Conference PaperPDF Available

IoT for Health and Well-being: A case study and call for action



In this short paper we describe the implementation of an IoT test-bed in an elementary school. We argue that by adding additional IoT senors to an existing IoT system it is possible to evolve an indoor climate control system into a indoor milieu control system aimed at improving the health and well-being for both pupils and staff who spend their days working in the school environment. Lastly, we call for multidisciplinary action as the domain IoT for health and well-being spans across several different knowledge domains.
IoT for Health and Well-being: A case study and call for action
Karin Ahlin
CTF the Service Research Center,
Karlstad University
Karlstad, Sweden
Agnieszka Kitkowska
Department of Computer Science and
Informatics, Jönköping University
Jönköping, Sweden
Erik Wästlund
CTF the Service Research Center,
Karlstad University
Karlstad, Sweden
In this short paper we describe the implementation of an IoT test-
bed in an elementary school. We argue that by adding additional IoT
senors to an existing IoT system it is possible to evolve an indoor
climate control system into a indoor milieu control system aimed
at improving the health and well-being for both pupils and sta
who spend their days working in the school environment. Lastly,
we call for multidisciplinary action as the domain IoT for health
and well-being spans across several dierent knowledge domains.
Computer systems organization
Embedded systems;Re-
dundancy; Robotics; Networks Network reliability.
Internet of Things (IoT), Health and Well-being, Indoor climate
system, School environment
ACM Reference Format:
Karin Ahlin, Agnieszka Kitkowska, and Erik Wästlund. 2023. IoT for Health
and Well-being: A case study and call for action. In Proceedings of the
16th International Conference on PErvasive Technologies Related to Assistive
Environments (PETRA ’23), July 05–07, 2023, Corfu, Greece. ACM, New York,
NY, USA, 3pages.
The disruptive impact of digitalization in general, and the internet
of things (IoT) in particular, has led to a completely new range of
service innovations and the servitization of existing business mod-
els. Systems for automatic regulation of indoor climate have been
around for quite some time, but today, these systems are no longer
stand-alone systems. Today they utilize interconnected IoT sensors
placed within a building. Data from the sensors can be fed into
cloud-based AI models that can utilize weather data to determine
the output of the system. Furthermore, in addition to measuring
temperature and humidity, IoT sensors can measure other variables,
such as CO2. As high levels of CO2 have adverse eects on hu-
man productivity, the main argument for selling indoor climate has
evolved from saving money on energy costs to instead enhance
All authors contributed equally to this research.
This work is licensed under a Creative Commons Attribution International
4.0 License.
PETRA ’23, July 05–07, 2023, Corfu, Greece
©2023 Copyright held by the owner/author(s).
ACM ISBN 979-8-4007-0069-9/23/07.
sta productivity, simultaneously aecting their overall well-being.
Given the increase in available IoT sensors that can connect to the
existing infrastructure of a cloud-based climate control system, it
is possible to expand the notion of indoor climate to encompass
multiple aspects of the indoor milieu.
The case study presented in this paper aimed to create a test bed
for measuring students’ health and well-being in an elementary
school. The objectives of the test beds have been to create insights
and learning for developing new IoT prototypes of solutions and
functions and new working methods, processes, and business mod-
els. The measurements were supposed to improve the students’
subjective well-being and health and to work for a better daily life
through the application of IoT in school. The way to do this was
by using IoT devices to increase learning and create a better life
for everyone. IoT devices can also give students better access to
everything, from learning materials to communication channels,
and allow teachers to measure knowledge development in real time.
This case study was conducted in collaboration between Arvika
municipality, DigitalWell Arena, Karlstad University, and Scaaler
IoT Labs and was nanced by Vinnova (the Innovation Agency in
The participating elementary school in this project was a newly
built school adaptable to individual student needs. The school is
designed so that students can choose between social interaction
or individual recovery in impression-reduced environments. The
physical building is divided into home nests, cafeterias, and group
rooms. The home nests have room for 100 students each and are de-
signed to act as smaller schools. In each residence, three classrooms
are separated from the rest of the school, and only the students
who belong to that residents have access. The home nests aim to
avoid unnecessary classroom movements and thereby reduce stress.
The students can meet friends from other classes and residences in
the cafeteria. An overall strategy for the school’s sta is to develop
inclusive and accessible learning environments to improve students’
mental health, decrease long-term school absences, and increase
student well-being.
Well-being can be dened as a “positive state experienced by
individuals and societies. Similar to health, it is a resource for daily
life and is determined by social, economic and environmental con-
ditions” [
]. Educational institutions frequently position well-being
as a goal or aim in their curricula, but, in practice, focusing on
young people’s physical and psychological health might be insu-
cient [
]. Still, some sources identify evidence-based determinants
that contribute to well-being in schools. These include positive
adult–child relationships, a sense of belonging, positive self-esteem,
and the possibility for pupils (in the school setting) to be involved
PETRA ’23, July 05–07, 2023, Corfu, Greece Trovato and Tobin, et al.
in the decision-making processes, giving students responsibility
and enhancing their perception of ownership [2].
The IoT test bed included multiple types of sensors enabling the
measurement of dierent indoor environment quality indicators.
In particular, sensors allowed for measuring TVOC (total volatile
compound emissions), carbon dioxide, temperature, and humidity.
The employment of a long-range wide-area network (LoRaWAN)
allowed easy integration of sensors. LoRaWAN is an open-source
technology allowing for the inexpensive creation of private net-
works without the support of third-party infrastructure [
]. The
solution allows data transmission over the long-range, even in rural
areas up to 10-20 km, because of its high sensitivity associated with
long-distance communication. There are many benets of using
LoRaWAN, for instance, wide coverage, low bit rate and low power
consumption, security, and easy management. There are, however,
some potential issues related to the scalability of LoRa-based solu-
tions, but they are not relevant to the present paper.
Next to the LoRaWAN solutions, the project also included propri-
etary software provided by Swegon, one of the project partners. This
software enabled reporting about the data collected by IoT environ-
mental sensors. Through sophisticated algorithmic manipulation
based on the international standards for indoor environmental qual-
ity (e.g., ASHRAE, R1, and WELL guidelines), the tool presented
not only an analysis of the data, but also the best recommendations
for specic school rooms.
Let us consider one example. In the school building, some rooms
experienced high concentrations of TVOC emissions, and the sys-
tem marked them as having potential for improvement. However,
since the system considered the frequency of high emissions, the
improvements were not urgent. Regardless, the system explained
what TVOC is and pinpointed the high TVOC concentrations, ex-
plaining that they occur when ventilation is reduced (outside of
working hours). Additionally, the system recommended preventive
measures: “favor low emitting materials and allow an o-gassing
period in a non-occupied ventilated room for new furniture. As
corrective measures, increasing ventilation (also outside working
hours) is recommended until emissions decrease. Based on the al-
gorithmic manipulation, such recommendations can be easily used
by people responsible for indoor quality, e.g., building managers,
to ensure that the climate does not negatively aect the building
It should be noted that the system oers a broad range of func-
tionality oering very detailed reporting, which, in some cases,
might exceed users’ needs and expectations. As much as a curious
user might be willing to dedicate time and cognitive eort to un-
derstand what information might be the most important, without
proper identication of user needs and information that might be
useful for a specic type of user (e.g., classroom teacher might have
dierent needs than a maintenance manager), and without training
and education, the system reports may have an adversary eect,
e.g., reducing sta eciency or causing stress. Also, the currently
implemented solutions consist only of IoT solutions connected to
the physical environment, which are the most available in today’s
market. Only a few solutions are connected to subjective infor-
mation, such as suitable conditions for learning. In order to meet
the needs linked to students’ well-being at school, the range of
IoT solutions needs to be increased—everything from those that
exist today to those that need to be developed to include subjective
measurements. There is also scope in showing both individual and
aggregated data to reach the well-being of the individual and the
well-being of a group or an entire school.
Although the information from indoor environment sensors can
help ensure that the school atmosphere contributes to the sta and
students’ well-being, it might be insucient. Considering the user
needs identied in the present project, many issues that school
personnel and students experience require integrating other tech-
nologies to build systems that may improve health and well-being.
Let us consider the attendance. By automating the count, IoT could
relieve teachers from checking students’ class participation. How-
ever, for school sta, it is essential not only to know that students
are absent but also why they are absent. Here, ML could help iden-
tify student participation patterns and alarm teachers when such
participation reoccurs and should be further investigated by school
The data collected from the sensors helps in several ways, such
as supporting decision-making or follow-up situations. Data must
be interpreted to become information, meaning dierent groups
do it dierently, depending on their professional role or interest
as caregivers. By extension, this means that the stakeholders have
their view on what is interesting data to collect and present as infor-
mation. The stakeholders’ knowledge in interpreting data and infor-
mation matters regarding how and which data should be presented
and whether it should be interpreted as information. Therefore, it
is interesting to understand how to nd and use the requirements
of data and information from various stakeholder groups and their
intention to use it.
Given the development of indoor climate systems into IoT platforms
prepared for the inclusion of novel sensors, it has become easy
enough to deploy an indoor milieu measuring platform. The basic
principle of an IoT-based model for health and well-being can be
described as a system centered around an individual comprising of
the three elements Health and Well-being, Sensors and Data, and
Health and Well-being relate to all aspects of an individual’s
subjective experience or objective status. In regards to cur-
rent systems of indoor climate control, it relates to comfort
as an eect of indoor temperature and productivity as an
eect of lack of oxygen.
Sensors and Data relate to all aspects of sensors and external
sources for data collection and data processing. Current sys-
tems typically measure temperature and humidity as well as
CO2 levels.
Services are all automatic or manual ways in which the
output from the data processing is utilized to aect the in-
dividual’s health and well-being. This is typically achieved
IoT for Health and Well-being: A case study and call for action PETRA ’23, July 05–07, 2023, Corfu, Greece
Figure 1: IoT for Health and Well-being
through the automatic control of airow and heating or
through dashboards showing the current status of the sys-
However, given the multifaceted scope of the problem, in order
to successfully implement such a system it is necessary to include
expertise from several domains. By combining the domain expertise
of researchers from various disciplines such as psychology, nurs-
ing, computer science, information systems, and service science
research it might be possible to create IoT augmented milieus for
health and well-being.
Mukarram A.M. Almuhaya, Waheb A. Jabbar, Noorazliza Sulaiman, and Suliman
Abdulmalek. 2022. A Survey on LoRaWAN Technology: Recent Trends, Opportu-
nities, Simulation Tools and Future Directions. Issue 1.
Donnah L. Anderson and Anne P. Graham. 2016. Improving student wellbeing:
having a say at school. School Eectiveness and School Improvement 27 (7 2016),
348–366. Issue 3.
Amy Chapman. 2015. Wellbeing and schools: Exploring the normative dimensions.
In Rethinking youth wellbeing. Springer, 143–159.
World Health Organization et al
2021. Health promotion glossary of terms 2021.
Received 20 February 2007; revised 12 March 2009; accepted 5 June 2009
ResearchGate has not been able to resolve any citations for this publication.
Full-text available
Low-Power Wide Area Networks (LPWAN) technologies play a pivotal role in the IoT applications owing to their capability to meet the key IoT requirements, i.e., long-range, low cost, small data volumes, massive devices number, and low energy consumption. Between all obtainable LPWAN technologies, Long Range Wide Area Network (LoRaWAN) technology attracted much interest from both industry and academia due to networking autonomous architecture and an open standard specification. This paper presents a comparative review of a selected five driving LPWAN technologies, including NB-IoT, SigFox, Telensa, Ingenu (RPMA), and LoRa/LoRaWAN. The comparison shows that LoRa/LoRaWAN and SigFox surpass other technologies by devices' life-time, network capacity, adaptive data rate, and cost. In contrast, NB-IoT technology excels in latency and quality of service. Furthermore, we present a technical overview of LoRa/LoRaWAN technology by considering its main features, opportunities, and open issues.
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