giving robustness when compounding the HRV signal. Once all R peaks are detected and the knowledge of sample rate with which ECG was acquired, the time between every R peak can be determined; moreover, the concatenation between R peaks represents the HRV signal.

Processing Methods
The following methods were used for data analysis.

Linear Discriminant Analysis (LDA)
The linear discriminant analysis (LDA) is a data analysis method, it was proposed for binary class problems in 1936 by Fisher [66]. Its goal is to find the data projection that minimizes the variance and maximizes the distance between each class of each measurement that compounds the dataset, guaranteeing in that way the maximum separability between classes, projecting the original matrix data in an inferior dimensional space similar to the PCA (Principal Compounds Analysis). This technique can be used in reduction dimensionality problems such as the previous step for the pattern's sorting and automatic learning [67]. The algorithm calculates the separability between different classes (this is known as between-class variance), next, it determines the distance between the mean and each class sample, known as within-class variance or within-class matrix. Finally, the algorithm constructs an inferior dimensional space that maximizes the variation within the class [68].

K Nearest Neighbors (KNN)
The K nearest neighbors (KNN) is an automatic learning method, based on a distance function that determines the similarity or difference between two distances. Commonly, the Euclidean distance is used (see Equation (6)), each distance x is represented by an attribute vector < 1 ( ), 2 ( ), … , ( ) >, where ai (x) describes the i-th x attribute value [69]: For the R points' location and setting in the ECG gathered signals, an algorithm was designed in Python, leaning on the derivative (see Equation (5)) of the ECG signal, which allows finding the precise localization of each R peak, giving robustness when compounding the HRV signal. Once all R peaks are detected and the knowledge of sample rate with which ECG was acquired, the time between every R peak can be determined; moreover, the concatenation between R peaks represents the HRV signal.

Processing Methods
The following methods were used for data analysis.

Linear Discriminant Analysis (LDA)
The linear discriminant analysis (LDA) is a data analysis method, it was proposed for binary class problems in 1936 by Fisher [66]. Its goal is to find the data projection that minimizes the variance and maximizes the distance between each class of each measurement that compounds the dataset, guaranteeing in that way the maximum separability between classes, projecting the original matrix data in an inferior dimensional space similar to the PCA (Principal Compounds Analysis). This technique can be used in reduction dimensionality problems such as the previous step for the pattern's sorting and automatic learning [67]. The algorithm calculates the separability between different classes (this is known as between-class variance), next, it determines the distance between the mean and each class sample, known as within-class variance or within-class matrix. Finally, the algorithm constructs an inferior dimensional space that maximizes the variation within the class [68].

K Nearest Neighbors (KNN)
The K nearest neighbors (KNN) is an automatic learning method, based on a distance function that determines the similarity or difference between two distances. Commonly, the Euclidean distance is used (see Equation (6)), each distance x is represented by an attribute vector a 1 (x), a 2 (x), . . . , a n (x) , where a i (x) describes the i-th x attribute value [69]: Electronics 2021, 10, 301

of 23
The performance of the algorithm depends mainly on the distance metric used for the identification of the nearest neighbors and the number of set neighbors, it shows better results when it is applied on large datasets and with reduced dimensions [70].
2.6.3. SVM Support Vector Machine (SVM) was developed in 1995 by Cortes and Vapnik for binary sorting [71], it is focused on the class separation and ideals separation hyperplane that maximizes the margin between the closest points within the classes [72]. When a linear separator cannot be found, the dataset becomes separable linearly (this projection is made through kernel techniques). Like other supervised learning algorithms, the SVM is trained with a labeled dataset that gives a learning base for the future data sorting, assigning them to a group or another one that is separated [73].

SISCO Inventory
The SISCO inventory is a psychometric instrument that allows measuring the stress level in university students. It can be self-taken, and it can be responded to in an individual or group setting. It is structured in 37 items that allow: Identifying if the survey respondent is an adequate candidate or not by answering the inventory (he can be the right candidate if during the semester he has had worriment or nervousness, if not, he cannot be a candidate), determining the academic stress level intensity, and identifying the environmental demands that are considered stressing stimulus by the survey respondent [74]. Its application was made virtually by qualified staff from the University of Pamplona from the Psychology program. Furthermore, every participant gave their permission for the use of the questionnaires through an informed consent which was shown to them before starting the academic stress inventory application, SISCO.

GSR Responses
Immediately after the location of the electrodes on the fingers, the signal registered by the sensor response was started, and the sensor response was measured in volts. Figure 9 shows the response acquired during the relaxation state. Therefore, the initial measured value of the skin electric characteristic of each person is different for its physical features, however, it was observed that during the test, the resultant wave-shape behavior always tends to be logarithmic when the participant is in a relaxation state.
The performance of the algorithm depends mainly on the distance metric u the identification of the nearest neighbors and the number of set neighbors, it show results when it is applied on large datasets and with reduced dimensions [70].
2.6.3. SVM Support Vector Machine (SVM) was developed in 1995 by Cortes and Vapni nary sorting [71], it is focused on the class separation and ideals separation hyp that maximizes the margin between the closest points within the classes [72]. Wh ear separator cannot be found, the dataset becomes separable linearly (this proj made through kernel techniques). Like other supervised learning algorithms, the trained with a labeled dataset that gives a learning base for the future data sorting ing them to a group or another one that is separated [73].

SISCO Inventory
The SISCO inventory is a psychometric instrument that allows measuring th level in university students. It can be self-taken, and it can be responded to in an ual or group setting. It is structured in 37 items that allow: Identifying if the su spondent is an adequate candidate or not by answering the inventory (he can be t candidate if during the semester he has had worriment or nervousness, if not, he be a candidate), determining the academic stress level intensity, and identifying t ronmental demands that are considered stressing stimulus by the survey respond Its application was made virtually by qualified staff from the University of Pa from the Psychology program. Furthermore, every participant gave their permis the use of the questionnaires through an informed consent which was shown to t fore starting the academic stress inventory application, SISCO.

GSR Responses
Immediately after the location of the electrodes on the fingers, the signal re by the sensor response was started, and the sensor response was measured in volts 9 shows the response acquired during the relaxation state. Therefore, the initial m value of the skin electric characteristic of each person is different for its physical f however, it was observed that during the test, the resultant wave-shape behavior tends to be logarithmic when the participant is in a relaxation state.  Figure 10 shows the GSR signal plot acquired from the same participant du measuring, corresponding to the stress state where non-random amplitude va were observed, and an important difference in comparison to the wave shape obt the relaxation state measurement that allows discriminating visually between bot  Figure 10 shows the GSR signal plot acquired from the same participant during the measuring, corresponding to the stress state where non-random amplitude variations were observed, and an important difference in comparison to the wave shape obtained in the relaxation state measurement that allows discriminating visually between both states. In an empirical way, it was set at a 5 min minimum period for the GSR sign uring, since the measurements taken during shorter periods can generate wrong i tations.

GSR Data Processing
After the acquisition of GSR signals, these were organized in an array whe signal was located in the rows and every signal´s data in the columns. This data normalized using the "StandardScaler" function from the "ScikitLearn" library in programming language. This function can obtain the mean and scale the varia unitary way. In this stage, the LDA algorithm from the same library was applied the resulting LDA algorithm factors were used as training and validation by u cross-validation method "k-folds", with k = 5 for the Support Vector Machine (S gorithm with the parameters correspondent to the linear kernel. Figure 11 illustrates the graphic representation of the SVM algorithm respo the distance to the hyperplane according to the samples analyzed. Through this a 100% success rate of classification with the GSR signal was obtained.

E-Nose Responses
From each measure taken by the E-nose, 8 signals corresponding to each ga that comprises the sensor array were stored. Each sensor response was measured In an empirical way, it was set at a 5 min minimum period for the GSR signal measuring, since the measurements taken during shorter periods can generate wrong interpretations.

GSR Data Processing
After the acquisition of GSR signals, these were organized in an array where every signal was located in the rows and every signal s data in the columns. This dataset was normalized using the "StandardScaler" function from the "ScikitLearn" library in Python programming language. This function can obtain the mean and scale the variance in a unitary way. In this stage, the LDA algorithm from the same library was applied, where the resulting LDA algorithm factors were used as training and validation by using the crossvalidation method "k-folds", with k = 5 for the Support Vector Machine (SVM) algorithm with the parameters correspondent to the linear kernel. Figure 11 illustrates the graphic representation of the SVM algorithm response, and the distance to the hyperplane according to the samples analyzed. Through this method, a 100% success rate of classification with the GSR signal was obtained. In an empirical way, it was set at a 5 min minimum period for the GSR sign uring, since the measurements taken during shorter periods can generate wrong i tations.

GSR Data Processing
After the acquisition of GSR signals, these were organized in an array whe signal was located in the rows and every signal´s data in the columns. This data normalized using the "StandardScaler" function from the "ScikitLearn" library in programming language. This function can obtain the mean and scale the varia unitary way. In this stage, the LDA algorithm from the same library was applied the resulting LDA algorithm factors were used as training and validation by u cross-validation method "k-folds", with k = 5 for the Support Vector Machine (S gorithm with the parameters correspondent to the linear kernel. Figure 11 illustrates the graphic representation of the SVM algorithm respo the distance to the hyperplane according to the samples analyzed. Through this a 100% success rate of classification with the GSR signal was obtained.

E-Nose Responses
From each measure taken by the E-nose, 8 signals corresponding to each ga that comprises the sensor array were stored. Each sensor response was measured the lineal base was eliminated in every signal and they were stored concatenati signals in a single vector; in that order, every sensor was labeled. Thus, the vecto sponding to every participant's measurements were stored in a matrix, and aft they were standardized using the "StandardScaler" instruction. It is important to that standardizing is a common requirement for many automatic learning algorith

E-Nose Responses
From each measure taken by the E-nose, 8 signals corresponding to each gas sensor that comprises the sensor array were stored. Each sensor response was measured in volts: the lineal base was eliminated in every signal and they were stored concatenating the 8 signals in a single vector; in that order, every sensor was labeled. Thus, the vectors corresponding to every participant's measurements were stored in a matrix, and afterward, they were standardized using the "StandardScaler" instruction. It is important to mention that standardizing is a common requirement for many automatic learning algorithms. The "ScikitLearn" library was applied to the standardized dataset. Figure 12a depicts the response in Two-Dimensional (2D), where the orange color represents the samples corresponding to the measurement in a stress state, and in blue, the samples acquired in a relaxation state. laxation state.
The two first compounds resulting from the dimensionality reduction made by the PCA over the dataset and which represents the 80% variance of data were used for training and validation, applying the cross-validation method "k-folds", with k = 5 from the k nearest neighbors algorithm (KNN). Furthermore, they were established as an input argument: n-neighbors = 5, metric = 'minkowski', and p = 2, where n-neighbors corresponds to the number of neighbors that are going to use the algorithm for data aggrupation. By using the Euclidean distance, 88.9% precision was attained in the measures sorting. On the other hand, another additional result was obtained with the PCA which was used for the input of SVM algorithm training and testing by the cross-validation method "k-folds", with k = 5. The linear kernel was set and 90% was obtained in the measurements classification, in Figure 12b is shown the graphic of each sample according to the hyperplane distance.  Figure 13 depicts an upper trapezius muscles EMG signal when a voluntary shoulder movement is done. In Figure 13a, the amplified signal is shown without filtering, and in Figure 13b, the filtered signal, the DC compound has been suppressed and the measure´s variable is given in DC volts, where the signal was acquired during 10 s with a 1000 samples/s sampling rate. The two first compounds resulting from the dimensionality reduction made by the PCA over the dataset and which represents the 80% variance of data were used for training and validation, applying the cross-validation method "k-folds", with k = 5 from the k nearest neighbors algorithm (KNN). Furthermore, they were established as an input argument: n-neighbors = 5, metric = 'minkowski', and p = 2, where n-neighbors corresponds to the number of neighbors that are going to use the algorithm for data aggrupation. By using the Euclidean distance, 88.9% precision was attained in the measures sorting. On the other hand, another additional result was obtained with the PCA which was used for the input of SVM algorithm training and testing by the cross-validation method "k-folds", with k = 5. The linear kernel was set and 90% was obtained in the measurements classification, in Figure 12b is shown the graphic of each sample according to the hyperplane distance. Figure 13 depicts an upper trapezius muscles EMG signal when a voluntary shoulder movement is done. In Figure 13a, the amplified signal is shown without filtering, and in Figure 13b, the filtered signal, the DC compound has been suppressed and the measure s variable is given in DC volts, where the signal was acquired during 10 s with a 1000 samples/s sampling rate.

EMG Responses
The measurements acquired during a voluntary movement were also used in the feature extraction for the sorting training algorithm. By including the voluntary movement characteristics, we can validate that during the stress and relaxation state, the EMG device was not altered voluntarily. The extracted features for these three labels were: mean absolute value (MAV), waveform length (WL), zero crossings (ZC), slope sign changes (SSC), the variance of EMG (VAR), log detector (LD), difference absolute mean value (DAMV), difference absolute standard deviation value (DASDV), difference variance value (DVARV), and average amplitude change (AAC), described in detail in Reference [75]. All these features were concatenated in a vector for each sample, all taken measures' features were stored in a matrix and they were normalized with the "StandardScaler" function. Afterward, the LDA algorithm was implemented for the processing of the dataset. The measurements acquired during a voluntary movement were also used in the feature extraction for the sorting training algorithm. By including the voluntary movement characteristics, we can validate that during the stress and relaxation state, the EMG device was not altered voluntarily. The extracted features for these three labels were: mean absolute value (MAV), waveform length (WL), zero crossings (ZC), slope sign changes (SSC), the variance of EMG (VAR), log detector (LD), difference absolute mean value (DAMV), difference absolute standard deviation value (DASDV), difference variance value (DVARV), and average amplitude change (AAC), described in detail in Reference [75]. All these features were concatenated in a vector for each sample, all taken measures' features were stored in a matrix and they were normalized with the "StandardScaler" function. Afterward, the LDA algorithm was implemented for the processing of the dataset. Figure 14 illustrates the algorithm response where the resulting factors were crossed with the SVM algorithm with lineal kernel and using the cross-validation method "kfolds", with k = 5, achieving a 90% success rate of classification over the EMG features set. Regarding EMG signal, the scatter plot (see Figure 14) shows that the classes that make up the dataset of the acquired measures (i.e., relaxation, voluntary movement, and stress) are significantly separable, this indicates that the characteristics extracted from each signal in the time domain provide relevant information for pattern recognition of EMG signals. Besides, different investigations have used this type of time-domain charac-  Figure 14 illustrates the algorithm response where the resulting factors were crossed with the SVM algorithm with lineal kernel and using the cross-validation method "k-folds", with k = 5, achieving a 90% success rate of classification over the EMG features set. The measurements acquired during a voluntary movement were also used in ture extraction for the sorting training algorithm. By including the voluntary m characteristics, we can validate that during the stress and relaxation state, the EM was not altered voluntarily. The extracted features for these three labels were: m solute value (MAV), waveform length (WL), zero crossings (ZC), slope sign (SSC), the variance of EMG (VAR), log detector (LD), difference absolute mea (DAMV), difference absolute standard deviation value (DASDV), difference value (DVARV), and average amplitude change (AAC), described in detail in R [75]. All these features were concatenated in a vector for each sample, all taken m features were stored in a matrix and they were normalized with the "Standar function. Afterward, the LDA algorithm was implemented for the processing o taset. Figure 14 illustrates the algorithm response where the resulting factors were with the SVM algorithm with lineal kernel and using the cross-validation me folds", with k = 5, achieving a 90% success rate of classification over the EMG feat Regarding EMG signal, the scatter plot (see Figure 14) shows that the cla make up the dataset of the acquired measures (i.e., relaxation, voluntary movem stress) are significantly separable, this indicates that the characteristics extract each signal in the time domain provide relevant information for pattern recog EMG signals. Besides, different investigations have used this type of time-domain Regarding EMG signal, the scatter plot (see Figure 14) shows that the classes that make up the dataset of the acquired measures (i.e., relaxation, voluntary movement, and stress) are significantly separable, this indicates that the characteristics extracted from each signal in the time domain provide relevant information for pattern recognition of EMG signals. Besides, different investigations have used this type of time-domain characteristics for the identification and classification of muscle movements in different scenarios of daily life [15,76,77]. For applying the LDA and SVM, the dataset was normalized previously with the "StandardScaler" function.

HRV Responses
The ECG module contains a programmable gain amplifier and a sample rate of 500 samples/s, allowing the ECG signal acquisition in an optimal resolution, as is shown clearly in Figure 15. Besides, in the data gathering and visualization software, digital filters with a variable cutoff frequency are included, with control and monitoring in real-time of the resulting signal varying the filter parameters.

HRV Responses
The ECG module contains a programmable gain amplifier and a sample rate of 500 samples/s, allowing the ECG signal acquisition in an optimal resolution, as is shown clearly in Figure 15. Besides, in the data gathering and visualization software, digital filters with a variable cutoff frequency are included, with control and monitoring in realtime of the resulting signal varying the filter parameters. Figure 15. ECG signal acquired. Figure 16 shows the resulting signals in each stage of the developed algorithm, where Figure 16a is the ECG signal, previously filtered, Figure 16b is the derivative of the ECG signal, Figure 16c is the ECG signal's sample with the R peaks detected by the algorithm marked with an asterisk, and Figure 16d is the HRV signal sample extracted from the ECG signal, where amplitude represents the time between R peaks and the horizontal shaft shows the extracted R points.   Figure 16 shows the resulting signals in each stage of the developed algorithm, where Figure 16a is the ECG signal, previously filtered, Figure 16b is the derivative of the ECG signal, Figure 16c is the ECG signal's sample with the R peaks detected by the algorithm marked with an asterisk, and Figure 16d is the HRV signal sample extracted from the ECG signal, where amplitude represents the time between R peaks and the horizontal shaft shows the extracted R points.

HRV Responses
The ECG module contains a programmable gain amplifier and a sample rate of 500 samples/s, allowing the ECG signal acquisition in an optimal resolution, as is shown clearly in Figure 15. Besides, in the data gathering and visualization software, digital filters with a variable cutoff frequency are included, with control and monitoring in realtime of the resulting signal varying the filter parameters. Figure 15. ECG signal acquired. Figure 16 shows the resulting signals in each stage of the developed algorithm, where Figure 16a is the ECG signal, previously filtered, Figure 16b is the derivative of the ECG signal, Figure 16c is the ECG signal's sample with the R peaks detected by the algorithm marked with an asterisk, and Figure 16d is the HRV signal sample extracted from the ECG signal, where amplitude represents the time between R peaks and the horizontal shaft shows the extracted R points.  After acquiring all ECG measurements during two minutes with a sample rate of 500 samples/s, the algorithm developed in Python was implemented for the HRV signal extraction. Figure 17a shows the ECG acquired signal in relaxation state with all R points identified; in Figure 17b, the HRV signal extracted from 17a is shown, where the amplitude represents the time between R intervals, and it is measured in seconds. On the other hand, Figure 17c illustrates the ECG acquired signal from the same participant in the measure taken in the stress state, and Figure 17d shows the HRV signal extracted from 17c. samples/s, the algorithm developed in Python was implemented for the HRV signal extraction. Figure 17a shows the ECG acquired signal in relaxation state with all R points identified; in Figure 17b, the HRV signal extracted from 17a is shown, where the amplitude represents the time between R intervals, and it is measured in seconds. On the other hand, Figure 17c illustrates the ECG acquired signal from the same participant in the measure taken in the stress state, and Figure 17d shows the HRV signal extracted from 17c. The vectors that contain the HRV signal were comprised by the RR intervals of the ECG signal in the time domain, which were stored in a matrix in which the HRV signals are in the rows and every amplitude signal is in the columns. For using the SVM algorithm, the LDA factors were used as training and validation by using the cross-validation method "k-folds", with k = 5 for the Support Vector Machines (SVM) algorithm. Finally, 88% of classification was obtained, and the algorithm's response is shown in Figure 18.

SISCO Method Analysis
Through SISCO inventory, it is possible to distinguish three stress levels, these levels are determined through the intensity with which a situation is considered as stressful from the student's perspective. According to the score obtained after the test performance, these levels can be sorted as follows: 0 to 33 indicates a mild stress level, 34 to 66 represents a moderate stress level, and scores between 67 to 100 are considered a deep academic stress level. The vectors that contain the HRV signal were comprised by the RR intervals of the ECG signal in the time domain, which were stored in a matrix in which the HRV signals are in the rows and every amplitude signal is in the columns. For using the SVM algorithm, the LDA factors were used as training and validation by using the cross-validation method "k-folds", with k = 5 for the Support Vector Machines (SVM) algorithm. Finally, 88% of classification was obtained, and the algorithm's response is shown in Figure 18. tude represents the time between R intervals, and it is measured in seconds. On the othe hand, Figure 17c illustrates the ECG acquired signal from the same participant in th measure taken in the stress state, and Figure 17d shows the HRV signal extracted from 17c. The vectors that contain the HRV signal were comprised by the RR intervals of th ECG signal in the time domain, which were stored in a matrix in which the HRV signa are in the rows and every amplitude signal is in the columns. For using the SVM algo rithm, the LDA factors were used as training and validation by using the cross-validatio method "k-folds", with k = 5 for the Support Vector Machines (SVM) algorithm. Finally 88% of classification was obtained, and the algorithm's response is shown in Figure 18.

SISCO Method Analysis
Through SISCO inventory, it is possible to distinguish three stress levels, these leve are determined through the intensity with which a situation is considered as stressfu from the student's perspective. According to the score obtained after the test performanc these levels can be sorted as follows: 0 to 33 indicates a mild stress level, 34 to 66 represen a moderate stress level, and scores between 67 to 100 are considered a deep academ stress level. − − Figure 18. The graphical response of the HRV signal classification algorithm.

SISCO Method Analysis
Through SISCO inventory, it is possible to distinguish three stress levels, these levels are determined through the intensity with which a situation is considered as stressful from the student's perspective. According to the score obtained after the test performance, these levels can be sorted as follows: 0 to 33 indicates a mild stress level, 34 to 66 represents a moderate stress level, and scores between 67 to 100 are considered a deep academic stress level.
Consequently, according to the global psychometric data obtained in the psychological analysis, in the group of student survey respondents, 4% show mild stress levels, 64% are at a moderate stress level, and the remaining 32% show intense stress levels, as represented in Figure 19.
Consequently, according to the global psychometric dat ical analysis, in the group of student survey respondents, 4% are at a moderate stress level, and the remaining 32% show i sented in Figure 19. The following information was obtained among the mo equal to 55.44, which keeps within the range equivalent to median, and a 45 points mode. Thus, the standard deviatio the variance coefficient was 23.
Through the questionnaire, a 10-item "Likert-scale" was ronmental demands which are perceived as stressful stimulu are shown in the relevant order: Teacher´s exams, homework the answers wrong, which were the most representative stre academic performance.
Besides, in the 18-item section from the SISCO inventor toward the stressful stimulus are indicated, among which concentration problems, and headaches. These were the sym quency among the students. The confrontation strategies we tion, among which stood out: Defending their ideas without d oration, homework execution, and information searching ab sional assistance searching was one of the strategies with whi least related to.
On the other hand, Table 3 illustrates the results of the S signals which depicted that there was a correlation between t Deep, Moderate, and Mild), where the maximum and minim nal were extracted to determine the sensitivity of the device the analysis was performed with four devices used in this stu sen for the correlation of the data since it revealed a better cla the results. Therefore, it can be seen that there is a correlation the signal measured by the electronic device. It can be seen different stress levels obtained by SISCO can be almost comp sitivities calculated from the GSR signals. To perform the co sensitivity of each of the electrode signals was determined. T Figure 19. Level of stress in the study population.
The following information was obtained among the most representative data: mean equal to 55.44, which keeps within the range equivalent to moderate stress, a 54 grades median, and a 45 points mode. Thus, the standard deviation was equalized to 12.8, and the variance coefficient was 23.
Through the questionnaire, a 10-item "Likert-scale" was applied to identify the environmental demands which are perceived as stressful stimulus. In this section, the results are shown in the relevant order: Teacher s exams, homework overload, and fear of getting the answers wrong, which were the most representative stressful factors in the students' academic performance.
Besides, in the 18-item section from the SISCO inventory, the symptoms or reactions toward the stressful stimulus are indicated, among which are sleep disorders, anxiety, concentration problems, and headaches. These were the symptoms shown with more frequency among the students. The confrontation strategies were assessed in an 8-item section, among which stood out: Defending their ideas without damaging others, a plan elaboration, homework execution, and information searching about the situation. The professional assistance searching was one of the strategies with which the participating students least related to.
On the other hand, Table 3 illustrates the results of the SISCO inventory and the GSR signals which depicted that there was a correlation between the different stress levels (i.e., Deep, Moderate, and Mild), where the maximum and minimum values from the GSR signal were extracted to determine the sensitivity of the device. It should be clarified, once the analysis was performed with four devices used in this study, the GSR device was chosen for the correlation of the data since it revealed a better classification and correlation of the results. Therefore, it can be seen that there is a correlation both in the SISCO data and the signal measured by the electronic device. It can be seen that the results of the three different stress levels obtained by SISCO can be almost completely matched with the sensitivities calculated from the GSR signals. To perform the comparison of the results, the sensitivity of each of the electrode signals was determined. Therefore, the range taken for this study was 100%, which was related to the highest value obtained in the sensitivities, which was 0.5. Thus, the range of 0-0.165 was established, which corresponds to the mild level of stress, from 0.17-0.33 to moderate stress, and finally, for a deep level of stress, which was 0.335-0.5.
Therefore, among the SISCO tests and the response of the GSR signals, it was possible to obtain a 92% success rate since only two signals (C and X) were not matched correctly.

Discussion
Significant variations were found in the measurements taken during an exam performance in comparison to the other measurements taken in relaxation state through the EMG signal features. The above was because the EMG measurement response changes from one person to another due to their physical characteristics such as body fat percentage, the age, the kind of sports activities done by the students, or the lack of activities that include physical effort. Therefore, it is necessary to count on an efficient normalization method that allows reducing the variability in the EMG signal features, and in this way, responses could be improved through the classification algorithms.
Besides, it can be mentioned that different factors may occur in the GSR signal response, such as a participant's deep breathing execution while the signal is being taken, which can induce an amplitude decrease during the measuring time. For that reason, every student was told to abstain from doing deep breathing during the 5 min measuring time. However, in these cases, the sample was dismissed and was taken again.
In the same way, it was demonstrated that a slight decrease in the HRV response amplitude was acquired in the stress state measures in comparison to relaxation states per student. The above is in line with the hypothesis in which the stressing events induce an increase in the heart rate [16]. However, for future research, it would be important to consider the use of adhesive electrodes that allow a better conductivity, since some students have shown ECG signals with very low intensity or amplitude, which is why it was necessary to modify the algorithm for keeping the R peaks right and recognizing the ECG signal.
Concerning the E-nose response, the commercial sensors selected were capable of detecting the Volatile Organic Compounds emitted by skin, since it was possible to make a differentiation in various categories. We can highlight that these kind of sensors are also sensible to VOC's presence in breath [78]. That is why it is important to handle the E-nose system carefully for avoiding the VOC's emitted by breathing becoming absorbed in the measuring time, inducing possible confounding factors in the acquired data. The low cost of commercial gas sensors based on metal oxide semiconductors are a good option for their use in multisensorial systems with biomedical applications, in addition to their diversified use in industrial applications [79,80].
It is worth considering that the psychosocial and physical aspects of daily life, such as family, economic, and behavior problems, as well as events that happen in our environment and that we cannot control, for example, in the job, such as an overload of work or being fired, can generate alterations in the student s mental state. Moreover, other factors such as personal problems with teachers, low academic performance, the loss of a loved one, domestic violence, as well as psychoactive substances consumption, medicines, and unbalanced feeding, can alter the body's response toward different environmental stimulus [81,82]. Experiences like these increase the importance of knowing the current profile and situations that the participant volunteers have been through in this research since it is possible to minimize wrong judgments that can hamper the research advancements and the evaluation of new technologies.
It is important to clarify that the LDA pattern recognition method was used with two main factors (F1 and F2) to apply the SVM algorithm. Therefore, the success rate in data classification was good because the best information was obtained from the dataset before applying SVM. This study was conducted to detect the level of stress during the COVID-19 pandemic as a pilot study carried out on engineering students at the University of Pamplona, where it was not easy to acquire the samples due to the risks caused by COVID-19 at the time of acquiring the samples, and the lack of cooperation on the part of the students. However, some students agreed to perform the different tests with the electronic devices during the virtual exam. It is noteworthy that one of the key points for the success in the acquisition and classification of the samples was because of the fact that the students had two quite noticeable options, one was to think about being able to pass the exam and the other to lose it. Thus, these two scenarios generated in them quite noticeable stress in either of the two situations during the exam.
Consequently, the present study generated interesting results through the detection of stress in academic contexts. Therefore, we can say that this study is the first to be carried out on this subject.
Indeed, there are many articles on the topic of stress using electronic devices and where good results have been obtained in the classification of the measures. For instance, one study investigated the efficacy of the data fusion from off-the-shelf sensors to accurately determine stress in humans, in this case, the SVM reached 100% [83]. In another work, SVM was applied for mental stress detection in University Students. The algorithm obtained 100% specificity to classify the dataset [84].
It is necessary to highlight that no work was found in relation to the combination of LDA + SVM for stress classification.
In this study, we want to highlight the Quintero-Posada and Chon article as they have done a study with different alternative techniques, like spectral analysis, which have emerged as potential tools for the analysis of the electrodermal activity (EDA). These new methods and tools may help us to generate new applications in the future by using the signals information obtained from the electronic devices to monitor the mood or stress of a person for short-and long-duration data records [85].

Conclusions
Through the methodology proposed by using electronic devices, it was possible to get a high precision in the acquired data corresponding to every state (stress and relaxation state), because these were used in a real situation in which the student was taking a virtual exam. According to the SISCO inventory results of the academic stress, the exams are considered a stressing agent with the most relevance in the academic population.
With the GSR device, a better response could be obtained for detecting stress. Therefore, a 100% success rate was obtained in data classification, and moreover, we can mention that this device is still one of the most efficient methods for stress detection. This system also allowed recognizing, visually from the gathered wave-shape, the participant's state (stressed or relaxed). However, it is important to keep on classifying the GSR response wave-shape in different situations and environments, since some researchers suggest that the state of mind can alter the GSR signal amplitude [82]. Consequently, it would be important that in future research, each person's characteristics can be defined before its use.
With the VOC's detection technology from the E-nose, 90% classification was obtained; besides, this could be incremented with the application of more advanced pattern recognition techniques. However, though good results were obtained, and it has been the first research conducted for measuring stress in a pandemic situation, it would be worthwhile to compare the proposed system functioning with classical gas analysis techniques such as gas chromatography and mass spectrometry (GC-MS), for being able to validate the proposed protocol adequately.
In this research, only the HRV signal response was assessed with the single-channel device designed, so, for future investigations, a two-channel device could be implemented that allows acquiring the EMG response of the two shoulders to obtain more information that could increase to a 90% success rate and continue exploring a better measurement protocol, since the resulting signal amplitude could be affected for the electrode responses.
Finally, from the HRV signal response, 88% of data classification was achieved, confirming that ECG systems are still a good option in psychophysiological research that expects to measure the person's physiological behavior.
We want to mention that the most significant aim of the study was to try to detect the academic stress during the COVID-19 pandemic by using different kinds of electronic devices, as we wanted to see how the virtual exams could generate stress in the university students and which of those devices could be more efficient to detect it. Thus, it is very important to comment that there were a lot of issues with regards to the sample collections because the students were quite scared, and they did not have much time to collaborate with this study because they had exams. However, at the end of this study, the students agreed and they were willing to participate in the different experiments to obtain results.
Nonetheless, despite the limited number of samples acquired due to the pandemic period and to the difficulty of being in direct contact with the student, we still obtained promising results for further investigations. Therefore, from this first research performed with each electronic device exposed in this article, we hope that we can take larger measurements set after the pandemic, considering more deeply the participants' characteristics and states before performing the test with the sensorial devices and psychological analysis.  Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Data Availability Statement:
The data presented in this study are available on request from the corresponding author.