Remote health monitoring of elderly through wearable sensors
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Due to a rapidly increasing aging population and its associated challenges in health and social care, Ambient Assistive Living has become the focal point for both researchers and industry alike. The need to manage or even reduce healthcare costs while improving the quality of service is high government agendas. Although, technology has a major role to play in achieving these aspirations, any solution must be designed, implemented and validated using appropriate domain knowledge. In order to overcome these challenges, the remote real-time monitoring of a person’s health can be used to identify relapses in conditions, therefore, enabling early intervention. Thus, the development of a smart healthcare monitoring system, which is capable of observing elderly people remotely, is the focus of the research presented in this paper. The technology outlined in this paper focuses on the ability to track a person’s physiological data to detect specific disorders which can aid in Early Intervention Practices. This is achieved by accurately processing and analysing the acquired sensory data while transmitting the detection of a disorder to an appropriate career. The finding reveals that the proposed system can improve clinical decision supports while facilitating Early Intervention Practices. Our extensive simulation results indicate a superior performance of the proposed system: low latency (96% of the packets are received with less than 1 millisecond) and low packets-lost (only 2.2% of total packets are dropped). Thus, the system runs efficiently and is cost-effective in terms of data acquisition and manipulation.
KeywordsInternet of things Ambient assisted living Healthcare Sensor network Real-time monitoring Wireless sensor networks
Technology has become an essential and integral component in modern day living and influences the way in which we all live and work. For the most part technology has a positive impact as its helps us to manage and control our daily routines. However, modern day technology has an important role to play in other domains such as overcoming the many challenges facing health and social care. Ambient Assistive Living (AAL) is the concept of generating new living spaces that combine social environments with the latest technology to produce products and services that significantly enhance the quality of life of the occupant. Many areas of research and technology contribute to assistive solutions, with numerous trials undertaken to assess their feasibility. The application of several domains has been exploited to enable and improve the use of assistive technologies within the household. The collection and integration of such technologies is often referred to as Ambient Intelligence or AAL, which aims to support people by enabling them to achieve their everyday objectives. AML is not only regarded as the integration of various technologies (i.e. sensors, computing etc.) and related domains (i.e. computer science, engineering, medicine and social sciences) but focuses on incorporation and applicability of such technologies. The main function of any system is the ability to identify human activities from the acquired sensory data. This is achieved by applying meaning (which is usually in the form of medical insight) to observations.
The underpinning concept of many AAL technologies is the Internet of Things (IoT) paradigm. IoT is a framework that enables developers to connect several devices, systems and technologies together to achieve certain tasks  such as health monitoring. The rapid growth of wearable sensors (e.g., fitness trackers, wearable biometric sensors etc.) changes the way we collect and analyse data [4, 6]. A large proportion of IoT technology directly builds on the motivation of monitoring our daily activities (e.g., monitoring steps and diets throughout the day). For instance, using a smart wristband, which is commonly used for monitoring health and fitness. Smart phones which are based on the electrocardiogram can be used to assess and analyses heart’s condition . The Brain Sensing Headband monitors brain activities and transmits information to a computer, smart-phone, or tablet via Bluetooth . Wearable sensor technologies and IoT have great potential to improve our lifestyle, such as providing healthcare monitoring systems which are responsible for tracking and managing our health and fitness. Additional features such as real-time communication enables the data to be transmitted and analysed by healthcare providers while providing the means to identify and act upon worrying behaviour or symptoms. Raising the efficiency and overall cost effectiveness of healthcare is one of the vital goals of modern society.
The worldwide population has increased significantly which is largely due to the advancement in medicine while the number of elderly people who need more care [35, 36] is increasing. For many countries, the emergence of an ageing population is fast becoming an increasing public health concern. Healthcare costs are continuously rising and the quality of services does not meet the needs of modern society. Remote real-time health monitoring provides one possible solution to overcoming these challenges. Constantly monitoring the health of elderly people via wearable devices and fitness trackers proved different avenues in facilitating Early Intervention Practices EIP and reducing release rates. Surveys undertaken in Europe and the United States  indicate that patients prefer to undergo treatment and healthcare monitoring in their own homes rather than being hospitalised. Monitoring patients remotely have several advantages, which include EIP, reduce relapse rates, while reduction in costs patient and convenience become a possible outcome .
Governments and healthcare organisations spend millions of pounds every year in providing high quality of services for healthcare sectors . Monitoring patient’s vital signs such as temperature, blood pressure and heart rate are one of the major aspects of today’s healthcare services . Bed blocking [16, 27, 40], where a patient is medically ready to be discharged but is delayed because of inadequate care, support and rehabilitation services outside hospitals has become increasingly common within the healthcare sector. Until recently, patients were monitored manually in clinics and or hospitals for long periods while keeping accurate reports and records for future use by doctors and other clinicians if needed . However, by combining multiple technologies such as smart-phone applications, wearable devices and sensors, a patient’s well-being can be accurately monitored remotely.
Our research motivations come from the interest in enhancing the living facilities in general, and our healthcare in particular. Healthcare services in our modern societies become capable to meet the need of individuals, because, on one hand, the significant growth of population leads to more consumers in our current and static healthcare systems. On the other hand, healthcare investment in modern technologies to handle these populations is still limited. Moreover, similarly to other people we are substantially care about our health and interested in constantly measuring our physical activities any-where at any-time using various healthcare and fitness trackers devises such the smart watches. In addition, while we are doing our day by day jobs we care about our elder and disabled people health when we leave them home alone for a long period. Therefore, it is becoming essential to engage technologies such as healthcare sensors and wearable with our healthcare systems, in order to have a safer and convenient environment for everyone to live in.
The growing elderly population, accompanied by the increasing prevalence of chronic diseases associated with ageing, will have profound implications for the health care system for decades to come . Therefore, we are proposing a system which enables continuous monitoring for elderly people health in real-time to prevent chronic diseases, thus preventing hospitalisation that burden the healthcare systems and costs. This paper presents a framework which utilises a smart-phone app and Wearable Sensors for Smart Healthcare Monitoring System (SW-SHMS) for elderly people. The system accumulates patient’s physiological data via wearable sensors (i.e., pulse, oxygen etc.) of elderly people in real-time. The data is transmitted to a data repository, where it will be stored and checked for any abnormality. Thus, any detection of disorder in a patient’s vitals will be reported to patient’s doctors and/or hospital in real-time to act on quickly and prevent a number of problems, such as, a sudden heart attack. Technologies are capable of providing patients physiological data from their locations to physicians anywhere in real-time, therefore, enabling remote remediation . For example, data such as blood oxygen saturation, heart-rate, and blood pressure can be measured via wearable devices and transmitted from patients locations to their doctors in real-time. This enables doctors and patients to communicate remotely. Providing such a system that can effectively monitor an elderly person physiological activity at regular intervals could detect diseases and other complications earlier . Especially, in the case of elderly people who are much likely to have a disorder in their physiological data . It is an utmost necessity to develop new methods and technologies in order to improve health services for the elderly community at an affordable price with ease of use while ensuring maximum comfort and independence.
The rest of the paper is organised as follows: related work for the proposed system is discussed in Section 2. The SW-SHMS system and its associated components are described in Section 3. A prototype system for the SW-SHMS is described in Section 4. The results from the implantation are presented in Section 5. Finally, the paper is concluded in Section 6 with a potentials for future directions of this work.
2 Related work
Recent advances in research and technology have broadened the horizons of the AAL platforms committed to assist ageing adults in their own residence. Wearable sensors have played a crucial role in the development of non-invasive strategies to monitor people activities. The purpose of this section is a two-fold: (i) reviews of similar systems/applications; (ii) The state of the art technologies in the field of sensors in remote monitoring.
The increase in lifestyle related disease along with the increase of the ageing population it becomes crucial to develop systems that facilitate the monitoring of health status independent at home and work. One important trend is that both the acquisition and processing of physiological data are moving out from the hospitals, allowing patients to stay at home or at work .
Several research groups and industry have initiated projects in the AAL area, for instance My-Heart , an EU FP6 project with Philips Research group focusing on vital sign monitoring to save lives by providing earlier diagnoses, closer monitoring and a means to help people manage any heart problems. In addition, HeartCycle project [11, 24], also coordinated by Philips, provide a closed-loop disease management solution being able to serve both Heart Failure (HF) patients and Coronary Heart Disease (CHD) patients, including possible co-morbidities hypertension, diabetes and arrhythmias. This has been achieved by multi-parametric monitoring and analysis of vital signs and other measurements. In the industry, the Continua Health Alliance [33, 44], which started in 2006 to enable an interoperable personal telehealth ecosystem, has become a major force in the personal telehealth domain, and over a dozen interoperable products have already been certified. Continua  has also made great progress in defining interoperability for the LANand WANinterfaces and now enables end-to-end interoperability .
Furthermore, Several projects have been conducted on the AAL framework and we have arranged them under their main functions. We first enumerate a group of projects focused on allowing people to age at home, being some of them funded by the Assisted Living Joint Programme1 projects fund . The project Aware Home [3, 12] uses a wide variety of sensors, covering from specifically designed smart floors to more typical video and ultrasonic sensors, together with social robots to monitor and help older adults [3, 10]. The Ubiquitous Home Project  is also centred in residents monitoring through a robot, which includes a dialogue based interface and operates as an intermediate platform between the smart home and the end users . In addition, some projects have focused on the monitoring patients suffering from a chronic disease, such as CASAS project , which use the smart-homes environment to monitor patients suffering from dementia, which is similar to ENABLE project  with the goal of giving them more autonomy in their lives. While, DOMUS  and IMMED  projects focus on behaviour recognition for patients suffering from Alzheimer disease .
Grenoble Health Smart Home  proposed a set of tools to measure patients activity in hospitals via several indicators of their activities in daily living, mobility, and repartition of stays and displacements. In the Gloucester Smart House project , a telehealth system was designed, based on lifestyle monitoring, with the pretension of continuously gathering information about the patients activity during daily routines. Some of these systems also sample biomedical constants, like in theWelfare Techno House project , which collects automated electrocardiogram measurements. In the Aging in Place project , residents are tested for urinary tract infections and alerts are sent to the caregiver using a sensor network. The goal was to detect signs of illness earlier than traditional health care assessment.
2.2 Related technologies
The development of new and innovate technologies in modern day life and their ability to enhance our daily activities has lead researches to focus on their use within clinical domains. The improvement of healthcare systems is gaining significant focus from both researchers and industry alike, by providing more convenient and automated healthcare products . Research on remote monitoring technology is increasing due to rising in healthcare costs [35, 36] and the significant increase of ageing population . Therefore, developing new innovative technologies to solve healthcare issues became one of growing fields. In this section, we will investigate several recent researches and related work to the this research.
One of the smart home experiments to produce a smart environment for healthcare in , presented a cloud-based smart home called CoSHIE. This system collects physiological data through wearable sensors. Minh et al. , use a non-invasive sensor to gather information about patient’s daily activities. The case study in this work proves that this approach is effective to collect contextual information to study patient’s behavioural changes and recovery processes at their homes. The sensor collects data and sends it to cloud via home getaway. CoSHIE cloud has a number of layers which include the hybrid storage that includes the relational (MySQL), and non-relational (NoSQL) data-centre. The authors intended to sort data such as name, sex, diagnosis, fluid intake and Spo2. All sensors data are stored in the non-relational databases, while other information is saved in the relational databases. One of the limitations of this system is the lack of direct monitoring for patients from their doctors or caregiver in case of disorder or emergency. There are several works on using micro-controller such as Arduino and Raspberry Pi for remote monitoring in healthcare, for instance improving healthcare using Raspberry Pi to help in remote monitoring by Gupta et al. , focused on monitoring one parameter which is ECG sign. Raspberry Pi was use as getaway to collect the ECG data from different wearable machines and send it to the databases. Thus, in case any change in the data happens, the Raspberry Pi will update the databases using MySQLdb. In addition, a wireless network (GSM) used to send an alert to doctor in case of emergency. The data will be available to access by authorised people through web application.
Wireless sensor network [2, 9] is presented to the collection of physical data such as blood pressure (BMP180 sensor), heartrate (Pulse sensor) and ECG (ADS1292R sensor) which is widely used to collect vital signs. Sensors were connected to Raspberry Pi to send sensors data in real time to doctors. Chauhan et al.  mentioned that using a system that is able to provide communication between patient and doctor for sending the right data to doctor and receiving prescript from the doctor can reduce the number of visit to doctors and hospital significantly. This can clearly demonstrate that reducing patients being hospitalised can reduce costs significantly.
Predictive Analysis and Arduino as a gateway connected to sensors in patient’s body to get vital data  is another system with the same concept which aims to detect changes in received sensory data and sends messages to operators if something goes wrong. Using the combination of ada boosting and Naive Bayes algorithm from remote sensing. Dudakiya et al.  examine the algorithm by monitoring patient’s data manually for few days, and then collected to check with data gained from the sensors. In this study, Arduino is attached to the patient’s body and connected to Wi-Fi for data transformation. This cannot be the best practice interim of Arduino hardware size and accuracy in kept detached to patient body and connected to the Wi-Fi. The data monitored in this work is heart rate, RR, BG and BP without mentioning the type or technologies to measure the data.
WonedRing is a wearable sensor developed to detect and analyses user’s activities . The sensor use for elderly people to monitor their activities in daily bases and send it to their supporters such as family member, doctor or neighbours to know information about their health status to help them to live independent safely. The system challenges to solve the issues of recognizing the activity of the user such as walking, eating, running, brushing and etc. Also, finding a useful information from monitoring user activities and deliver these data to the authorised party. User’s activities are stored in databases then, the system filter these activities to get useful data and share it with supporters.
Mobile phone (Smart-phones) gateway to monitor blood pressure level via keep in touch (KIT)  used a close loop data transferring by connecting the mobile phone in Java to website that make all data accessible by everyone to monitor the blood pressure level. Testing blood pressure using Breakout barometric pressure sensor  through measuring the diastolic and systolic and report it to doctor in case of disorder detected. The normal blood pressure result is 120/80 mm Hg, if recorded sensory data are different than these data reported to doctor [14, 46].
Another example of using patient’s smart-phones as the gateway communicates with a wearable sensor for healthcare monitoring architecture is presented by Chanhee et al. . This system is developed to monitor patients through sensor impeded on users mobile or wearables such as heart rate sensor in smart watches. The solution for filtering the overload data (data provided by the sensor) constructed through databases querying facility. Sensor Network Query Processor (SNQP) and Sensor Network Query Language (SNQL) use to improve data acquisition for sensor network databases [26, 29]. SNQL and SNQP have different roles, the first query language support applications software advance expressiveness, while the query processor provides adequate algorithm for data accumulation from sensors.
In our research, we are using a pulse sensor connected to (Arduino Uno) to measure patient’s heart-rate and send all measurements to patient’s smart-phone application through a Bluetooth device. The mobile app will act as a gateway to transmit all patient’s data to cloud for data processing.2 The data then will be available to access through multi-platform software such as doctor’s platforms (i.e., website) to monitor and interact with patient’s data and all in real-time.
3 Proposed system
The AAL systems, in general, aim at allowing elderly, disabled, and people with chronic conditions live safely at their home environment. In addition, AAL systems enable collaboratively assisting environment controlled by health-givers (e.g., family, friends and medical staff). These systems consist of different kinds of wearable medical sensors, smart phones, wireless sensors and actuators, computer hardware, computer networks, software applications, which are interconnected to exchange data and provide services in an Ambient Assisted environment. The sensors and the actuators are connected with the AAL applications and patient’s gateways for sending medical data to data-center, and then become available for health monitoring purposes. In this section, we will describe the network topology and framework for AAL system. However, before presenting the network topology and framework model, system architecture is given to help understanding the infrastructure of SW-SHMS.
3.1 System architecture
Wearable devices (Patients Layer): A wearable device and smart-phone sensors will be connected to patient’s body to collect physiological data. Such sensors are measuring vital-sign, such as, blood oxygen saturation, skin temperature and heart-rate, variety of healthcare sensors are available today [9, 31, 34, 35, 41]. Monitoring these symptoms in patient’s body is very important, as any abnormal data could potentially end up with a disease [35, 41]. For instance, drop of oxygen level in the human body causes a sleep apnea, which leads to death. In addition, abnormal blood pressure causes a kidney disease or diabetes. The sensory data are transmitted to patient’s smart-phone app via Bluetooth device and ultimately to a cloud database. Moreover, sensors will operate to measure and send the data regularly without patient attention to make everything automated (i.e., IoT), thus, this enhance users quality of experience and make them more comfortable.
Cloud (Data Layer): The Cloud refers to the place where the system data is stored and processed. Cloud receive patient’s data from their smart-phone over internet to be sorted and than it become available for doctor’s inspections. In addition, all data analysis and processing will be held in the cloud for any disorder detection in patient’s data, thus, the abnormal changes in patient’s data will be categories based on patient status and diseases. All resulted data/info will be reported either to patient’s and/or doctor’s platform or emergency unit or both depend on patient status. Thus, Cloud enables collaboration and knowledge sharing through its infrastructure which allows medical professionals to host information, analytics and diagnostics about patients so that other professionals around the similar interests can immediately access the data. This reveals in faster prescriptions and real-time updates to patient’s data.
Monitoring platform (Hospital Layer): This layer is a doctor’s platform to monitor patient’s records and sensory data. The doctors will be able to inspect reports provided by the system from the cloud and they able to take actions. Data synchronisation in this platform in real-time by pulling all data from the cloud as soon as it become ready to use to keep doctors up to date with patient’s status, also to help paramedic to take early action in case of emergency before the situation getting worse and prevent hospitalisation.
3.2 Motivating scenario & network topology
Improving the efficiency of healthcare and biomedical systems is one of the considerable goals of modern society. During the system design time of a healthcare system, a number of factors should be considered. However, two significant and essential design factors should be taken into consideration and related to the scope of this paper, these are: Firstly, the healthcare system should be live for 24/7 with no downtime. Therefore, a caregiver can always monitor patients conditions. Secondly, data freshness, the data should be always up-to-date (aka, minimum delay). If the data is not fresh, or system is down there is a potential to cost human life. This section proposes a healthcare motivating scenario and the network topology for the proposed SW-SHMS system.
User Environment (wearable and sensors): this represents the percipient layer, where all medical sensors and wearable are connected to patients and operated over the IoT network (i.e., able to send and receive data over this network). Each sensor/wearable (e.g., pulse sensor) has a unique identifier and communication capability over the network to interact with the gateway. The data gathered within this layer can be biomedical data (directly from patient’s body) and/or environmental data (from the surrounding). The generated/gathered data in this layer are transmitted to the gateway via wireless or wired communication protocols, such as Bluetooth, ZigBee, and Wi-Fi.
Gateway: this part is responsible for interacting with patients sensors which are used to detect patient’s symptoms and perform prime processing on the sensed data. Outcome of this segment is a summary of patients conditions sent to the caregivers. In addition, the Gateway is also able to react upon symptoms abnormality when it’s been detected, for instance, sending a help-request (e.g., request for caregiver for assistant) or emergency-request (e.g., call for ambulance) as soon as a emergency situation is detected.
Cloud Data-center: this segment was deployed to act on data provided from the Gateway. Thus, the main data storage and disease detection activity are executed. This includes the machine learning (ML) activities for data training and analysis to detect diseases and abnormality.
Monitoring Platforms: this includes both patient’s platform and caregivers platform. Thus, each dashboard provided for data visualisation, monitoring, and functionality controls over the collected data from sensors. This platform requires the level of security access alongside with different access layers, for example, patients users can have the master level of access, while caregiver have restricted access according to the granted level of access.
The other components of the adopted network topology are used primarily to manage the network configuration settings and interactions among system platforms and its entities. Moreover, in the user environment, the local context management is available within the network to setup and manages sensor’s related functions such as active/inactive timestamps and frequency of sensing. Thus, the local context management interact with both local area network for deploying sensors configuration settings and updates, and the gateway to inform/update the services that are running within the network about the new changes. Similarly, within the could data-centre the data management component is existing to manage the data manipulation processes, such as data analysis, according to the services provided.
4 System requirements and implementation
4.1 System requirements
Healthcare solutions are carefully designed because it is crucial and involve dealing with patient’s life. Thus, developing an effective healthcare monitoring system, several factors need to considered, such as data availability, consistency, and reliability. However, the most important factor is data freshness that needs to be well supported during runtime [20, 31] by the healthcare systems which deal with people health. More precisely, when authorised people monitoring patient’s symptom, the data it has to be refreshed with high frequency so that doctor’s platform update continuously to pull the latest data from patients with warbles.
Healthcare systems rely on several approaches to transfer patient’s data from one platform to another. Some of these approaches can be internal, like data is already on one of the system platforms then it sends the data in a secure channel to other platforms. While other approaches require middle-ware devices to collect these data and send it to system platforms, this what so-called system Getaway . The basic gateways performance is to accumulate data from sensors or any wearable devices that measure patient’s physiological data, then send it to data-centres (e.g., Cloud data-centre). Thus, in this instance gateway can be seen as a bridging point, for example, the UT-GATE , is provide data monitoring from original data sources (e.g., sensors) to the main monitoring platform. Moreover, there are different type of devices that can be used as a gateway such as a network router, and smart-phones [15, 20, 34, 35]. The Gateway, can be any micro-controller that is programmed to act on the data able to transmit from one platform to another.
Wireless sensor network (WSN) is the core of smart healthcare monitoring systems, where it represents than main sources of data, thus providing symptom physiological data [8, 9, 41] from patient’s body. There are different type of sensor use in healthcare monitoring, each sensor has different capability , for instance, Pulse sensor use to measure heart-rate , BMP180 sensor measuring blood pressure , and ADS1292R sensor use for both ECG and respiration [9, 34]. These sensors provide data from patient’s body in real-time, and the output data is either in analogue format or digital format. Therefore, the sensors should be connected to an appropriate Gateway that is able to read sensors output data and act on it or transmitted to data-centre.
In this research, Arduino Uno and patient’s smart-phone will be used as gateway to prove the concept of the proposed solution. However, healthcare solutions must have elasticity design, to be adapted with growth of the system, and hardware components must be improved continuously, due to the fact that hardware components are enhanced rabidly these days.
5 Results & evaluation
SW-SHMS deals with patient’s data directly, therefore, system scalability and latency must be well examined to make sure that the system can achieve high performance in dealing with elderly people data and effectively monitor their health in real-time. In addition, system scalability is that the system should be fixable to expand accordingly with the growing number of the population and patients using the system. In this section, we highlight some results and evaluations obtained from the developed prototype in term of efficiency and scalability.
The results in Fig. 10 are promising, thus, the total number of sent packets is around 3500 packets, 3432 packets are received and only 68 are dropped by the network. The overall dropping ratio of all sent packets is around or less than 2.2% which is promising ratio and way lass than the acceptable threshold of 15% as everything above that rate noticeably deteriorates the availability of the network . Moreover, Fig. 10c show the total latency in millisecond (ms) for transmitted packets, thus, must packets are received with (0 ms) latency, however, the longer latency found where (6 ms) for less than 4% of the packets, out of the overall number of 5000 packets.
There is a high flexibility in the SW-SHMS system to grow with the increasing numbers of sensors and patients connected to the system. SW-SHMS can be expanded simply by adding more gateways that are capable for handling patient’s sensor data. Also, according to the workload such as data traffic several gateways can be added to collaborate with each other to handle the load, thus, enhancing system reliability and performance.
6 Conclusion & future work
The smart healthcare monitoring system (i.e., SW-SHMS) has been proposed in this paper to handle the challenges of providing home based healthcare monitoring and avoiding hospitalisation. The literature show that there is a great demand of producing an effective healthcare solution that monitor elderly people in their home and in real-time. SW-SHMS can highly contribute to provide comfortable, and safe environment for elder and disable people, thus, enable them to live independently without the fear of any emergency or critical healthcare situation through continuous monitoring of their health by SW-SHMS. Briefly, SW-SHMS accumulate patient’s physiological data via wearable sensors and transmit it to Cloud for data analysing and processing. Thus, any detection of disorder in patient’s data will be reported to patient’s doctors via hospital platform. SW-SHMS has a fixable architecture that can scale and expand easily, thus, providing a reliable and cost effective systems to monitor patients remotely. In addition, the results shows that the system could efficiently contribute to improve healthcare services by using the prosed impeccable SW-SHMS system which able to monitor patients symptoms remotely and in real-time.
Future developments and enhancements for the SW-SHMS are wide, for instance, the system can be extended to apply artificial intelligence and/or machine learning concepts to help predicting life threatening diseases early. Moreover, the system holds a fair amount of medical data which be invested to develop a recommendations system that able to give a recommendations about the health and diets for better life style. In addition, the network topology can expand to have Fog computing node which will help processing the data at the edge of the network instead of traveling for long distance to Cloud, also, Fog computing cloud reduce data traffic on the cloud and help to make better healthcare decisions due to it’s location within the local area network. Thus, Fog computing will be employed to reduce the packets loses by upgrading the network infrastructure to allow distributing several Fog nodes at the edge of the network. This will help in empowering the system in data acquisition and manipulation via Fog-Cloud collaboration model that act faster on the data packets before it gets lost.
EU Commission. Active and Assisted Living Programme. ICT for Ageing Well in a digital world. Available at: http://www.aal-europe.eu/..
It is worthy noting that local processing of collected data (i.e., on the Fog layer) is out of the scope of this paper.
patient.info (https://patient.info/symptom-checker) is a registered website in England and Wales use in most GPs.
Google Cloud platform. Available at https://cloud.google.com/storage/pricing.
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