This work is supported by the Spanish State Research Agency and the European Regional Development Fund (ERDF) under the PALLAS (TIN2016-80515-R AEI/EFRD, EU) project.

All methods described below have been approved by the regional government of Galicia&#39;s committee for research ethics of Pontevedra-Vigo-Ourense (reg. code 2017/336). The protocol was implemented for first year students at the School of Telecommunication Engineering - University of Vigo, both in a comfortable laboratory room and in several lectures and practice sessions of a bachelor&#39;s degree course on Computer Architectures.
1. Prepare the Devices
<ol>
	<li>Connect the smartphone and...

<ol>
	<li>IDC. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Forecasts Wearables Shipments to Reach 213.6 Million Units Worldwide in 2020 with Watches and Wristbands Driving Volume While Clothing and Eyewear Gain Traction. , (2016).</li><li>Piwek, L., Ellis, D. A., Andrews, S., Joinson, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+rise+of+consumer+health+wearables:promises+and+barriers.">The rise of consumer health wearables:promises and barriers.</a> PLoS Medicine. 13 (2), (2016).</li><li>Rudner, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interrogation+of+Patient+Smartphone+Activity+Tracker+to+Assist+Arrhythmia+Management.">Interrogation of Patient Smartphone Activity Tracker to Assist Arrhythmia Management.</a> Annals of Emergency Medicine. 68 (3), 292-294 (2016).</li><li>Gao, Y., Li, H., Luo, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+empirical+study+of+wearable+technology+acceptance+in+healthcare.">An empirical study of wearable technology acceptance in healthcare.</a> Industrial Management &amp; Data Systems. 115 (9), 1704-1723 (2015).</li><li>Lukowicz, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glass-physics:+using+google+glass+to+support+high+school+physics+experiments.">Glass-physics: using google glass to support high school physics experiments.</a> Proceedings of the 2015 ACM International Symposium on Wearable Computers - ISWC '15. , 151-154 (2015).</li><li>Sapargaliyev, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Wearables in Education: Expectations and Disappointments. , 73-78 (2015).</li><li>de Arriba P&#233;rez, F., CaeiroRodr&#237;guez, M., Santos Gago, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+do+you+sleep?+Using+off+the+shelf+wrist+wearables+to+estimate+sleep+quality,+sleepiness+level,+chronotype+and+sleep+regularity+indicators.">How do you sleep? Using off the shelf wrist wearables to estimate sleep quality, sleepiness level, chronotype and sleep regularity indicators.</a> Journal of Ambient Intelligence and Humanized Computing. , 1-21 (2017).</li><li>Espinosa, H. G., Lee, J., Keogh, J., Grigg, J., James, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+the+Use+of+Inertial+Sensors+in+Educational+Engagement+Activities.">On the Use of Inertial Sensors in Educational Engagement Activities.</a> Procedia Engineering. 112, 262-266 (2015).</li><li>Travers, C. J., Cooper, C. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> El Estre&#769;s de los profesores la presio&#769;n en la actividad docente. , (1997).</li><li>Maslach, C., Jackson, S. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+measurement+of+experienced+burnout.">The measurement of experienced burnout.</a> Journal of occupational Behavior. 2, 99-113 (1981).</li><li>Maslach, C., Jackson, S., Leiter, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Maslach Burnout Inventory. , (1986).</li><li>Kitsantas, A., Winsler, A., Huie, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Self-Regulation+and+Ability+Predictors+of+Academic+Success+During+College:+A+Predictive+Validity+Study.">Self-Regulation and Ability Predictors of Academic Success During College: A Predictive Validity Study.</a> Journal of Advanced Academics. 20, (2008).</li><li>Deberard, C., Scott, M., Glen, I., Spielmans, D. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Julka+Predictors+of+academic+achievement+and+retention+among+college+freshmen:+a+longitudinal+study.">Julka Predictors of academic achievement and retention among college freshmen: a longitudinal study.</a> College Student Journal. 381, 66-80 (2004).</li><li>Healey, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Wearable and automotive systems for affect recognition from physiology. , (2000).</li><li>Vrijkotte, T. G. M., van Doornen, L. J. P., de Geus, E. J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+Work+Stress+on+Ambulatory+Blood+Pressure,+Heart+Rate,+and+Heart+Rate+Variability.">Effects of Work Stress on Ambulatory Blood Pressure, Heart Rate, and Heart Rate Variability.</a> Hypertension. 35 (4), (2000).</li><li>Karthikeyan, P., Murugappan, M., Yaacob, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Descriptive+Analysis+of+Skin+Temperature+Variability+of+Sympathetic+Nervous+System+Activity+in+Stress.">Descriptive Analysis of Skin Temperature Variability of Sympathetic Nervous System Activity in Stress.</a> Journal of Physical Therapy Science. 24 (12), 1341-1344 (2012).</li><li>de Santos Sierra, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Design, implementation and evaluation of an unconstrained and contactless biometric system based on hand geometry and stress detection. , (2012).</li><li>Natale, V., Drejak, M., Erbacci, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Monitoring+sleep+with+a+smartphone+accelerometer.">Monitoring sleep with a smartphone accelerometer.</a> Sleep and Biological Rhythms. , (2012).</li><li>Guo, F., Li, Y., Kankanhalli, M., Brown, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+evaluation+of+wearable+activity+monitoring+devices.">An evaluation of wearable activity monitoring devices.</a> Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia. , (2013).</li><li>Wallen, M. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accuracy+of+Heart+Rate+Watches:+Implications+for+Weight+Management.">Accuracy of Heart Rate Watches: Implications for Weight Management.</a> PLOS ONE. 11 (5), e0154420 (2016).</li><li>Wang, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accuracy+of+Wrist-Worn+Heart+Rate+Monitors.">Accuracy of Wrist-Worn Heart Rate Monitors.</a> JAMA Cardiology. 2 (1), 104 (2017).</li><li>de Arriba P&#233;rez, F., CaeiroRodr&#237;guez, M., Santos Gago, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Collection+and+Processing+of+Data+from+Wrist+Wearable+Devices+in+Heterogeneous+and+Multiple-User+Scenarios.">Collection and Processing of Data from Wrist Wearable Devices in Heterogeneous and Multiple-User Scenarios.</a> Sensors. 16 (9), 1538 (2016).</li><li>IDC. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Wearables Aren't Dead, They're Just Shifting Focus as the Market Grows 16.9% in the Fourth Quarter, According to IDC. , (2017).</li><li>Mark, H., Ian, W., Eibe, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Data Mining: Practical Machine Learning Tools and Techniques. , (2011).</li><li>de Arriba P&#233;rez, F., Santos Gago, J. M., Caeiro Rodr&#237;guez, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analytics+of+biometric+data+from+wearable+devices+to+support+teaching+and+learning+activities.">Analytics of biometric data from wearable devices to support teaching and learning activities.</a> Journal of Information Systems Engineering &amp; Management. 1, 41-54 (2016).</li><li>Machine Learning Group at the University of Waikato. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Weka 3 - Data Mining with Open Source Machine Learning Software in Java. , (2018).</li><li>Zhai, J., Barreto, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stress+Detection+in+Computer+Users+Based+on+Digital+Signal+Processing+of+Noninvasive+Physiological+Variables.">Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables.</a> 2006 International Conference of the IEEE Engineering in Medicine and Biology Society. , 1355-1358 (2006).</li><li>Tombaugh, T. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comprehensive+review+of+the+Paced+Auditory+Serial+Addition+Test+(PASAT).">A comprehensive review of the Paced Auditory Serial Addition Test (PASAT).</a> Archives of Clinical Neuropsychology. 21 (1), 53-76 (2006).</li><li>Fan, Q., Wang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+real-time+realization+of+filtering+of+speech+with+DSP+TMS320VC5416+Chip.">The real-time realization of filtering of speech with DSP TMS320VC5416 Chip.</a> 2010 International Conference on Educational and Information Technology. , (2010).</li><li>Gonz&#225;lez Barajas, J. E., Velandia C&#225;rdenas, C., Nieto Camacho, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Implementaci&#243;n+de+filtro+digital+en+tiempo+real+para+detecci&#243;n+de+la+onda+R.">Implementaci&#243;n de filtro digital en tiempo real para detecci&#243;n de la onda R.</a> Revista Tecno L&#243;gicas. 18 (34), 75-86 (2015).</li><li>Mesirov, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computer+science.+Accessible+reproducible+research.">Computer science. Accessible reproducible research.</a> Science (New York, N.Y.). 327 (5964), 415-416 (2010).</li><li>American Journal Experts. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> How to Write an Easily Reproducible Protocol. , (2018).</li></ol>

The authors have nothing to disclose.

COTS wearable devices are among the most popular consumer electronics products available today. These devices are typically used to monitor physical activities, but their capabilities and performance could be of great interest in other areas. In this paper, a protocol to evaluate the use of COTS wearable devices for estimating stress in learning environments is discussed. The definition of such a protocol is especially relevant in order to analyze different solutions involving wearables and machine learning algorithms. T...

State-of-the-art wearable technologies are widely available, and their application environments are continuously expanding. We can find in the market many different devices, among which COTS wrist wearables1, such as smart watches and smart bands, are popular among athletes as a personal physical fitness monitoring tool2. By applying data analytic techniques, the data obtained using these devices can be processed to provide indicators such as general physical state, sleep quality or recovery factor. The demonstrated applicability in this area raised interest in the academic community about their possible application to other fields, especially in the health domain3,4, although the strict requirements of clinical trials limit their introduction. However, in a less demanding context such as education, we can find in the literature recent investigations involving the use of different types of wearable devices, both related to teaching activities5,6 and to the estimation of certain characteristics of the student such as sleep patterns7, or the analysis of students&#39; engagement in different educational activities8.
In our case, we focus on analyzing COTS wrist wearable devices as means to collect physiological signals that would eventually facilitate stress estimation, which in turn is a key aspect in educational contexts. Stress has a relevant influence in the development of academic activities and overall students&#39; performance. For example, stress levels are directly related to the onset of the burnout syndrome in students9,10,11, and high stress levels are especially relevant during the freshman year, where drop-out rates between 20% and 30%12,13 are common. Detecting and controlling stress indicators could dramatically improve academic performance.
The use of COTS wrist wearable devices is justified because they have sensors that provide information on physiological signals that have been widely used by the scientific community in stress assessment and detection. Some of the signals referred to in the literature used for this purpose include heart rate (HR)14, heart rate varaibility15, skin temperature (ST)16, respiration14, and galvanic skin response (GSR)17. These signals can be collected by COTS wrist wearables. However, they do not offer the same performance as clinical devices. There are differences related to the accuracy of sensors among devices18,19,20,21. Nevertheless, previous works18,19,20,21 have shown that, in a slow movement scenario, COTS wrist wearable sensors have error patterns similar to specialized devices.
The aim of this paper is to introduce a protocol to evaluate different solutions for stress estimation in students using COTS wrist wearables. There are many arrangements that can be proposed to estimate stress levels, involving the use of different wrist wearable devices and data analytics techniques, and more specifically machine learning algorithms. COTS wrist wearables are characterized by their high fragmentation, heterogeneity and interoperability problems22. Three companies have an aggregated market share of almost 50%23, but many other companies account for much smaller individual market shares, with an aggregated share above 50%. On the other hand, in terms of heterogeneity, not all wearables have the same number and type of sensors, with accelerometers and h sensors being the most common, and ST&#39;s and GSR&#39;s being only present in 5% of the devices studied. As for interoperability, there are different operating systems and data collection approaches that are not compatible with each other. As for the machine learning techniques that can be applied to estimate stress from the data collected by means of a wrist device, there are many options available24, including decision trees, neural networks, nearest neighbor approaches, Na&#239;ve Bayes classifiers, etc. To sum up, there is a great variety of solutions that may be developed for stress estimation, so it is instrumental to design an evaluation protocol to facilitate the comparison among different tentative options to eventually select the most suitable in a given context.
For the implementation of the protocol, several tools are needed (Figure 1). First, a COTS wrist wearable device is needed to fetch physiological data. This wearable device should have at least HR monitoring capabilities, but additional sensors are desirable (e.g., accelerometer, ST, GSR sensors). Second, a smartphone running the PhysiologicalSignal app is required to collect the data captured by the wearable device. Third, a tablet running the StressTest app is needed to run stress induction exercises (the smartphone could be used instead the tablet for this purpose). Fourth, some questionnaires to collect qualitative data on students&#39; perception on stress. Fifth, a server with a Web service25 to perform data collection and pre-processing, and a Web dashboard to show the evolution of the signals. And finally, a data analytics package26 to process the data collected about students using machine learning techniques.
The evaluation protocol is organized into two phases. The first one, the laboratory phase, is carried out in a comfortable room, where different stress levels (i.e., &#34;relax&#34;, &#34;concentrated stress&#34; and &#34;stress&#34;) are induced to a target subject (a student) through several common stress-inducing tasks. The second part takes place in the classroom, and it involves monitoring the student during the accomplishment of several academic activities: theoretical explanations, individual activities, short tests, exams, etc. During the implementation of this protocol, the subject&#39;s physiological signals are captured by means of a wrist device. Finally, these signals are processed by machine learning algorithms to provide estimations on the level of stress.
During the laboratory phase, the StressTest app is used to induce different stress levels. This app guides the subject to the completion of four different tasks. The first task is to create a baseline for stress analysis. In this task, the student visualizes a 4-minute relaxing video in which different shots of a sunset on a bridge are shown. The second task is an adaptation of the Stroop Color and Word Test27&#160;(SCWT). Every two seconds, the subject must choose the color in which the name of a color is painted (red, green, orange, blue and purple). Several buttons located at the bottom of the screen containing the initial letter of each color are available for the subject to choose the painted color at each time. For example, the button that refers to blue depicts the letter B. In our case, this test is divided into three different levels of difficulty. For the first level (SCWT1), the colored &#34;words of colors&#34; will appear in the same order as the buttons, so color and name match directly. This level is taken as baseline, as it does not involve any difficulty and the subject should only press the buttons properly, always in the same order. For the second level (SCWT2), the colored &#34;words of colors&#34; appear randomly, but the correspondence between name and color is maintained. Every time the subject fails a beep is emitted, and if two errors are made, the correct color score will be reset. For the last, most difficult level (SCWT3), name and color do not match. In this way this level is intended to be more complex and stressful for the subject. The third task consists on the Paced Auditory Serial Addition test (PASAT)28, which measures how the student experiences a concentration test. During this task, a sequence of consecutive numbers is played aloud, and the student must add the last two numbers and write the result in the provided on-screen box before listening to the next number. In this task, if the subject makes a mistake, a disturbing event occurs to generate stress (two numbers sound at the same time or a long period of silence in maintained). In this case, if three errors are committed, the sum account will be reset. The fourth task consists on a hyperventilation activity to induce the same variation in the physiological signals that would provoke a stressful situation17. At the end of each task and level, the subject has to indicate the level of perceived stress, using the application itself, according to a 5-value Likert scale.
During the classroom phase, students carry out their ordinary academic activities together with the rest of their classmates. The protocol focuses on the stress levels that occur during classroom-specific activities. At the end of the lecture, a brief questionnaire (Annex 1) is completed by the student to indicate the perceived level of stress in the several activities according to a 5-value scale.

<table border="0" cellpadding="0" cellspacing="0" width="968">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:219px;">Microsoft Band 2</td>
			<td style="width:191px;">Microsoft</td>
			<td style="width:119px;"></td>
			<td style="width:439px;">Wearable</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:219px;">Nexus 5</td>
			<td style="width:191px;">LG/Google</td>
			<td style="width:119px;"></td>
			<td>Smartphone</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">PhysiologicalSignals</td>
			<td style="width:191px;">Developed by the paper authors</td>
			<td style="width:119px;"></td>
			<td>App to collect data from wearables</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">StressTest</td>
			<td style="width:191px;">Developed by the paper authors</td>
			<td style="width:119px;"></td>
			<td>App to develop laboraty experiment: Video visualization, Stroop color and word test, paced auditory serial addition test (PASAT) , hyperventilation activity&#160;</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">Quizs</td>
			<td style="width:191px;">Developed by the paper authors</td>
			<td style="width:119px;"></td>
			<td>Questionnaires to collect qualitative data on students&#8217; perception on stress</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:219px;">Server</td>
			<td style="width:191px;">Developed by the paper authors</td>
			<td style="width:119px;"></td>
			<td>Server to store, analyze and display data</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Weka application</td>
			<td style="width:191px;">University of Waikato</td>
			<td style="width:119px;"></td>
			<td>Application to process the data using machine learning techniques</td>
		</tr>
	</tbody>
</table>

evaluation of commercial-off-the-shelf wrist wearables to estimate stress on students

Wearable commercial-off-the-shelf (COTS) devices have become popular during the last years to monitor sports activities, primarily among young people. These devices include sensors to gather data on physiological signals such as heart rate, skin temperature or galvanic skin response. By applying data analytics techniques to these kinds of signals, it is possible to obtain estimations of higher-level aspects of human behavior. In the literature, there are several works describing the use of physiological data collected using clinical devices to obtain information on sleep patterns or stress. However, it is still an open question whether data captured using COTS wrist wearables is sufficient to characterize the learners' psychological state in educational settings. This paper discusses a protocol to evaluate stress estimation from data obtained using COTS wrist wearables. The protocol is carried out in two phases. The first stage consists of a controlled laboratory experiment, where a mobile app is used to induce different stress levels in a student by means of a relaxing video, a Stroop Color and Word test, a Paced Auditory Serial Addition test, and a hyperventilation test. The second phase is carried out in the classroom, where stress is analyzed while performing several academic activities, namely attending to theoretical lectures, doing exercises and other individual activities, and taking short tests and exams. In both cases, both quantitative data obtained from COTS wrist wearables and qualitative data gathered by means of questionnaires are considered. This protocol involves a simple and consistent method with a stress induction app and questionnaires, requiring a limited participation of support staff.

The protocol discussed was put into practice in a Computer Architectures course in the first year of the Telecommunication Engineering degree at the University of Vigo. This course has more than 200 students enrolled who are organized into 10 working groups. To carry out this experiment, students from four of the groups were invited to enroll at the beginning of the academic year. The project attracted considerable interest among the students, and around 30 students volunteered to partici...

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Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

A protocol to evaluate solutions based on commercial-off-the-shelf (COTS) wrist wearables to estimate stress in students is proposed. The protocol is carried out in two phases, an initial laboratory-based stress induction test, and a monitoring stage taking place in the classroom while the student is performing academic activities.

Wearable commercial-off-the-shelf (COTS) devices have become popular during the last years to monitor sports activities, primarily among young people. ...

Wearable devices have become very popular. Among them, the most used are wrist wearables such as smart bands or smart watches. These devices are frequently used by sports people as activity trackers for monitoring and tracking fitness-related metrics.Sensors available in these devices allow to collect data such as users to your location, movement patterns, steps taken, or heart rate, among others. This data can be processed using data analytics techniques to estimate the value of variables such as distance walked, calories consumed, maximal aerobic capacity, or sleep quality. These devices are starting to be introduced in other domains, such as health or education.In our case, we decided to explore the possibility of using commercial wrist wearers in an academic context. More specifically, our work focuses on estimating the stressing of students using these devices. For this, we measure variations in physiological signs, such as heart rate, skin temperature, or sweating, during the performance of different training activities.Then, material learning techniques are applied to these signs to estimate stress levels. Commercial off-the-shelf wrist wearable devices have sensors that provide information on physiological signals that have been widely used by the scientific community in stress assessment and detection. Among these signals, heart rate, respiratory rate, skin temperature, or galvanic skin response are the most common.To carry out this study, an evaluation protocol has been defined, in which the following tools are used. A commercial wrist wearable that is provided to the student to capture physiological signals, a smartphone running an application developed to collect data and submit it to an analysis server, a dashboard that facilitates the visualization of collected data, and finally, the data analytics package that supports the execution of machine learning algorithms. The protocol that will help to stimulate the stress is divided into two phases.First, a calibration process is carried out in a laboratory, according to fully-controlled conditions. For this, different levels of stress are induced in the student by means of normalized tests, which are the Stroop color and word test, the Paced Auditory Serial Addition Test, and a hyperventilation test. Changes in biological signals are analyzed in these controlled conditions, to be used as a reference for the next phase.The first task is to create a baseline for a stress analysis. In this task, the student visualizes a four-minute relaxing video, in which different shots of a sunset and bridge and so on. Then the student will be questioned about their perceived stress level.The second task is an adaption of the Stroop color and word test. In our case, this test is divided into three different levels of difficulty. For this task, the user must choose the color in which the name of a color is painted.Several buttons located at the bottom of the screen containing the initial letter of a color are available fore the user to choose the color. For example, the button that refers to blue, that picks letter B.Difficulty increases for each level. To increase the student's frustration and increase their stress level, every time an error is committed, an annoying beep will sound.And if two errors are made, the correct color count will be reset. For the last, most difficult level, name and color will not match. Once each of the three levels is completed, the student will be questioned about their perceived stress level.The third task consists on the Paced Auditory Serial Addition Test, which measures how the student experiences concentration. During this task, a sequence of consecutive numbers is played aloud, and the student must add the last two numbers and write the result in the provided onscreen box, before listening to the next number. Again, if three errors are committed, the score will be reset.The student will be questioned about their perceived stress level upon completion of the task. The fourth and the last test consists on a hyperventilation activity to induce the same variation in the physiological signals that would provoke a stressful situation. The student will be inquired about their perceived stress level.The second phase takes part in the academic room. The objective in this case is to collect the physiological signals of the students while they are performing educational activities. For example, changes in the physiological signals are measured when the teacher asks a question or when the student is completing an answer.Connect your smartphone and tablet device to a stable internet connection. Turn on Bluetooth communications in the smartphone. On the smartphone and tablet, download and install the official wrist wearable applications for physiological signals and stress test applications.Turn on the wrist wearable device and place the wearable. In the smart phone, open the official wrist wearable application. The application will proceed to synchronize the wearable device with the smartphone.For some devices, an email address is required. In the smartphone, open the physiological signals application. Wait for the physiological signals app to display the word Weared"in green.This indicates that the wearable device has been detected, and therefore the transmission of information from the sensors to the smartphone is enabled. Choose a comfortable and non-disturbing room. Turn on the wrist wearable device.Place it around the subject's non-dominant wrist, and place the headphones on the head of the subject. Fit tightly but comfortably, the wearable around the wrist. In the smartphone, launch the physiological signals application.Select the change user option in the left configuration menu, and provide the ID of the subject who will complete the test, and click save. In a laptop, access the dashboard and enter the test administrator's ID and password. Select the subject's ID and select the subject's stress step.Check the physiological signal's evolution, and wait for the wearable device to reach thermal stability to start the experiment. On the tablet, launch the stress test application. Explain to the subject the four laboratory tests.Show some of the screens and actions to perform during each of the tasks. This is very important, because the subject shall feel stressed or relaxed in accordance with the performed activities, and not because they experience fear or concern about what is going to occur. Enter the same user ID as in the physiological signals app, and click the arrow.Launch the video task and give full control to the student. When the task is finished, check that the subject provides the perceived stress. Launch the Stroop color task consecutively for levels one, two, and three.Only for level three, in case the subject does not solve it after four minutes, terminate the task by pressing the arrow located at the top of the screen. Launch the PASAT test. In case the subject does not solve the test after four minutes, terminate the task by pressing the arrow located at the top of the screen.Launch the hyperventilation test. Observe the evolution of heart rate, using the dashboard. If the physiological signals do not change significantly, increase inspiration and expiration rates gradually.In case the subject feels dizziness or uncomfortable, halt the task. In any case, complete the task after four minutes. Turn on the wrist wearable device and place the wearable.Launch the physiological signals application and choose the correct subject ID.in a laptop, access the dashboard and enter the test administrator's ID and password. Select the subject ID and select the subject's stress stop. Check the evolution of physiological signals.Take notations about any relevant event occurring in the classroom in relation to student-teacher interaction. Relevant information and basic events will be used to label physiological signal samples afterwards. Example events are a question from the teacher to the student or a theoretical explanation.At the end of the lecture, ask the subject to complete the questionnaire about their level of stress at the specific times during the session, according to a five level scale. In a laptop, access the dashboard and enter the test administrator's ID and password. Select the subject ID and select the subject's stress step.Select the day of the classroom experiment. Begin to file lecture room activities and their duration according to the starting and finishing times and their types. For each activity, select a perceived stress level.For each subject and each session, download the file with the text samples. A comma-separated value file is created for each student. It shows reflecting the values of the physiological signals with their standard deviation, slope, and difference with higher values, the activity type, the activity-based stress, and the subject's perceived stress.Launch the data analytics package. Choose a set of classifiers. For example, support vector that changed, and import the comma-separated value file for all students for each session.Train and evaluate a set of classifiers, using the 10-fold cross-validation technique. Finally, check the results of accuracy and error rates. The protocol discussed so far has been validated in a real-world scenario, using a course in computer architectures.Preliminary results show strong variations in physiological signal values captured in the laboratory, from relaxed video watching to the hyperventilation test. These variations are a consequence of the evolution of the students in the study from the relaxed status to a stressed one. In this context, machine learning classification provides success rates about 90%Success rate worsens when data is captured in the lecture room.Nevertheless, relevant differences are found when the student is subjected to particularly stressful situations, such as pop quizzes or finals. It is well known that stress may have a most relevant influence in the development of academic activities and overall students'performance. Higher stress levels are especially relevant during the freshman year, with estimated dropout rates between 20%and 30%The proposed protocol is intended to serve as an instrument to eventually define richer student models than those presently used in learning management systems or student information systems.We think that the student model, enhanced with information of their mental or physical states, will pave the way for new added-value applications and services to improve academic performance. For example, the new information captured with the most suitable wearable device according the protocol discussed, could be applied to the early detection of situations affecting performance, such as fatigue, lack of attention, or stress. And to guide students to overcome those situations.

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Research

JoVE Journal

Engineering

7.3K Görüntüleme.  University of Vigo, Campus Lagoas-Marcosende. Wearable devices have become very popular. Among them, the most used are wrist wearables such as smart bands or smart watches. These devices are frequently used by sports people as activity trackers for monitoring and tracking fitness-related metrics.Sensors available in these devices allow to collect data such as users to your location, movement patterns, steps taken, or heart rate, among others. This data can be processed using data analytics techniques to estimate the value of varia...