=Paper=
{{Paper
|id=Vol-1925/paper05
|storemode=property
|title=Ongoing Research About the Use of Commercial-off-the-shelf Wrist Wearables in Educational Contexts
|pdfUrl=https://ceur-ws.org/Vol-1925/paper05.pdf
|volume=Vol-1925
|authors=Francisco de Arriba-Pérez,Manuel Caeiro-Rodríguez,Juan M. Santos-Gago
|dblpUrl=https://dblp.org/rec/conf/lasi-spain/PerezRS17
}}
==Ongoing Research About the Use of Commercial-off-the-shelf Wrist Wearables in Educational Contexts==
https://ceur-ws.org/Vol-1925/paper05.pdf
Ongoing research about the use of commercial-off-the-
shelf wrist wearables in educational contexts
Francisco de Arriba-Pérez1[0000-0002-1140-679X] Manuel Caeiro-Rodríguez1[0000-0002-2784-6060]
and Juan M. Santos-Gago1[0000-0002-2930-4249]
1
Department of Telematics Engineering, University of Vigo, Campus Lagoas-Marcosende,
36310 Vigo, Galicia | Spain
farriba@uvigo.es, Manuel.Caeiro@det.uvigo.es,
Juan.Santos@det.uvigo.es
Abstract. In this paper, we present the main achievements and analysis of our
ongoing piece of research about the use of wearables in educational contexts.
This work has led us to explore the use of wearables in educational environments
with the aim of enriching the student profile with new information, such as sleep
and stress indicators. These indicators can be estimated through the use of widely
available wearable device sensors, namely Commercial-Off-The-Shelf wrist
wearables. The first step has been to validate the use of these devices and their
sensors as good predictors of stress and sleep. Once this has been validated, we
have proposed some indicators that offer the user the opportunity to get infor-
mation such as the sleepiness quality, the chronotype, the latent stress of the stress
regularity. These are different features that contribute to get a better knowledge
of the learner and his state. In this paper, we will present the main research lines
that make up our project, the elements implemented in our system and how these
indicators can be applied in educational environments.
Keywords: wearables, wearables sensors, Learning analytics, self-regulated learning,
multimodal learning analytics.
1 Introduction
The appearance of the first Commercial-Off-The-Shelf (COTS) wearables as electronic
devices and systems incorporated in some part of our body or clothes [1, 2] became a
reality projects like Google Glasses and the first wrist wearables. The commercializa-
tion of this new type of devices generated a high expectation in all areas, from sport [3]
to medical environments [4, 5]. These devices offer the ability to see the world and
interact with the environment in a completely different way. The inclusion of portable
sensors such as the accelerometer, Heart Rate (HR), skin temperature (ST), Galvanic
Skin Response (GSR) and other sensors, offer users new self-quantification and train-
ing systems never seen before. But, certainly among all the available wearables wrist
wearables (watches and wrist bands) are the reference COTS wearable par excellence.
This type of devices has a greater presence in the market, leaving aside any other avail-
able option [6, 7].
Copyright © 2017 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.
Currently, wrist wearables are the main COTS wearables and among sensors avail-
able on them there is one whose presence is clearly superior to any other one: the ac-
celerometer. This sensor is without a doubt the most present sensor in COTS wearables
[8]. This sensor allows to quantify the movement in three axes and therefore to detect
the level of activity of the subject (number of steps, distance walked, speed, etc.). It can
also be used for other purposes, for example allows to study the level of rest while
sleeping through the application of actigraphy techniques using the accelerometer data
[9]. These analyses allow us to provide an estimate of sleep quality. Although the esti-
mated results are not as accurate as in the case of polysomnography [10], they are ac-
ceptable [11]. Therefore, the use of wearables including this sensor allows to obtain a
daily estimate of physical condition and rest without the need of any medical equip-
ment, which is inaccessible and expensive, although much more precise, of course.
Other sensor commonly present in COTS wrist-wearables is the HR [9]. The use of
this sensor in combination with the accelerometer allows to obtain more precise results
about features such as the training level, calories consumed (during the sport) or the
level of deep rest during sleep. In addition, this sensor combined with GSR could be
used to detect the level of agitation and anxiety in work and educational settings. The
variation of these physiological signals is completely related to stress, as indicated in
the following papers [12–16].
Focusing on education, both stress and sleep have an extensive amount of scientific
papers explaining how they affect students in educational settings. The existing litera-
ture demonstrates that sleep has an influence on the ability to acquire new knowledge
as well as in the academic achievement of students [17, 18]. Students’ cognitive func-
tions may be affected by levels of sleepiness as a result of poor sleep quality [19]. Fur-
thermore, as indicated in [20, 21], sleep deprivation affects the abilities to memorize
and think and even low motivation of students may be related to sleepiness [22]. Re-
lated to stress, it can be experienced by students through an emotional exhaustion and
a decrease in professional effectiveness as a result of trying to reach challenges too
demanding without the necessary resources. This type of stress is described as Burnout
syndrome [23, 24]. The effect of stress on students is especially important among first-
year college students [25, 26] and for this reason between a 20% and 30% of the stu-
dents drop out during such a year of college studies [25, 27]. As a result, it seems of
great importance to analyse both stress and sleep and to provide tools so that students
are able to manage their time, resources and environment in a better way.
Following these ideas our research group is interested on exploring the use of con-
sumer electronics wrist wearables, to feature learners and provide educational profiles
that can be used to enrich the educational support and improve learning performance.
Since the field of application is broad, we decided to start our study from a global per-
spective to focus on the applicability of existing results in the educational environment.
In this way, this article aims to provide a summary of all those questions that we have
studied over the last few years and our proposal of application for education, with the
aim of improving academic performance and increasing self-regulated learning.
The rest of the paper is organized as follows. First, in section 2, research points
and related work are introduced, describing important issues that need to be taken into
account when working with wearables. In section 3, it is provided an overview of the
developed system. Then, in section 4 application areas in education for indicators ex-
tracted from the wearables explained. The paper finishes with some conclusions in sec-
tion 5.
2 Research context
Our focus of study combines a total of 4 points of interest. Three of them are analysed
from a general point of view, while a third point is focused on the applicability of the
previous ones to educational scenarios:
• Homogenization of COTS wearable devices [9]. The use of different devices is not
direct, because they present very heterogeneous features, sensors, software plat-
forms, protocols, etc. In order to work with COTS wearables from different vendors,
a platform is needed that unifies the incoming data and that offers a service of ho-
mogenization independent of the type of wearable and the data that it supplies.
• Estimation of sleep indicators [28]. As it has been described in the introduction, ac-
celerometer and HR data can be used to estimate the level of sleepiness of a subject.
We have been working on the development specific sleep indicators, such as: sleep
quality, sleepiness, chronotype and sleep regularity. These indicators are proposed
to create a profile of the student sleep.
• Estimation of stress indicators. This is focused on estimating a subject stress levels
through collecting data from wearable sensors on which useful indicators can be
offered, such as: snapshot stress, aggregated stress, latent stress and stress regularity.
• Applicability of sleep and stress indicators in education. Through a student enriched
profile based on sleep and stress indicators, it could be possible to offer valuable
information and services for education, such as: to support the creation of groups
with homogeneous students according to the chronotype, to support the management
of classroom activities to reduce the global stress, to include reactivating tasks
against sleepiness, etc.
The result is a system that is able to collect information from a set of wearables and
offer a much more detailed student profile.
2.1 State of the art
In order to start this project, we reviewed the state of the art about similar projects in
this field. This work has led us to understand that the applicability of wearables, not
just COTS wearable wristbands, is still at a very early stage, with enormous possibili-
ties, but also with a major effort on the part of developers and researchers. These studies
are divided into two types of classes: (i) studies that use wearables as an educational
tool to develop specific projects; and (ii) analysis of the use of wearable sensors and
capabilities to feature students and enrich their profile.
As examples of the use of wearables to be used as an educational tool, it is possible
to find from the use of T-shirt to represent human body organs and their relations [29],
use of small microchips to carry out small educational projects [30], and use of Blue-
tooth Low Energy BLE beacons for interaction with real physical objects [31] to pro-
jects whose main focus are Google Glasses with the aim of using augmented reality for
experiments [32].
Meanwhile, as examples of wearable sensors and capabilities to feature students, it
is possible to find examples such as the use of wearables by teachers in accordance to
a new approach known as Teaching Analytics [33]. Also, projects focused on our re-
search line, using basic variables collected from a wearable such as number of steps,
HR, and the time and answers for a brief questionnaire, in order to estimate student
performance and stress [34], analysing the engagement in different educational activi-
ties [35], or the social relations among the students [36]. We emphasize in this line
StudentLife project [37], that uses smartphones (not wearables) to collect the data of
apps usage, user location, physical activity, number of conversations and duration of
each conversation per day, and so on, and relates them with the GPA, mental health
(e.g. stress) and behavioural patterns. In this sense, even if the variables collected are
not wearable, their objectives are closely related to our project.
As it can be seen, the literature on this subject is still at an early stage, and the exist-
ing projects focus on analysing the most intuitive and quick inputs that can be used with
wearables, marking the path of more complex studies that will arise as a result of them.
From our knowledge, there are no studies that use wrist wearables to enrich the student
profile and improve academic environments through indicators of stress, sleep and the
analysis of activities that are more sleepy or stressful. Once these indicators are pro-
posed, we believe that there are innumerable works that will take advantage of them
and eventually will provide useful information and services for education and teaching
purposes.
2.2 COTS Wrist Wearables
Before analysing the proposed system and its applicability in educational environments
we believe it necessary to transmit to the reader the most important ideas that surround
the wearables, analysing their diversity and their main problems. Although these and
other aspects have been addressed in an earlier publication [9], here it is a brief sum-
mary of the most relevant issues.
Diversity
COTS wearables are devices that have a wide range of possibilities, although it is true
that wrist bands and watches are over the rest as indicated in [7]. These devices had
approximately 50.5% and 41% of the market share respectively in 2016, while only the
remaining 9% aggregated involves a wide variety of device types ranging from eyewear
to clip-on devices [7]. In addition, this scenario does not appear to undergo changes in
the coming years [7], with wrist bands and smartwatches being the main devices in the
sector.
The diversity of devices is followed by the high diversity of sensors. GPS, accel-
erometer, Heart Rate (HR), gyroscope, barometer or thermometer are just some exam-
ples of the multiple sensors available. Notice that they do not have the same degree of
penetration in the market. Sensors such as the accelerometer and the HR are included
respectively in more than 86% and 33% of the devices [9]. This fragmentation of de-
vices is accompanied by the fragmentation of operating systems included on them. Dif-
ferent companies like Google, Samsumg, Apple, have presented their own software
platforms and their operating systems for this type of private devices. This also contrib-
utes to increase the level of fragmentation.
Main issues
Unlike PCs or smartphones, the world of wearables presents many difficulties because
the problems of fragmentation explained above. This results in many issues when data
from sensors of different devices needs to be collected and processed. These problems
are referred to as homogenization problems. Although two wearables have the same
sensors, collection of their data may be very different [9]. Moreover, even if the data is
accessible on both devices, the delivery of the data to our analysis server will depend
to a great extent on which tools are available, protocols, authentication tokens, etc.
Once the server is reached, a new problem arises, the data of different wearables follows
different data models, vocabularies, and these must be homogenized. For example, the
sleep states are named by Fitbit as ("asleep", "awake" and "really awake") and By Mi-
crosoft as ("awake", "light" and "deep").
Finally, another of the main problems in the world of wearables is its accuracy. Each
sensor has a certain precision, but this information in many cases is not available on the
manufacturers' websites, and for this reason their accuracy is discussed in some papers
[38, 39]. Nevertheless, there are also recent studies that validate the wearables' HR ac-
curacy within a reasonable margin of error [40–42], especially in conditions of low
movement or rest, common situations in a classroom. In any case, their use has not yet
been accepted in medical settings yet [43–45].
3 Our analysis system
Taking into account the current state of similar projects and the issues with COTS wear-
ables, in this section we will present the state of development of our research project.
We reviewed the COTS wearable market to select the wearables to be used in our stud-
ies. The following ones were tested: Fitbit Charge, Fitbit Charge 2, Fitbit Surge, LG
Watch R, Microsoft Band, Microsoft Band 2, Moto 360 v1 and Jawbone.
Initially, to perform the data collection we developed a system running on a Linux
machine, written in Java and running on a Tomcat server. This development allows us
to automatically collect the data from the wearables directly, via an intermediate
smartphone or PC, as well as some data from wearable vendor servers, because in some
cases they store and provide services to get users’ data on-line. Therefore, in our sys-
tem, using a REST API and several applications running on Android devices, the data
is transferred to our server. Later this data is analysed and processed to allow its visu-
alization in a teacher and student dashboard and the estimation of indicators.
To get the information we need to compile for stress and sleep indicators, we devel-
oped several applications for Android:
• Survey app for the sleep study. Our first goal involved offering a set of sleep indica-
tors, including Sleep Quality. This indicator required a validation based on user re-
sponses. In addition, the system is trained and improved through the responses of the
user perceived slept at each night. For this reason, we made a survey-app, which
every day asked at the same time how tired the subject felt on that day. A capture of
the application with only one answer and no data about the sleep is shown in the next
Fig. 1.
Fig. 1. Sleep quiz application.
• App for collecting data from wearable. This application is used to collect in real time
the values of the sensors and once collected, through internet access, send them to
our server. It is important to notice, as indicated in [9], not all wearable devices allow
this type of access in real time, in particular. The device offering greater flexibility
was Microsoft Band 1 and 2, because it is possible to retrieve all the sensor data of
these bracelets in real time. In addition, this app is prepared to serve as the student
dashboard application, showing data about stress and sleep features, as shown in the
Fig. 2. Moreover, this app enables the user to register daily, the stress level, or the
level of sleepiness, and thus improve the classification system to predict news states
of stress and sleepiness.
• Application for the study of stress. In order to validate stress or as a pre-training
phase of the model, we perform an application that combines several activities to
provoke relaxation, stress and concentration in the users who perform them. These
activities have been described in the literature in stress-related studies: a relaxing
video, Stroop Colour-Word Interference Test [46], the PASAT text [47] and an Hy-
perventilation exercise [16]. A capture of two layouts of the application is shown in
the Fig. 3.
In addition to the applications mentioned above, we have also implemented a small
dashboard, where the student and the teacher can see the variation of physiological
signals and several of the proposed indicators. This dashboard is still under develop-
ment, although several of the features are already available. In Fig. 4, we show an ex-
ample of what would be the dashboard of a teacher-subject, visualizing the average
level of stress / concentration / relaxation of the class and its variations during teaching
hours.
Fig. 2. Application to collect wearable data in real time.
Fig. 3. Sleep test Application.
4 Application in educational contexts
In this section, we explain how the stress indicators, such as the Snapshot Stress level
(SS) Aggregated Stress (AS), and Latent Stress (LS), and the indicators of sleep, such
as the Sleep Quality (SQ), Sleepiness (S) or Chronotype (CT)), can be applied in edu-
cational environments with the aim of assisting student learning tasks, teachers and
developers of educational content.
To understand the possibilities offered by these indicators, it is necessary to briefly
explain what the function of each one of them is. On the one hand, the SS will indicate
the level of stress that the users feel at a particular moment, the AS indicates the stress
percentage along the day while the LS indicates the levels of stress that a student can
accumulate after several days of activities and consecutive exams. The first indicator
provides an instant measure of stress while the second and the third offers a cumulative
value. Related to sleep, SQ evaluates the quality of sleep during a night; S offers the
level of tiredness and sleepiness that the student experiences; and finally, the CT indi-
cates the most suitable hourly range for going to sleep and get up. While S is an instant
value, SQ value is daily and CT is a value analysed in broad temporal periods, as for
example 4 weeks.
The final purpose of our work is to support teachers, students and developers on the
performance of their educational activities. Next sub-sections provide more details
about our goals focusing on services for these roles.
Fig. 4. A sample of a teacher dashboard.
4.1 Services for students
Our main focus is to apply these indicators to enrich the student profile and enable him
to manage and organize by himself his activities and efforts more accurately and there-
fore increase his performance and learning. This idea fits perfectly in the Self-Regu-
lated Learning (SRL) approach [48, 49]. As indicated in [50], Self-regulated learning
(SRL) autonomy and control by the individual is emphasized, because it is the student
who monitors, directs, and regulates actions towards goals of information acquisition,
expanding expertise, and self-improvement. But even if a user knows how he feels, or
what activities are the ones that stress him the most, sometimes he does not know how
to interpret these values, or what decisions he should make. That is the reason to offer
some technical support that can help him to monitor and reflect about his psychological
and physiological states. This kind of information can also be useful in the context of
affect-aware tutors [51], as long as these systems base their actions on the features and
states of the students. In both cases, the use of stress and sleep indicators can lead to
enhance the performance of tasks autonomously and with a high performance. We con-
ceive a system that will offer the following tools:
• Values of indicators and graphs. Sometimes a simple value of an indicator is not
enough. The representation of values and their variation along time in a graph are
necessary for the user to detect some problem in their levels of rest and stress. That
is the reason why graphs are proposed to represent snapshot stress, latent stress, sleep
quality or sleepiness indicators.
• Notifications and alerts. Depending on the levels of stress and sleep indicators, no-
tifications will be generated that the user can understand as suggestions of the system
to improve some behaviours. Similarly, alerts that will show a worrying warning
about some of the behaviours can be triggered, indicating how they could be tackled.
A notification would be a low level notice, while an alert will show a high level of
danger. Some conditions to trigger alerts could be high values of latent stress, aggre-
gated stress with constant increases, sleep quality lower than 50% for more than one
week, a high snapshot stress time rates during classes or a sudden variation in the
chronotype during a certain period of time.
• Through the analysis of the activities that have generated higher levels of sleepiness
and snapshot stress, a work agenda can be proposed to the student interleaving ac-
tivities to maintain an adequate level of stress. The idea would be neither to generate
too many monotonous and boring activities, nor to saturate students with difficult
and costly activities that generate too much stress.
4.2 Services for teachers and content providers
Although our main focus is situated in the context of self-regulated and affect-aware
tutors, the proposed indicators can be useful for other purposes in educational systems.
For example, these indicators could help teachers to select and improve learning re-
sources. In this way, the indicators can also be useful for content providers, because the
stress and sleepiness reactions of students while experiencing contents can be very use-
ful to improve them
For these reasons, we have explored how the proposed indicators can offer support
during teaching and in the creation of learning contents. In this case, we conceive the
following tools:
• Values of indicators and graphs. As in the case of students, the representation of
graphs in a dashboard will allow the teacher to get a global view of the students and
their average indicators. With indicators, such as the sleepiness, the teacher could
know if his last lecture has been as engaging as hoped. Depending on the results and
variations over time the teacher could change his plans, for example, trying to get
students stay on average in low levels of sleepiness and stress.
• Arousal level detection. Based on papers such as [34], detection of stress and mood
states during exercises, exams or during lectures, allows to draw a map of the psy-
chological profile for each student. In our case, we could use latent stress, variations
in chronotype and regularity of sleep, to understand the mood of students. These
indicators provide a snapshot of how the previous days have been and therefore of
the accumulated fatigue and stress.
• Creation of working groups. One of the most direct utilities of the use of indicators
such as the chronotype is the creation of working groups. Creating working groups
with similar sleep profiles will allow students to agree on a broad range of hours. If
people with different sleep profiles are joined this can introduce difficulties for ar-
ranging meetings and plan activities. In other scenario, if it is detected that the ac-
tivities of oral presentation in front of the classroom are especially traumatic for a
student, it could be wise to join him in a same group with students whose speech
facility and level of stress in this type of activities is smaller.
• Timetable. Analysing the level of sleepiness and stress along with accumulated val-
ues (latent stress, sleep regularity) both outside and inside the classroom, and com-
bining this information with the school and extracurricular hours could allow to dis-
cover specific difficulties for a student, higher workload day, or hours of the day at
which arousal levels are higher. With this, the teacher could make better personal-
ized work plans, or decide about the best days for the exams. From a more institu-
tional point of view, the educational institution itself could structure teaching hours
based on these indicators, avoiding tasks of high level of concentration in the hours
where students do not present high performance
• Improving the learning resources. As it has been said about the teacher, there is also
the task of producing the educational content to be used by learners. A good way to
know if this content is adequate would be to evaluate the levels of stress and sleepi-
ness during their use. Those contents that generate very high levels of stress should
be reconditioned, thus avoiding that these levels could lead to frustration and aban-
donment by the student. Likewise, activities that do not generate any stress and that
raise the levels of sleepiness of the student should be eliminated or placed between
tasks that generate a depletion above the average.
5 Conclusions
The existing literature demonstrates the role of stress and sleep in academic and learn-
ing performance. Proper stress control and a good level of rest would reduce dropout
rates and improve student achievement. To enable the measurement of stress and sleep
related features, devices such as wearables are presented as a good alternative because
of their great advantages: they are comfortable, programmable, and have a large number
of sensors. Nevertheless, studies focused on the use of these devices have revealed that
this field of research is still at an early stage where it is possible to explore new alter-
natives of applicability in educational settings. Our proposal is focused on studying if
the detection of stress and sleep through COTS wrist wearables is possible. To achieve
this, we have developed our own homogenization / analysis server and several apps on
Android to collect, adapt and analyse data from wearables sensors. This complete sys-
tem allows us to calculate indicators of sleep and stress, such as sleep quality, sleepi-
ness, chronotype, snapshot stress, aggregated stress, latent stress, etc. In general, the
use of these indicators results in an enrichment of the student profile. This enrichment
allows the student to know better his levels of stress and sleep, to understand what
routines and strategies provide better results and helps him to achieve an efficient man-
agement of his learning time. Similarly, the teacher could know first-hand his students,
efficiently distribute the contents of his subject throughout the term and know what
activities are better and worse for the students.
Finally, although the feasibility of using these devices seems valid, our studies also
show that the use of COTS wearables has limitations. On the one hand, the precision of
the sensors, or the low penetration of interesting sensors such as the GSR or ST, prevent
the same advantages for any wearable device. Factors such as the lack of homogeniza-
tion, or the programming efforts required to set up a system, are also a problem. On the
other hand, solved all the previous problems, there are other factors like the ambient
conditions (e.g. temperature) or the level of movement that affect the physiological sig-
nals measured by the wearable and therefore to the possible results obtained.
6 Acknowledgment
The authors acknowledge the support of SNOLA, officially recognized Thematic Net-
work of Excellence (TIN2015-71669-REDT) by the Spanish Ministry of Economy and
Competitiveness, the Spanish State Research Agency, the European Regional Develop-
ment Fund (ERDF) under the PALLAS (TIN2016-80515-R AEI/EFRD, EU) project and
the employment contract granted by the University of Vigo in July 2016 for the perfor-
mance of PhD studies.
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