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Augmented Coaching Ecosystem for Non-
obtrusive Adaptive Personalized Elderly Care on
the Basis of Cloud-Fog-Dew Computing
Paradigm
Yu.Gordienko1*, S.Stirenko1, O.Alienin1, K.Skala2, Z.Soyat2, A.Rojbi3, J.R.López Benito4, E.Artetxe
González4, U.Lushchyk5, L.Sajn6, A.Llorente Coto7, G.Jervan8
1 National Technical University of Ukraine "Igor SIkorsky Kyiv Polytechic Institute" (NTUU KPI), Kyiv, Ukraine
2 Ruder Boskovic Institute, Zagreb, Croatia
3 University of Paris 8, Paris, France
4 CreativiTIC Innova SL, Logroño, Spain
5 Medical Research Center "Veritas", Kyiv, Ukraine
6 University of Ljubljana, Ljubljana, Slovenia
7 Private Planet, London, United Kingdom
8 Tallinn University of Technology, Tallinn, Estonia
* yuri.gordienko@gmail.com
Abstract - The concept of the augmented coaching
ecosystem for non-obtrusive adaptive personalized elderly
care is proposed on the basis of the integration of new and
available ICT approaches. They include multimodal user
interface (MMUI), augmented reality (AR), machine
learning (ML), Internet of Things (IoT), and machine-to-
machine (M2M) interactions. The ecosystem is based on the
Cloud-Fog-Dew computing paradigm services, providing a
full symbiosis by integrating the whole range from low level
sensors up to high level services using integration efficiency
inherent in synergistic use of applied technologies. Inside of
this ecosystem, all of them are encapsulated in the following
network layers: Dew, Fog, and Cloud computing layer.
Instead of the "spaghetti connections", "mosaic of buttons",
"puzzles of output data", etc., the proposed ecosystem
provides the strict division in the following dataflow
channels: consumer interaction channel, machine
interaction channel, and caregiver interaction channel. This
concept allows to decrease the physical, cognitive, and
mental load on elderly care stakeholders by decreasing the
secondary human-to-human (H2H), human-to-machine
(H2M), and machine-to-human (M2H) interactions in favor
of M2M interactions and distributed Dew Computing
services environment. It allows to apply this non-obtrusive
augmented reality ecosystem for effective personalized
elderly care to preserve their physical, cognitive, mental and
social well-being.
I. INTRODUCTION
A. Background
The advances in medicine and living standards in the
last century have resulted in a significant increase in the
number of elderly people in Europe and most other
developed countries in the world. Over the next decades,
the worldwide number of older people will further
increase dramatically. In Europe, this development is even
more pronounced: for example, in Portugal, Spain, Croatia
and other European countries, the old age dependency
ratio, which gives the quotient of people 65+ will reach
~30-36% with pan-European average value up to 29.6% in
2050 [1]. These demographic changes have drastic
structural, societal and economic implications, and
challenge elderly care stakeholders like policymakers,
families, businesses and healthcare providers alike. The
ever increasing percentage of old people in the most
advanced Western and Eastern countries is posing a great
challenge in social healthcare systems. The effort required
by formal caregivers for supporting older people can be
enormous, and this requires an increase in the efficiency
and effectiveness of today care. One way for achieving
such a goal is the use of information and communication
technologies (ICTs) for supporting and assisting people in
their own homes.
Older generations need to be included as active and
integral pillars of our society instead of being isolated in
the special elderly care facilities. They should remain
active members of the work force as long as possible,
since the traditional assumption that retirement equals the
worker’s final exit from the labor force does not hold true
any longer. The required transition of society can only be
successful if huge efforts are made on various levels to
foster independence of this age group, from more flexible
employment arrangements, remote services in care giving
(telecare), support of independent living (ambient assisted
living - AAL), access to information, access to
transportation (accessibility), to specific communication
services and devices as well as entrepreneur approaches in
educational offers like life-long learning (LLL).
ICT is believed to play a key role in all these fields.
However, ICT can successfully contribute to their
individual well being, and help to meet the challenges of
an aging society in general, if ICT could be non-
obtrusively adapted to the older adults’ knowledge, needs,
and abilities. Furthermore, the whole of our society can
gain enormous benefits by integrating the knowledge and
skills and high degree of experience the elderly can
provide to the coming generations, in all aspects of living,
from technological expertise in any field, to everyday
living experiences. Current ICTs range from systems for
reminding appointments and activities [2], for medical
assistance and tele-healthcare [3], to human-computer
interfaces for older persons or people with special needs
[2]. Usually, these ICTs incorporate application dependent
sensors, such as sensors, cameras or microphones. Many
studies [4] have demonstrated that people prefer non-
invasive sensors, such as microphones, over cameras and
wearable sensors, and this drove the scientific community
to develop systems and technologies based on non-
invasive approaches only.
ICTs have become an integral component of
everyone’s life, including older adults, to continue
education, obtain health information, communicate and
exchange experiences, as well as online banking/shopping
etc. Though recent research has shown that older adults
are receptive to using ICTs, a commonly held belief is still
prevalent that supports the idea that older adults are
unwilling to use ICTs due to bodily and cognitive decline
in working memory, attention, and spatial abilities [5,6].
The main problem is that despite the current progress
of elderly care facilities the vast majority of EU older
people wish to live independently at home as long as
possible; meeting their needs can be a major challenge [7].
The different providers often work under conditions of
poor coordination among ICT experts, elderly caregivers,
patients, and their families [8-9].
B. State of the Art (Similar Works)
ICTs are promising for the long-term care of elderly
people. As all European member states are facing an
increasing complexity of health and social care, good
practices in ICTs should be identified and evaluated.
Recently, several projects funded by DG CNECT were
related to Active and Healthy Ageing (AHA). They
provided: independent living and integrated services —
BeyondSilos (http://beyondsilos.eu), integrated care
coordination, patient empowerment and home support —
CareWell (www.carewell-project.eu), set of standard
functional specifications for an ICT platform enabling the
delivery of integrated care to older patients — SmartCare
(http://pilotsmartcare.eu/). Some successful initiatives
were initiated in Europe and supported by EU, for
example, European Rosetta project [11], research network
for design of environments for ageing (GAL) [10],
assisted living environment for independent care and
health monitoring of the elderly (ENRICHME),
responsive engagement of the elderly promoting activity
and customized healthcare (REACH), digital environment
for cognitive inclusion (DECI), integrated intelligent
home environment for the provision of health, nutrition
and mobility services to the elderly (MOBISERV),
unobtrusive smart environments for independent living
(USEFIL), open architecture for accessible services
integration and standardization (OASIS) and others.
C. Unresolved Problems
These innovations can improve health outcomes,
quality of life and efficiency of care processes, while
supporting independent living. However, in the face of
new challenges some disruptive innovations should be
proposed and implemented, and the new
challenges/problems should be addressed. The potential
radically new solution should take into account the
following additional set of aspects/problems related to
quite different (1) targeted communities; (2) level of
functional (technical/computer/digital) literacy of the
targeted communities; (3) realistic time of massive
implementation of the proposed technologies for these
communities with people of various functional literacy;
(4) differences in national and geographical mentality as
to elderly care in Europe.
Targeted communities in the context of elderly care
consist of:
• individuals — self-directed elderly care, where
elders control both the objectives and means of
elderly care;
• families, i.e. individuals inside family and/or
supported by family — informal elderly care,
where elders control the means/tools, but not the
objectives of elderly care;
• assisted elderly care — non-formal elderly care,
where elders control the objectives but not the
means/tools of elderly care;
• specialized elderly care facilities — formal elderly
care, where elders have no or little control over
the objectives or means/tools of elderly care.
Level of functional/computer/digital literacy of the
targeted communities (in the order from the lowest to
highest): absolute computer illiteracy, digital phobic, basic
computer literacy, digital immigrants [12], intermediate
computer literacy, digital visitors [13], proficient
computer literacy, digital residents [13], digitally native
[12].
The proposed time of massive implementation of the
proposed technologies/environments depends on the
maturity of the available solutions and the
functional/computer/digital literacy level of the targeted
community: now (the current mature technologies can be
applied immediately), in the nearest future (the
perspective technologies can be mature in the nearest 2-3
years), in the much later future (the perspective
technologies can be mature at unknown time).
Differences in national and geographical mentality as
to elderly care in Europe were observed and reported
elsewhere [14,15]:
• informal care is more common in South than in
North Europe;
• informal care is more common in the "new"
member states in the "East" than in the "old"
member states in the "West";
• informal care provision to someone outside the
household is comparatively rare in the
Mediterranean countries, elderly care to someone
in the home is more common in these countries
than in the EU-states on average;
• the low proportion of people providing care
within households is explained by the rarity of
multigenerational households in Nordic Europe.
The proposed Augmented Coaching Ecosystem for
Non-obtrusive Adaptive Personalized Elderly Care (AGE-
Care) is focused on the provision of the virtual care,
support, and coaching to elderly people in the various
targeted communities and with different
functional/computer/digital literacy of the targeted
communities. It will be achieved by enhancement of
available ICT-enabled elderly care services, development
of new ones, and their application with the tight
coordination, monitoring, self-management and caregivers
involvement inside the proposed AGE-Care ecosystem.
II. CONCEPT, MAIN AIMS, AND BASIC PRINCIPLES
A. General concept
The proposed AGE-Care ecosystem is assumed to be
based on the integration of the several new ICT
approaches and available ones, which should be enhanced
by the radically new ICT based technologies concepts
(shown in Figure 1) in favor of the elderly care
stakeholders. They include multimodal user interface
(MMUI), augmented reality (AR), machine learning
(ML), Internet of Things (IoT), Internet of Everything
(IoT), machine-to-machine (M2M) interactions, based on
the Cloud-Fog-Dew computing paradigm services,
providing a full symbiosis by integrating the whole range
from low level sensors up to high level services using
integration efficiency inherent in synergistic use of
applied technologies.
The AGE-Care ecosystem is assumed to penetrate any
organizational, national, mental, gender, and cultural
division lines, boundaries, and limits. It will use the most
appropriate available resources and elderly care,
healthcare, and social care services. The AGE-Care
ecosystem will be based on open standards, multi-vendor
interoperability, collaboration with ICT suppliers and
ICT-related service providers.
B. Main aims
The main aims of AGE-Care ecosystem are as follows:
• to develop, test, and validate radically new ICT
based concept of non-obtrusive augmented reality
learning and coaching ecosystem for effective
personalized elderly care to improve and maintain
their independence, functional capacity, health
status as well as preserving their physical,
cognitive, mental and social well-being,
• to develop and implement the synergetic user-
centered design of intuitive human-to-machine
(H2M) and machine-to-human (M2H) interactions
on the basis of information and communication
technologies (ICTs) including internet of things
(IoT), multimodal augmented reality (AR), and
predictive machine learning (ML) approaches,
• to decrease the physical, cognitive, and mental
load on elderly care stakeholders by decreasing
the secondary human-to-human (H2H), human-to-
machine (H2M), and machine-to-human (M2H)
interactions in favor of machine-to-machine
(M2M) interactions and distributed Dew
Computing services environment,
• to overcome cognitive, mental, institutional,
regional, and national barriers enabling delivery
of integrated elderly care on the European scale
by joining efforts across governmental, non-
governmental, and volunteer elderly care
organizations and individuals.
The following radically new ICT based main concepts
and approaches are planned to be used to reach these aims
(Figure 1):
• multimodal user interface (MMUI) — for the
more accessible and effective intuitive H2M/M2H
interaction on the basis combination of creative
"artistic" approaches;
• augmented reality (AR) — for non-obtrusive
H2M/M2H interactions,
• machine learning (ML) — for virtual decision
making and virtual guidance of users,
• Internet of Things (IoT) + Internet of Everything
(IoT) + machine-to-machine (M2M) interactions
encapsulated inside Dew computing layer — to
hide "behind the curtains" the mental and
cognitive overloads, and shift them from H2H to
M2M interaction zone.
C. Basic Principles
The proposed open AGE-Care ecosystem is based on
the several basic principles:
• dominance of machine-to-machine (M2M)
interaction over human-to-human (H2H);
• multimodal instead of single-modal interactions;
Figure 1. The integration concept of non-obtrusive augmented reality
learning and coaching ecosystem for effective personalized elderly care.
• non-obtrusive augmented reality feedback instead
of obtrusive direct communication with numerous
high-tech sensors, actuators, devices, and gadgets;
• virtual decision making and coaching by machine
learning instead of real human-related services,
• short adaptive learning curve by selection of
specific and context-related virtual coaching
methods based on LLL principles instead of the
obsolete and awkward "user guide" and "context
help" approaches;
• highly distributed service oriented local and
distance communication and service facilities.
III. STRUCTURE, WORKFLOWS, AND SOME EXAMPLES
A. Hierarhical Structure
This basic hierarchical structure of the AGE-Care
ecosystem is virtualized at different levels and visually
presented in Figure 2. In contrast to the current concept of
elderly care (Fig. 2a), the proposed concept (Fig. 2b) will
allow stakeholders:
• to decrease significantly (and avoid in the most
situations) the level of H2H interactions —by
emphasis on the M2M interactions for the basic
technological scenarios;
• to avoid technological H2H interactions, but
emphasize emotional H2H interactions in favor of
emotional positive feedback from elderly people
due to involvement of augmented multimedia
channels like observed and even performed art,
music, dance, etc.;
• to increase efficiency of H2M/M2H interactions
— by introduction of multimodal communication
channels like audio, visual, tactile, odor, etc., so-
called Augmented Reality Human-to-IoT
(ARH2IoT) interactions;
• to increase the acceptance level of the available
ICT technologies for elderly care — by providing
their functional abilities through non-obtrusive
augmented reality pathways;
• to eliminate the gap between the newest available
ICT technologies for elderly care and computer
literacy of the targeted communities — by
context-related, problem-based, and personalized
virtual AR-related coaching;
• to decrease the market entry threshold for the
future ICT technologies for elderly care — by
providing the related open platform specifications
based on the best practices and lessons learned
during the project;
• to provide more security and privacy — by the
localization of the personal consumer data at the
lower scales of the AGE-Care ecosystem.
B. Workflows and Network Layers
Inside of AGE-Care ecosystem all workflows are
encapsulated in the following network layers:
• Dew computing layer: the raw sensor data and
basic multimodal actuator actions are
concentrated, pre-processed, and resumed in the
smallest scale local network (Dew) at the level of
the IoT-controllers (individuals) and shared with
the upper Fog computing layer;
• Fog computing layer: the resumed IoT-controller
data and advanced actuator actions are located in
the medium scale regional network unit (Fog) at
the level of the IoT-gateway (family/room/office)
and shared with the lower Dew computing layer
and upper Cloud computing layer;
• Cloud computing layer: the accumulated IoT-
Figure 2. The current concept of elderly care (a, top
), and the proposed
concept of Augmented Coaching Ecosystem for Non-obtrusive
Adaptive Personalized Elderly Care (AGE-Care) (b, bottom).
gateway data are thoroughly analyzed by ML
methods to provide virtual decisions and coaching
advices in the highest scale global network
(Cloud) at the level of the global computing
centers (hospitals, healthcare authorities,
associations, corporations, etc.) and delivered to
the lower Fog and Dew Computing layer.
C. Communication Flows and Interactions
The typical communication flows inside the AGE-
Care ecosystem are schematically shown in Fig. 2b by
arrows, where the higher emphases (in contrast to the
current concept of elderly care) are placed on:
• ARH2IoT interactions — under Dew computing
layer: green arrows depict the main dataflows
from/to consumers by the familiar communication
channels and devices, but with context-sensitive
information provided by the multimodal
augmented reality;
• M2M interactions — mainly inside Dew
computing layer: light blue circle depicts the
undercover dataflows among sensors and
actuators, which are laid in the base of the
multimodal augmented reality in ARH2IoT
interactions;
• Cloud-Fog interactions — between Cloud and
Fog computing layers: red arrows denote the
familiar dataflows between the global computing
centers and the medium scale network unit (Fog)
at the level of the IoT-gateway
(family/room/office);
• Cloud-Dew interactions — between Cloud and
Dew computing layers: blue arrow denotes the
dataflows between the global computing centers
and the IoT-controllers.
It will allow to decrease cognitive overload on the
stakeholders, because in the current concept of elderly
care (Fig. 2a) the stakeholders are overwhelmed by the
everyday increasing variety of the newest ICT
technologies, the related devices and unusual practices. In
the current paradigm of eHealth and elderly care, the
stakeholders have to go by the long, complicated, and
non-familiar learning curve to leverage the new ICT
technologies. In contrast to it, the AGE-Care ecosystem
proposes them to use the familiar information pathways
(devices like television and radio broadcasting, landline
phone communication), that seem to be the same old
things, but actually enhanced by newest AR and AI
technologies under the hood.
Instead of the "spaghetti connections" to the numerous
sensors, actuators, devices, and gadgets with sporadic
dataflows, "mosaic of buttons", and "puzzles of output
data" for each device/technology, etc. (Fig. 2a), the AGE-
Care ecosystem will provide the strict division in the
following dataflow channels (Fig. 2b):
• consumer interaction channel — by allowing
feedback data from all applied eHealth and elderly
care ICT technologies through augmented reality
pathway only at ARH2IoT layer;
• machine interaction channel — by integration of
all sensor/actuator technologies and isolation of
their raw data at Dew computing layer,
• caregiver interaction channel — by integration of
Dew, Fog, and Cloud computing layers.
In general, the AGE-Care ecosystem will decrease the
high cognitive load on customers, increase the efficiency
of caregivers, and provide a unified way for incorporation
of any future ICTs by division of dataflows into the above
mentioned consumer, machine, and caregiver channels.
This work will include the necessary formalization
procedures: standardization, definitions of customer and
stakeholder interfaces, identification of data models and
data processing tools, and privacy and security policies
and recommendations.
The necessary conditions for incorporation of the
available and future ICTs to the AGE-Care ecosystem are
mostly related with adaptation to the paradigms of:
• multimodal augmented reality (AR) data output
for consumers;
• Dew computing (and available M2M standards
inside it) for basic and automatic decision making;
• multilayer interaction between Cloud, Fog, and
Dew computing for advanced (mostly automatic
and limited manual) decision making.
D. Some Implemented Combinations of Components
Several combinations of the new ICTs (which are
actually the components of the AGE-Care ecosystem) are
already implemented by authors and their detailed
explanation and related background can be found
elsewhere in the related publications, for example:
• Frameworks for Integration of Workflows and
Distributed Computing Resources: gateway
approaches in science and education [16-18];
• Dew (+ Fog + Cloud) computing + IoT + IoE:
the conceptual approach for organization of the
vertical hierarchical links between the scalable
distributed computing paradigms: Cloud
Computing, Fog Computing and Dew Computing,
which decrease the cost and improve the
performance, particularly for IoT and IoE [19];
• AR + visual + tactile interaction modes: to
provide tactile metaphors in education to help
students in memorizing the learning terms by the
sense of touch in addition to the AR tools [20,21];
• ML + visual + tactile interaction mode: to
produce the tactile map for people with visual
impairment and recognize text within the image
by advanced image processing and ML [22];
• IoT for eHealth (wearable electronics) + ML +
AR + brain-computing interface + visual
interaction mode: to monitor, analyze, and
estimate the accumulated fatigue by various
gadgets and visualize the output data by AR
means [23-25].
IV. CONCLUSIONS
The proposed integrated ecosystem provides the basis
for effective personalized elderly care by introduction of
multimodal personalized communication channels. It
allows end users to get cumulative effect from mixture of
ICTs like IoT/IoE, multimodal AR, and predictive ML
approaches. As a result, it could exclude obtrusive
H2M/M2H technological interactions by delivering them
to M2M interactions encapsulated in Dew Computing
layer, and enhancing the pleasant multimedia H2M/M2H
intuitive interactions. It hides "behind the curtains" the
mental and cognitive overloads by: shifting the most
portion of ICT-related interactions from H2H to M2M
zone; using AR pathways for delivering status information
and advices for elderly end users; increasing AR-readiness
of the available ICTs for AR-output of data for non-
obtrusive H2M/M2H interactions, and improving every-
day communication and service needs. It could be the
integral platform and paradigm for overcoming cognitive,
cultural, mental, gender/ethical, institutional, regional, and
national barriers and enabling the targeted delivery of
integrated elderly care on European and worldwide scale
by joining efforts across governmental, non-governmental,
and volunteer elderly care organizations and individuals.
In this way elimination of any kinds of “borders” between
people at European (and worldwide) scale by targeted
efforts can strengthen the relationships between the
different age categories of people and various elderly
communities despite their intrinsic or imposed differences.
ACKNOWLEDGMENT
The work was partially supported by Ukraine-France
Collaboration Project (Programme PHC DNIPRO)
(http://www.campusfrance.org/fr/dnipro), EU TEMPUS
LeAGUe project (http://tempusleague.eu), and Croatian
Centre of Research Excellence for Data Science and
Advanced Cooperative Systems.
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