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Internet of Medical Things for Independent Living and Re-Learning

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Abstract

This position paper gives better insight about the role and importance of Internet of Medical Things (IoMT) for independent living and re-learning for older adults. Sensing Technologies are the paradigm shift for transforming conventional healthcare practices into the smart, and self-assisted activities, which are envisioned for today's medical world. Internet of Things (IoT) and IoMT are the interrelated technologies for promoting independent living and re-learning practices. In this paper, re-learning is defined as the process for adults to recover useful instrumental activities of daily living skills that have been lost after an impairment.
Internet of Medical Things for Independent Living and Re-Learning
Ali Hassan Sodhro, Karin Ahlin, Awais Ahmad, Peter Mozelius
Computer and Systems Science
Mid Sweden University
Östersund, Sweden
Emails: {alihassan.sodhro, Karin.ahlin, Awais.ahmad, Peter.mozelius}@miun.se
Abstract This position paper gives better insight about the role
and importance of Internet of Medical Things (IoMT) for
independent living and re-learning for older adults. Sensing
Technologies are the paradigm shift for transforming
conventional healthcare practices into the smart, and self-
assisted activities, which are envisioned for today’s medical
world. Internet of Things (IoT) and IoMT are the inter-related
technologies for promoting independent living and re-learning
practices. In this paper, re-learning is defined as the process for
adults to recover useful instrumental activities of daily living
skills that have been lost after an impairment.
Keywords: Internet of Medical Things; Independent Living; Re-
Learning; Smart Homes; Older Adults.
I. INTRODUCTION
The aim of this positional paper is to show how the
Internet of Medical things (IoMT) can be used for independent
living and re-learning. In addition, the paper shows how
healthcare is revolutionized with the help of the Internet of
Things (IoT) and advanced sensing technology. This paper
presents the role of IoMT in independent living of elderly
people both in their own houses and in retirement homes, and
the utility of embedding sensing technologies in everyday life
objects. Thus, IoMT can reduce the expenses for healthcare
due to the lower need for personal assistance, and provides a
better Quality of Life (QoL) to the elderly users [1][2].
In the modern world, a better healthcare system is one of
the main challenges of a growing world population. The IoMT
is a collection of Wi-Fi-enabled medical devices intended to
collect data on health parameters such as heart rate or blood
pressure. Wireless sensor networks (WSNs) enable device-to-
device (D2D) communication, necessary for the synthesis of
multiple types of medical data. In addition, IoMT is the vision
of providing a better healthcare system. The main
requirements for WSNs are increased data rates, high speed
and more bandwidth. Ongoing development of consumer
technologies not only have enhanced the speed of Internet
driven platforms, but also encouraged and promoted the
markets of IoT devices such as smartphones, Personal Digital
Assistants (PDAs), and many types of sensor-enabled
wearables [3]. In addition, the technology company Ericsson
claims advanced technologies that manage the massive data
amounts, bandwidth, delay, and data rate will entirely
transform and reshape the healthcare world. There are various
advantages of the IoMT based smart and pervasive healthcare;
for instance, better Quality of Service (QoS), adaptive and
scalable features to other heterogeneous networks, high and
cost-effective capacity, high reliability, lower delay, longer
connectivity with intelligent data traffic management, and
high energy-efficiency [4].
The IoMT is a collection of various sensor nodes, which
collect data and transmit to a gateway (i.e., the platform or a
medical office) for proper connection and communication
with the help of the cloud and the Internet. The main task of
the IoMT is to incorporate lightweight portable sensor
technologies to support healthcare systems with impressive
and integral capabilities for ongoing data collection and
synthesis to support accurate monitoring and diagnosis of
older adults. The physician can efficiently access and review
patients’ data and analyse which patient needs more attention.
The IoMT is a promising technology for remote older patients
monitoring where it improves medical care with the key focus
on ‘healthcare for anyone everywhere’ [5].
The remaining of the position paper is organized as
follows. Section II presents Internet of Things, Section III
discusses Internet of Medical Things. Independent living is
described in Section IV and Section V reveals the re-learning.
Discussion, conclusion and future research are given in
Sections VI and VII, respectively.
II. INTERNET OF THINGS
Longer lives and better healthcare facilities are
cornerstones for an increasing aging society, and in several
parts of the world healthcare is becoming challenging with
high cost and poor economic status, which affects many
generations. The recent trends in emerging Information
Communication Technologies (ICTs) have reshaped the entire
healthcare world by providing easy and effective data-
collecting, diagnosis, and treatment facilities [1]. It is
necessary to manage and preserve the patient’s experience for
efficient monitoring of the healthcare applications for
instance, home health monitoring, and Personal Health
Records (PHRs). Several IoT-based wearable devices for
instance, smartwatches, smart rings, smart necklace, and
Fitbits can be used with the human body (i.e.,
on/inside/implant) to collect vital-sign signals for effective
diagnosis and cure. The service providers can access the data
for accurate diagnosis and treatment to give convenient, cost-
effective and timely treatment [2].
16Copyright (c) IARIA, 2021. ISBN: 978-1-61208-892-1
GLOBAL HEALTH 2021 : The Tenth International Conference on Global Health Challenges
It is also essential for the healthcare system to assure the
availability of accurate and error-free critical information to
intended users (physicians and patients). The mobility of IoT
devices and pervasive features of integrated technologies
adopt different healthcare applications with wide coverage
and sustainable connectivity. Thus, it is important to
effectively monitor the lives of elderly people while
exchanging the data through IoT-based portable devices [6].
III. INTERNET OF MEDICAL THINGS
The rapid increase in the number of elderly people at
present gives clear insight for future population record and
healthcare status that about 15.7% population shall be in the
age range of 65 or older by 2030 [7]. The flexible and scalable
features of IoMT easily integrate the wearable medical
devices with existing and advanced technologies for
independent living, sustainable, reliable and better
connectivity with improved efficiency, accuracy and economy
[8]. The services offered by IoMT require less cost, are
simple, accurate and have an effective mechanism with
sustainable battery capacity at fast speed and with reliable
connectivity. The IoMT system must be well-equipped with
advanced network and continuous connectivity of devices.
For scanning and connectivity with doctors, patients must
have a valid identity. The rapid growth in smart cellular
technologies have revolutionized the notion of healthcare with
the support of IoMT [9].
The collected patient’s data must be preserved privately
for better analysis and diagnosis. Because of its sensitive
nature, it is necessary to properly monitor and draw the
reports. There is a big impact of IoMT in our daily life and its
role increases as life goes on. It also provides solutions to
chronic diseases as well as those patients who suffer a lot from
constant and long-term pain [10]. The medical data of patients
like electrocardiography (ECG), heart rate, and
electroencephalogram (EEG) signals can be monitored within
e-health applications through recent wearable IoT devices. An
important issue in these IoT devices with the transmission of
signals is power consumption and the devices need battery
resources; there are serious limitations for the continuous
observation of signals. To extend the battery lifetime, lossy
signal compression is used to reduce the size of collected bio-
signals data and, in return, increase the battery lifetime of
wearable devices for continuous and long-term monitoring.
One-dimensional bio-signals like EEG, ECG and respiratory
data are usually available in commercial IoT devices. [11]
gives the review of some existing medical data compression
algorithms.
IV. INDEPENDENT LIVING
The emerging notion of independent living or ambient
assisted living is realized due to the vast and revolutionary role
of the Internet of Things (IoT). Due to the lightweight nature
and cost-effective features of sensors, it is easy to provide
quality of life to elderly patients even at remote locations.
Thus, IoT can be considered as promising and vital for various
fields such as smart healthcare, smart transportation, and
smart cities [12]. The rapid progress and advancement in
smart technologies have not only facilitated the lives of every
age group, but also reduced the costs of healthcare to
reasonable rates. Hence, longer and better life expectancy is
the result of emerging and user-friendly IoT-based wearable
devices [13].
Over the past two decades, the population of older adults
has been rapidly increasing all over the world [14]. Due to
these changing demographics, healthcare providers are facing
an increasingly massive workload; as time goes on, the need
to alleviate their workload becomes more critical [15][17]. On
the other hand, despite age-related physical and cognitive
problems, older adults like to live independently in their home
environment [18]. Several studies highlighted that patients not
only recover more quickly in their home environment, but
their quality of life is also improved [15][19]. The Internet of-
Medical Things (IoMT), which includes technologies and
devices such as sensors for windows, doors, temperature,
humidity, luminosity, and smart audio and video cameras,
might be used to achieve a better QoL and independent living
[20].
Several researchers emphasize IoMT as a foundation for
independent living, called ambient assisted living; sometimes,
the integration of IoMT throughout a house is referred to as a
smart home [21]. Researchers focus on requirements for the
technology and smart home applications and explain that
devices and applications should be flexible, adaptive and
changeable over time [21]. The authors of [22] focus on the
middleware, which serves as a collection point on the one
hand, and as a processing and distribution centre, on the other
hand. The data is collected via tailor-made parameter
monitoring devices and home sensor-technology and further
distributed to care givers. The authors in [20] address
problems with IoMT for independent living in terms of user
interfaces, easy-to-use features, size, weight, and
obtrusiveness. Besides these problems, [23] addresses lack of
interoperability causing problems while connecting to
caregivers or relatives.
The IoMT implementations for older adults focus on
preventing falls and supporting Activity of Daily Life (ADL)
[22][24]. Starting with falls, [25] express fall prevention as
important for older adults. They built a prototype using an
accelerator and sensors. In this case, the accelerometer detects
if a fall has occurred and if it does happen, the server will
automatically notify the caregivers or relatives. There are
several examples focusing on supporting ADL, where one is
to add awareness of older adults’ decreased physiological
resilience and weakened response to stressors [24]. The
authors frame their study by collecting data from the
participants’ activity when moving around the city. The data
from older adults is linked with data collected from smart city
implementations, such as geographical positions. Another
example is predicting upcoming or ongoing disease attacks for
noncommunicable diseases and cognitive assessment [20].
They base their system on correlations between physiological
and cognitive data and frame it as predictive analysis.
V. RE-LEARNING
In the e-health area, re-learning has been described as the
process for an adult to recover useful instrumental activities
of daily living skills that have been lost after an impairment
17Copyright (c) IARIA, 2021. ISBN: 978-1-61208-892-1
GLOBAL HEALTH 2021 : The Tenth International Conference on Global Health Challenges
[26][27]. This is a process with the aim of improving the
quality of life and well-being of patients, to increase their
potential for an independent living. Often, re-learning consists
of an unstructured process that has been referred to as the
Trial-and-Error method, with skills acquisition by guessing
correct responses and learning from errors. However, more
structured methods have been tested and the study by [28]
found an advantage for errorless re-learning when compared
to errorful re-learning among memory-impaired patients.
Errorless learning refers to the use of feed forward
activities to prevent learners from making mistakes.
Therapists present the different steps in a re-learning activity
with detailed instructions and visual cues. Another structured
re-learning method is the spaced retrieval approach, a
technique that requires a patient to memorise and reproduce
task sequences. These methods have been tested and found
successful for patients with memory impairment [26][27][29].
In addition to older adults' need for cognitive re-learning, there
are also needs for motoric re-learning and speech re-learning
[15]. For all these three branches of re-learning, it is essential
to adapt the more general instructional design for the older
adult target group. A frequently applied approach is to extend
pedagogy with the principles of adult learning [30][31].
Cognitive and motoric re-learning are today, to a high
degree, technology enhanced, but speech re-learning could
also possibly be technology enhanced. However, the condition
is to tailor software and hardware solutions to the actual target
group [32]. This should also be a condition for instructional
design and pedagogy, where the important adult learning
principle 'learning to learn’ [33] is not exclusively for older
adults. For e-health in general and for IoT solutions in
particular, there is also a need for upskilling the staff [34].
Finally, for re-learning in the growing number of older adults,
it seems worth to consider an extension of the pedagogy
andragogyheutagogy continuum [35], in the direction
towards geragogy, with the idea of facilitating their e-health
media literacy [36].
VI. DISCUSSION
A. Smart healthcare
Rapid progress in IoT-based wearable devices has
revolutionized the entire traditional healthcare perspective
into a smart and pervasive fashion. IoMT is the cornerstone to
achieve the required and standard needs of the elderly patients.
Intelligent features of the IoT-based IoMT devices not only
provide ease and comfort to the patients, but also lead to cost-
effective and smart healthcare anywhere and for anyone.
Thus, it can be claimed that sensors within IoT and IoMT
based wearable technologies, are the vital entities for
promoting smart and ubiquitous healthcare.
B. IoMT and older adults independent living
For older adults to live happily and independently, IoT
seems to be a cornerstone and it is widespread in its
functionality. This claim is built on transforming the
traditional homes of the elderly into smart homes using
sensing technologies for sending information to caregivers.
IoMT allows the possibility to prevent common falls among
older adults as well as predict various diseases. Besides, in
smart homes, IoMT can be useful for geographical
monitoring, combining smart city approaches with older
adults’ data while conducting physical activities. Still, IoMT
for independent living of older adults seems to be in its
infancy, and the development potential is not fully utilized.
C. IoMT and Re-learning
Amongst many other things related to e-health and
independent living, re-learning can benefit from IoMT
enhancement in several aspects. One aspect is the possibility
of measuring the progression in the various branches of re-
learning. For cognitive and motoric re-learning, there is also
the opportunity of monitoring re-learning, both for assessment
of exercises and for avoiding accidents. Furthermore, the
general technology enhancement can realise the idea of re-
learning anytime and anywhere and support the andragogy-
heutagogy concept of self-directed learning.
D. Usability and technology acceptance of IoMT for older
adults
Although the IoMT based devices have several potential
benefits for older adults, the usability of and technology
acceptance for these devices has been a matter of concern.
Particularly, the older generation who have not used the
technology frequently in their entire life might have problems
with adapting and using these devices. Age-related
impairments and chronic diseases also limit the use of
technology in older adults. Therefore, it is of great importance
to design and develop IoMT based devices and services
according to the special needs of older adults. A User-Centred
Design (UCD) approach should be adopted where the users
are involved throughout the design and implementation
process.
VII. CONCLUSION AND FUTURE RESEARCH
Independent living and re-learning are main activities to
be practiced effectively by the older adults on a daily basis to
keep themselves happy and healthy. This can be possible
through the emerging sensing technology, IoT and IoMT
driven wearable devices. Besides, smart and pervasive
healthcare is based on the fundamental characteristics of these
unobtrusive portable and lightweight devices. Due to its
highly intelligent sensing and processing capabilities, IoMT
easily provides the ‘smart and cost-effective healthcare for
older adults’. Finally, the implementation of IoMT to support
re-learning and independent living share the same concerns as
other e-health technologies: trust, security, personal integrity,
user acceptance, and accessibility of ICT.
18Copyright (c) IARIA, 2021. ISBN: 978-1-61208-892-1
GLOBAL HEALTH 2021 : The Tenth International Conference on Global Health Challenges
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Deep learning (DL) driven cardiac image processing methods manage and monitor the massive medical data collected by the internet of things (IoT) based on wearable devices. A Joint DL and IoT platform are known as Deep-IoMT that extracts the accurate cardiac image data from noisy conventional devices and tools. Besides, smart and dynamic technological trends have caught the attention of every corner such as, healthcare, which is possible through portable and lightweight sensor-enabled devices. Tiny size and resourceconstrained nature restrict them to perform several tasks at a time. Thus, energy drain, limited battery lifetime, and high packet loss ratio (PLR) are the keys challenges to be tackled carefully for ubiquitous medical care. Sustainability (i.e., longer battery lifetime), energy efficiency, and reliability are the vital ingredients for wearable devices to empower a cost-effective and pervasive healthcare environment. Thus, the key contribution of this paper is the sixth fold. First, a novel self-adaptive power control-based enhanced efficient-aware approach (EEA) is proposed to reduce energy consumption and enhance the battery lifetime and reliability. The proposed EEA and conventional constant TPC are evaluated by adopting real-time data traces of static (i.e., sitting) and dynamic (i.e., cycling) activities and cardiac images. Second, a novel joint DL-IoMT framework is proposed for the cardiac image processing of remote elderly patients. Third, DL driven layered architecture for IoMT is proposed. Forth, the battery model for IoMT is proposed by adopting the features of a wireless channel and body postures. Fifth, network performance is optimized by introducing sustainability, energy drain, and PLR and average threshold RSSI indicators. Sixth, a Use-case for cardiac image-enabled elderly patient’s monitoring is proposed. Finally, it is revealed through experimental results in MATLAB that the proposed EEA scheme performs better than the constant TPC by enhancing energy efficiency, sustainability, and reliability during data transmission for elderly healthcare.
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