We face enormous medical and health services demands due to the Covid-19 pandemic. This fact implies that the healthcare sector does not manage to cope with the considerable pressure from patients who experienced Covid-19, from which some fail to survive. The hospitals and medical practitioners everywhere were faced with significant stresses, and they were unable to cope with these problems. The patients were scared and unsatisfied with medical help, and the healthcare staff was exhausted everywhere. One of the solutions was implementing the concept of e-health with the usage of e-PHR of the patient and using some components of ambient assisted living (AAL), telemedicine, and telehealth. Many sensors for measuring vital signs of life are available on the market. Some applications can collect these data from IoT devices (sensors), wearables, or other devices that can help monitor the patient's healthcare condition in a pandemic situation. In this way, healthcare professionals can access patients' vital conditions and take actions such as e-prescription and e-referral. In the paper, we highlighted some aspects of possible healthcare improvement for the patients using AAL devices who can assist in improving access to medical and healthcare professionals, especially for the patients with chronic diseases, some disabilities, the elderly and children. This concept is based on the e-PHR concept, which understands that the patient owns their data and can access their e-Personal Health Record.
With the advent of the Covid-19 pandemic, the world became aware of the need for e-health and services related to telehealth and telemedicine. The workload of the health sector due to a pandemic has led to an increased demand for more health and medical services worldwide. Particularly aefcted were those citizens who have chronic diseases and some disabilities and need frequent health or medical care. It was clear that the world’s moves toward e-health services are accompanied by an intelligent environment that needs to be intensively developed to overcome the gap between the required services and those delivered. Suppose we add to this that more and more citizens are reaching old age, and thus their abilities are decreasing, and they need more attention and help from health and social sta.f In that case, it inevitably concludes that health and social workers are becoming more burdened. They should use new technologies that can help improve the quality of everyday life of this elderly population and relieve these employees by applying technology.
It should be noted here that the number of citizens with some disability is
constantly growing, and thus the need to provide services for them is growing.
Due to these facts and the ageing of the world population, new policies, systems
and technologies are needed to support healthy ageing and people with
disabilities [
Let us consider that the lack of physical activity can be connected with
ageing and low social activity, depression and function of cognitive decrease [
The AAL concept can include technologies with smart devices, wearables, and
part of the devices according to the concept of Smart cities that help this category of
citizens. These IoT sensors can be wearables / or static, IoMT class devices, various
auxiliary sensors and applications for monitoring metabolic processes, movement,
nutrition etc. Of course, all this data has to be collected, and for this reason, many
researchers worked on creating reference models for AAL. As a result, numerous
reference models and architectures have been developed [
The context of usage of AAL – indoor/outdoor environment can be restrictive for the technologies used. In addition, this usage influences whether they are invasive or not, in which citizens’ category they are applied, and whether they are people with chronic diseases, disabled persons or healthy persons. On the AAL usage influence, did we have to get just warnings, do some vital signs parameters have to be monitored, or do some correlations between data have to be detected as time sequences and functionalities are implemented? Many choices can be made for sensors and techniques with used methodologies in the diferent environments and the diferent users.
The scope of AAL systems is comprehensive and demands the inclusion of high numbers of stakeholders, making them even more complex. However, although several reference models (RMs) and reference architectures (RAs) have been proposed, we found that the standardization legislation and technological maturity in this field are still unsatisfactory in linking AAL systems with PHR (personal health records) of the patient. PHR is the core part of monitoring patients’ health conditions allowing them to receive health, social and medical services using e-health, telemedicine and telehealth, i.e. gaining consultant help from a distance if they need it.
For this reason, the paper is structured as follows. In the first section af ter the Introduction, the overview of related works highlights the AAL models and architecture that proposed solutions for the domain, focusing on those who connect AAL concepts with patients’ PHR and e-health services. The third section highlights the prerequisites for using AAL services with PHR and e-health services. This part highlights the ability of AAL systems that are very useful for the patients with special needs, especially for the elderly, that have to be provided with safe ageing and higher quality of life and health services through e-health. The needs and data challenges for the proposed reference model suitable for this AAL-PHR connection are considered in the fourth section. The data flow has to be defined according to previously described rules and selected RM for AAL-PHR. The fifth section provides a discussion and research results in order to highlight the proposed model. In this part, increasing the citizens’ digital health literacy to use the e-health services and create their PHR are considered. Diferent alert algorithms that have to be activated in the case of need (if some vital signs of life are out of normal parameters) are discussed in the paper. Concluding remarks draw some research conclusions and give some future action recommendations.
The substantial increase in the average age of the world population and the
growth of the number of elderly citizens inevitably increases the number of
citizens with reduced abilities for normal living, i.e., some disability or incapacity.
According to statistics, 8.5% of the population is over 65 years old [
The scenarios that AAL have to be ofered are complex. A vital source of this
complexity is the built-in heterogeneity of the end-user population when
arranging their homes and beyond, the physical limitations of users, etc. [
Due to the importance of the concept, many eforts have been made to create
a reference model (RM) and reference architecture (RA) of the AAL domain to
provide a framework for the development of this type of system. Eforts have led
to the creation of multiple architectural models and architectures that have been
evaluated according to numerous criteria [
However, the most appropriate architecture for integrating the AAL domain
with the PHR is the Feelgood [
However, this RA of Feelgood is not the most suitable according to the
evaluation [
Researchers are aware of the need for personalization in AAL systems, as
personalized environments may difer significantly from individual to general
population [
Many systems and methods have been developed for AAL systems to
monitor continuously biological, behavioral, or environmental data, implement
interventions, and assess their vital signs. By developing systems that collect data
from heterogeneous sensors and additional data from citizens, new information
can be obtained regarding physiological, psychological, emotional and
ecological conditions. [
The technologies used to develop AAL systems range from simple IoT and
IoMT devices to more sophisticated sensor networks of environmental sensors,
smart devices, camcorders, robots, and more. These diverse technologies use data
complex in size, heterogeneity, and sampling frequency and are often big data.
Data management inevitably has to involve complex communication protocols
with high-security controls because the sensitive personal data must be secured
and legal requirements must be met [2]. In addition, the energy consumption, the
possibility of detecting defects as well as interoperability between devices and
equipment vendors should be taken into account. Many more technical issues
should be considered when designing such a complex ALL-PHR structure [
Although they are widespread, the diversity of technologies requires meeting
specific requirements, such as being non-invasive, readily accepted by citizens,
and not influencing users’ daily activities. In addition to the wide range of de
vices, four main categories of technologies can be distinguished. The first cat
egory consists of wearables that require user acceptance. The second one is less
invasive, smart objects or furniture, the next category is related to environmental
sensors, and the fourth one covers social assistance robots [
Every day’s SMART devices are objects that use IoT / IoMT and sensors as
well as processors to identify them and communicate with them, i.e. to be smart
and remotely controlled, providing residents of the home/institution with a wide
range of possibilities: detecting anomalies, preventing injuries, increasing safety,
or assessing health problems. For example, the first signs of cognitive impairment
can be easily seen in behavioral variations during meal preparation or stove
handling. Smart devices in the home and their use are limited only by the imagination
of designers and users’ requirements and have the potential to monitor the daily
behavior of users at home and their habits and detect inappropriate behavior.
They can be smart boxes from which medicines, carpets, refrigerators, doors,
pillows, and many other devices are taken to provide data using diferent methods
and algorithms for the users’ daily activities. If these data are available to health
and medical staf, they can quickly conclude the patient’s health status [
Environmental monitoring sensors are non-invasive devices that can overly
warn of air pollution, temperature or other parameters and are helpful in the AAL
environment. These include devices for analyzing radio frequency devices and
microwave sensors, intelligent optical systems and more that can help detect
behavior and aid in movement, sleep etc. Not all data from these sensors are needed
in the patients’ PHR. As a consequence, some data can be entered into auxiliary
databases. They will then be extracted with the help of intelligent data, if
necessary [
Social assistance robots are a technology that is rarely used due to the still
high cost. However, it can be used to humanize some activities in hospitals,
rehabilitation centers. Wherever there is a need for frequent repetitive and quick
reactions, such as carrying food, lifting or grabbing objects, delivering, and releasing
medical staf. It is also important to personalize the human-robot interaction by
providing the robot with human-like social skills, such as natural language
processing, user emotion assessment, etc. [
All data collected from AAL systems have to be analyzed for AAL
situations. Some use data from heterogeneous sensors and advanced analysis
techniques such as Artificial Intelligence (AI), Support Vector Machines (SVMs), and
machine learning, which should lead to anomaly detection and intervention. The
cost of AAL systems largely depends on this. They generate vast amounts of data
to personalize systems and refine their reactions. Only some information obtained
from data using intelligent algorithms needs to be stored in the user’s PHR. Those
required for healthcare personnel include falls, loss of consciousness, balance,
physical inactivity, sensory measured vital signs of life, and similar data requiring
medical intervention [
However, it must be acknowledged that AAL systems play an essential
role in achieving greater well-being for the elderly and disabled by significantly
improving their quality of life and providing distance services, improving
autonomy, emergency treatment and comfort services [
The model that integrates AAL data with the PHR of the patient assumes
that the data collected by the AAL systems are following the health standards
FH7 and all data collected should be following the ontology used as a basis for
creating standard PHR [
It is especially necessary to analyze data for measuring vital signs of life that should alert medical personnel and caregivers to take swift action to save their lives or provide emergency care. Because these data collected from AAL systems are big data, it is often necessary to analyze them first using the big data analysis tools and machine learning methods that are most suitable for this purpose. These data can help detect the condition and the need to alarm the caregiver. These data have to be recorded as additional data that can be accessed via PHR by selected doctors or caregivers – medical staf [5].
We believe that it is necessary to create additional data that would lead to data detected as information from AAL systems. These additional PHR extended functionalities that will collect data on user health-related situations detected by AAL systems are important to healthcare professionals and patient care and should be subject to detailed analysis. For example, suppose we consider the wide range of AAL devices that collect data and alert users to the situation. In that case, it can not be ignored that the flood of health professionals’ data from such AAL generated data can lead to confusion, inability to analyze in time and information flooding. Therefore, it is necessary to define priorities for those data that mean life-threatening and alarming situations. Such requirements certainly require an interdisciplinary approach to solving the problem of involving medics and defining these priorities. Furthermore, to avoid information flooding in the PHR of the patient, it is necessary to determine the data that will be stored in the external tables that can be accessed through the PHR of the user. 4.
The data available to the patient and stored in the patient’s PHR are created
according to the FHIR standard ontology, which involves using patient data from
the patient-centric system. This cloud-based system [
An important issue for this paper is the part with the AAL Data Repository.
AAL data can be very extensive and collected in diferent formats. If it were pos
sible to obtain information on life-threatening situations for the patient, it would
mean that they would be received in the form of warnings, and alerts related to the
patient’s PHR from intelligent agents who would assess the situation according to
previously defined algorithms for generating alerts, depending on some devices
[
For the independent living of the elderly and maintaining a quality of life, one
of the key components of AAL applications is human activity recognition (HAR)
[
These are just a few examples of AAL usage that can be associated with
PHR and patient status warnings. The use of AAL technologies for warning and
improving the health care of patients in need is limited only by the imagination
of the designers of AAL-PHR systems. Some of these technologies are already
widely used in some hospitals, and the possibility of data connectivity and service
integration is increasing every day [
The model from Figure 1: High-level robotic system model [
The first step defines the needs of AAL sensors for a specific patient. In this
step, the needed sensors for this patient are defined, depending on his chronic
disease or motor impairments or some other problems. In this step, the
functionalities that have to be covered and the needed sensors for measuring vital signs of
life have to be defined. Examples of this type of AAL system can be seen in [
Collecting data in some big data formats for many of the sensory data is an
important part of being used to identify the behaviour of a specific PHR own
er using artificial intelligence, machine learning, and other AI methods used to
detect normal and abnormal behaviour of the patient or his vital parameters of
life. In the third step, it is necessary to define the conditions that will be subject
to warning of the selected doctor or caregivers and which should be defined as
changing states of indicators in PHR to activate the ALERT and to warn the
person responsible for providing care or the selected physician who has access to the
patient’s PHR [
The fourth step concerns the personalization of each patient’s behaviour and the development of a specifically tailored model for each patient. To adjust the parameters of a particular patient and his behavioural parameters to normal and ability to detect anomalies, a big data analysis is needed from the data collected by the machine learning methods and other AI methods that should lead to the specific set of settings of its parameters that should alert for disturbance of its normal state. This means that the mechanism AAL -data analysis – alert – sending data about the condition – time of intervention is activated immediately or a prediction for future deterioration of the condition and the period in which that deterioration will occur. The intelligent agent triggers and alerts the people in charge – caregivers or chosen doctors who immediately intervene for the patient and decide on future activities based on the analysis of the results obtained from the specific AAL systems. This AAL system can or cannot be part of the PHR, but is accompanied and available for analysis by selected physicians and caregivers.
The proposed model has a patient-centric architecture and strong AAA
security Keycloak server control. This strong Authentication control provides cross
border healthcare usage, routing patients to a suitable database server that
acquires patients’ data in the country of living. The patient can grant permission for
their PHR to the selected medical staf, enabling e-health services over distance.
The patient can use e-prescription and e-referral services [
This approach requires a complex AAL structure, support servers,
application and data service. Still, some of them can be done in a fog-computing
environment on the local network [
The psychosocial factors of human-technology interaction, communication, and their usage have to be considered when developing AAL systems and connecting them with the PHR of the users. Together with data usage and processing of AAL data, these factors are worthy of further investigation by the scientific community. The aim is to develop complex, intelligent systems for independent living support, improvement of healthcare services, tele monitoring and consultation, social participation and well-being.
In this paper, some attempts to propose a PHR-AAL model that will connect AAL data with alerts that are important for the health status of PHR owners are made according to the previously made analysis of PM and RA. This type of communication should be done at the necessary level of alertness for a condition that should be predefined and deviate from the patient’s normal behavior – whether it is movement or some vital signs of life with wearables or IoMT / IoT sensors measurement. The trend of deviations from normal behavior should be detected in advance, and algorithms have to be trained using machine learning or other intelligent methods. Alarms should be predefined, and the alert’s location should be determined to provide an immediate response to Caregivers / Selected doctors and allow an immediate response if needed. The need for this connection is increasing due to the workload of the medical staf due to the pandemic and the growing number of chronically ill, elderly and disabled citizens. They all need regular monitoring to ensure independent living with the help of AAL systems that provide multiple forms of monitoring of their health, condition and need for health and social care.
The model involves creating a patient-centric health record in a system in
which the patient owns the data and is the one who shares their health record with
the selected doctor that they decide to give grant permission and will need to take
care of their health. This model can be connected to health institutions that assist
patients with special needs and reap the benefits of the symbiosis of e-health,
PHR, telemedicine, AAL and remote assistance using IoMT, Mobile applications
and SMART devices related to the cloud with established security measures and
protection following the law on the protection of personal information [2, 5]. The
use of data from public data services does not require any higher security
mechanisms. It can be used to assess the risk of environmental conditions as exposome
data, at the place of residence [
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