Abstract
The growing global population has increased the necessity for technologies in the form of smart devices and software algorithms that can assist and monitor patients anywhere and at any time, enabling them to live an independent life. Real-time monitoring of patients is an essential issue in the telemedicine field. Medical experts apply real-time online telemedicine systems due to the possibility of efficient and timely healthcare services. The real-time healthcare monitoring systems are based on technologies such as wearable sensors and wireless technologies. Real-time health monitoring systems refer to strategies, installations, resources, and strategies that enable doctors and medical professionals to offer healthcare services remotely to treat, consult, and diagnose patients. Furthermore, one strategy that is drawing significant research consideration is a focus on real-time health monitoring applications based on the disruptive technologies of big data and the Internet of Things (IoT). The concept of real-time health monitoring systems decreases pressure in hospitals, improving homecare and reducing healthcare costs, particularly for patients with chronic illnesses and the elderly. This research paper compares different types of real-time health monitoring applications and different technologies deployed by the systems.
Keywords: Real-time Health Monitoring Applications, Chronic Diseases, Big Data, IoT
Introduction
An increasing number of aging populations globally in recent years has resulted in complex health issues such as rise chronic diseases and an increase in clinical and hospital expenditure (Nguyen, Mirza, & Naeem, 2017). Real-time and remote health monitoring plays a vital role in maintaining health for the elderly and individuals with chronic diseases. Furthermore, it increases the quality of life and reduces hospitalization. Traditional health monitoring systems are inconvenient and time-consuming. Additionally, they are insufficient to meet the rising healthcare needs and lack the capability to integrate disruptive and trending technologies such as AI, IoT, and big data. This comparative paper compares monitoring applications used in chronically ill and elderly and maternal stress real-time monitoring.
There is a dire need to develop efficient healthcare solutions that reduce pressure on healthcare providers, hospital systems and keep patients out of hospitals during routine care programs. Just like other IT fields, there has been incredible progress accounted in the medical field’s monitoring systems that have changed human life by protecting and maintaining multiple chronic diseases. The trends in healthcare monitoring applications enable healthcare providers to access medical information wirelessly through cellular and WI-FI networks. Additionally, cloud computing technology initiation has allowed the monitoring applications to be more scalable and efficient in processes such as accessing and storing medical information in reduced development cost.
Real-Time Cloud-based Monitoring and Tracking System for Cardiology Patients
In the medical field, using pervasive medical devices with their connectivity to advanced networks or the internet has brought new visual sense for human treatments, medical diagnosis and monitoring, real-time remote monitoring of patients’ health, and Wireless Body Network (WBN) (Shahzad, Lee, Lee, Kim, & Xiong, 2018). Furthermore, the pervasive medical sensors and devices are connected to specific parts of the patient’s body to assess the acquired medical data such as heart rates, blood sugar, blood pressure, and other medical signals. The observed data is transmitted to a medical advisor or assistance through wireless media such as a cellular network, where it is observed for further diagnosis.
The application employs automated medical analytic tools like electrocardiogram analyzers to analyze medical information in a real-time approach. The electrocardiogram is perceived as part of the telemonitoring application. The real-time monitoring application monitors remote and indoor patients’ status to overcome emergency cases and diagnose and fight against critical illnesses before they worsen. Furthermore, it is enhanced by cloud technology to manage and healthcare information easily. Hospitals use public cloud computing to manage administration, underpin a variety of healthcare services, and manage healthcare IT requirements.
Public cloud services enable the real-time monitoring and tracking system for cardiology patients to retrieve real-time information from the patients without delay. Furthermore, they allow the application to share and truly synchronize users and scalable workload cases securely and ensure information is always readily available when needed. Public cloud technology is efficient in managing services and monitoring information for real-time monitoring and tracking application (Kulesza, Dziak, & Jachimczyk, 2017). The application also employs Elastic Utility Architecture to link it to use systems for hybrid and private cloud settings to interpret medical information adequately.
Healthcare Monitoring Application for Chronically ILL Elderly and Patients
This application’s discovery was driven by the rising demand for a remote and real-time healthcare system that caters to elderly and chronically ill patients (Mardini, Iraqi2, & Agoulmine, 2019). The demand was derived by the need to enable the elderly and patients to be independent at their homes’ comfort without caregivers or family help. Furthermore, the application’s development was successful due to advancements in information technology that alert and monitor mechanisms on a real-time basis. The healthcare monitoring application for the elderly and chronically ill patients gathers health data, transfers, analyzes the data remotely and in real-time.
The application is used in the form of a wrist-worn unit or hand-held device. The devices gather information regarding medical conditions and environmental conditions, and the activities of patients. Furthermore, they use sensors for better health identification and monitoring capabilities. The application employs Myheart technology that uses intelligent systems to prevent and monitor cardiology diseases. Furthermore, the application transfers patients’ information through Multimedia Messaging Service (MMS) to the caregivers. The message entails real-time data regarding the patient’s map-location and GPS coordinates that enable the caregiver to locate the patients quickly (Ding, Soni, Bashar, & Noh, 2017).
The application uses an IoT-based system that leverages health information gathered from the devices. Furthermore, it deploys deep learning technology to detect and transfer the information in real-time. The applications remind patients about specific physical tasks and medication based on the data gathered from the environment and patients’ activities. Want is more, it uses RFID technology to locate the patients within their homes. The application’s RFID gateways and sensor networks execute web hosting and data processing in real-time. Furthermore, the RFID technology enables the application to gather information regarding motion, CO2, humidity, and temperature.
Smart Home-Based IoT for Secure and Real-Time Health Monitoring System
IoT is applicable across diverse domains, including healthcare. Furthermore, it shapes and revolutionizes modern healthcare with ambitious prospects social, economic, and technical aspects. IoT Smart Home-based system for real-time health monitoring uses a wide range of telemedicine architectures. IoT enables the application to collect and exchange health data for diagnosis and care delivery, medical data exchange, treatment and consultation, and health education. Furthermore, it facilitates multimedia-rich and deep communication within the application. The application uses a 3-tier pervasive technology system based on Wireless Body Area Network (WBAN) (Talal, Zaidan, & Albahri, 2019).
The first and second tier entails the client-side of the application, and the third tier consists of the server-side. Both the server and client-side collaborate to facilitate real-time and remote health monitoring through IoT. The application requires the interconnection of multiple devices for effective functioning. The devices include blood glucose measuring devices and ECG. The application enables the healthcare provider to gather psychological patients’ data to offer real-time telemedicine care. Additionally, psychological information should be collected in real-time and automatically transferred to health specialists through a cellular network to support and help patients utilize smart home technologies to control their health conditions.
The multimedia and IoT technologies used in smart home-based IoT healthcare monitoring and tracking application facilitate patients’ follow up activities such as recovery, and assessment. The application also uses sensors in the client side to communicate information regarding patients’ conditions and alert the nurses and doctors when abnormal conditions are detected. Additionally, it has gateway modifications, access management programs, and mobile-based communication. Furthermore, it deploys smart home technologies to improve life quality and safety of the patients.
IoT-based Real-time Monitoring Health System for Maternal Stress
There are widespread concerns regarding pregnancy-related anxiety and stress, which lead to multiple pregnancy complications that directly endanger the health of the fetus and mother. Maternal adaptations to reduce stress levels are essential for successful pregnancy through multiple disruptions and environmental stressors. To support the conventional health methods, experts have developed an automated and personalized IoT-based real-time monitoring health application that offers stress monitoring facilities for remote and hospital-based patients (Oti, Azimi, & Anzanpour, 2018).
The deployment of the application is driven by IoT technology that facilitates continuous analysis, collection, delivery of medical information. The technology offers embedded systems, actuators, and sensors that leverage shared data, computing and communication resources, and advanced internet services. The stress monitoring systems use the patient’s vital signs like heart rate variability to present the level of stress. Furthermore, they use patient’s physical and context information gathered through mobile sensors. The application uses role-based models and methods to detect the patients’ stress levels.
Real-time Human Tracking and Health Monitoring System Using Arduino
The real-time human tracking and health monitoring system using Arduino consists of body temperature and pulse rate sensor with microcontroller-based body temperature and heart rate measuring devices. What is more, the devices work hand in hand with LCD output. The system sets a threshold value for the temperature and heart rate. When the threshold range is exceeded, the device signals message (Lal, Uday, & Abhijith, 2019). The application programmer decides the range of the threshold. Additionally, the collected body temperature and heart rate information is transferred wirelessly to the healthcare providers.
The application system entails a transmitter section and a receiver section. The transmitter section initializes the system to enable it to monitor all the set health parameters. Additionally, the health information is transferred to the reception unit, where a health issue is detected, and a reply message is sent back to the patient. The receiver section gets initialized, and output information like threshold temperature and mobile numbers are given. Furthermore, the gathered information is sent to the registered phone number and stored on an SD card. The LCD technology displays the analyzed heart rate and body temperature information.
Literature Review
As telecommunication sectors are improving, telemedicine applications are developed using multiple technologies. Cornet and Holden 2018 states that healthcare remote and real-time monitoring requires new systems based on demands to offer healthcare services in healthcare centers. They state that telemedicine is the best option to offer medical monitoring capabilities for distance patients and medical advisors, especially in situations of arrhythmia or cardiology that always happen instantly. They propose the use of an Electrocardiogram (ECG) telemonitoring system that facilitates remote and real-time services. The ECG is a portable remote networked system used in emergency cardiology situations.
Cornet and Holden (2018) state that ECG-measured health status information is simultaneously observed and transmitted through satellite transmission, cellular network, and any other wireless network to the healthcare’s control center. The information is further assessed for storage and diagnosis purposes. Rel-time monitoring information is stored in the forms of store-and-forward and real-time. The researchers also discovered that real-time cloud-based monitoring and tracking applications could also use RFID technology called medical information tag (miTag) in cardiology disasters. Cornet and Holden state that RFID technology is cost-effective and provides inclusive cardiology information by monitoring and tracking patients’ conditions.
Vora & Tanwar (2017) researched a system that deploys a heart rate sensor and a webcam for detection purposes. The system transfers the gathered medical data to a home gateway for analysis through neural networks of Multi-layer Perceptron (MLP). The system uses gateways to alert caregivers in case heart rate fall. What is more, it uses a webcam to cover the patients’ surroundings. The system also deploys face detection to switch between tracking the patient and the webcams. Each webcam gathers information regarding speed of the patents’ physical activities. They then send the information to MLP for processing. After the information is processed, the application proposes heart rate fall prevention strategies.
Kraemer & Braten (2017) proposed an Enhanced Sustained Use Monitoring System (ESUM) developed as a smartphone application for remote and real-time monitoring of discharged heart patients. The ESUM monitoring system entails a wearable device secure at the patient’s chest to gather information regarding skin temperature, heart rate, posture, and activity level. Furthermore, the wearable device uses a battery charged through a regular phone charger. The collected data is transmitted to the patient’s smartphone via Bluetooth and the health center via a cellular network.
Thota & Sundarasekar (2018) recommended multiple hierarchical healthcare monitoring and a real-time application for the elderly. The system has numerous layers that control and report to higher layers. The first layer entails sensors categorized into Sensor Processing Units (SPU) responsible for gathering patient’s critical data, storing, processing, and transferring the data to a higher layer. The second layer consists of processing units that intercept the collected data to detect irregular health status. The application uses smartwatch technology to monitor and sense patients ‘health. Furthermore, it provides real-time ecological assessments displayed through the smartwatch.
Alaa & Zaidan (2017) proposed a secured and efficient architecture for authentication and authorization for IoT-based remote health monitoring applications. The proposed system architecture utilizes an e-health smart gateway to communicate with medical sensors. Furthermore, it enables the available IoT gateways in the application to major on simple tracking tasks rather than challenge the application’s authentication and authorization. They suggested sensors and a secure and novel terminal-getaway-group system to facilitate cooperation between the application’s network application layers. The gateway modifications in the application establish secure, seamless, and authenticated communication between data collecting devices and the internet.
Rahman et al. (2018) suggested implementing access management schemes in the IoT-based health tracking application to facilitate remote-tracking without user involvement. They elaborate how the integration authentication approach for identity management (IdM) from IoT to the internet. The IoT gateways offer a connection between the client and server but cannot access the health information. Furthermore, the scheme uses email services to alert the user about any home device access. The scheme ensures the privacy of queries and authentication of IoT services within the application. (Rahmani, 2018)
Kalid et al. (2018) discuss the use of mobile-based communication within the application’s IoT devices. Furthermore, they suggest the implementation of WI-FI networks in smart home devices. Moreover, they propose a hybrid application with a concept of smartphone application embedded in the healthcare monitoring and tracking application to increase IoT devices’ security features in smart home settings. The proposed mobile-based communication feature enables the healthcare facilities to auto manage the application to enhance its security and auto update it. Furthermore, it enables the healthcare providers to detect unusual activities and intruders inside the patients’ homes.
[17] proposed the use of a monitoring system with a sensor board attached to the patient’s pillow to determine pressure signals resulting from movement and heart rate. Furthermore, the monitoring system performs signal processing and transfers the data to a server through the internet. The system also facilitates communication with professionals when the application’s prediction model detects changes. The body sensors detect proteinuria and pressure levels on a real-time basis to determine the probability of hypertension disorder. They also proposed using a wrist-worn device to record continuous heart rate that enables the health professional to ask the patient regarding stress level severally on a daily basis.
Hye-Geum & Eun-Jin (2018) proposes an integration of online machine learning to stream data in healthcare applications. This suggestion was driven by the increasing demand for online machine learning algorithms. They describe how the use of naïve Bayes that classified the generated stress levels into low, medium, or high using Support Vector Machine (SVM). Saeid (2017) suggests using edge-based IoT architecture in healthcare applications that enable healthcare providers to compare patients’ stress levels every 15 minutes. The architecture also has a cloud server that updates all patients’ stress levels.
Discussion
Real-Time Healthcare Monitoring Applications Architecture
First Tier
All real-time healthcare applications’ architecture consists of three tiers. The first tier is sensor-based that outlines the sensor challenges and techniques deployed for continuous healthcare monitoring services. Furthermore, it entails Wireless Body Area Network (WBAN) with small that gather patients’ vital information. The sensor-based tier-one real-time medical applications have seven domains of ontology that explain the wearable platforms. The reliability domain follows that controls congestion in the application. Additionally, the other domain is energy efficiency that describes the robustness and efficiency of health data transmission.
Quality of Service (QoS) is the fourth domain that offers parameters of understanding the application performance. What is more, privacy and security is the fifth domain that focuses on securing the sensor nodes and wireless communication. The last domain is the assessment and evaluation that manage the time-allocation scheme in the WBAN to achieve the applications’ efficiency. What is more, this tier has a medium access control (MAC) protocol that prioritizes multiple biosignals minimizing packet delay time to ensure data effectiveness.
Second Tier
The second tier is gateway-based that facilitates the use of wireless devices such as mobile phones. The gateway contains a mobile user interface (MUI) that presents the design rules of the application. It also evaluates the distribution of wireless devices and medical alarms to prevent congestion and failure of the application’s network. This tier also integrates data sources and aggregation. Integration involves incorporating numerous data sources for the utilization of health information from mobile phones and sensors to support the connection between healthcare providers and patients. The gateway also contains disaster management tactics for recovering occasions of data loss in the application.
Aggregation uses cloud-assisted WBAN to enhance the application’s flexibility and scalability. The gateway has network management schemes that support multi-user platforms of an ad-hoc network. The ad hoc network enables the programmer to denote challenges and improve the application’s communication reliability. Lastly, the gateway entails the application’s decision support system provides detailed disease diagnosis and decision-making process. Real-time healthcare applications use knowledge-based DSSs to decrease and discover false alarms.
Third-tier
The third tier entails the application’s server responsible for remote and real-time healthcare monitoring. The server uses remote telemedicine devices that gather patient’s vital signs and analyses. The medical servers enable the medical professionals to identify the appropriate services to offer to remote patients. The servers carry out activities such as analysis processes such as using sensing techniques to enhance healthcare services efficiency. In the analysis process, the severs use activity mining techniques. Additionally, servers control environmental management activities such as data gathering processes or older people in homes and hospitals.
The servers assess and evaluate the effectiveness and features of the healthcare applications. Furthermore, they secure the signals gathered from sensor devices to ensure the integrity and confidentiality of medical information. The servers create a tele-expertise environment between healthcare professionals that enables them to share medical data and come up with an effective diagnosis and treatment method. The servers also enable PHRs and EHRs to access the applications with high performance. The servers facilitate the overall remote monitoring, remote healthcare provision, and patient prioritization within the application. Lastly, they control the application issues such as power consumption, data management, and sensor network optimization.
Attributes of an effective Application
An effective real-time healthcare monitoring and tracking application should have multiple attributes such as memory footprint, reliability, latency, and many more. Furthermore, it should have work stability over a long period to deliver services effectively. The memory print of an effective application should use software tools such as the MSP430 toolchain that shows the RAM and ROM status of the application. The application should be able to calculate packet overhead using a board of wireless packet sniffer interfaced in its system. the packet device captures signals of a patient’s body contact.
Additionally, the application should be able to calculate latency which is a measure of packet transmission time after or before the CPU intensive time duration. The application should effectively determine the energy consumption of all hardware within the system. Furthermore, all the networks, software, and hardware should be reliable. The devices should also continuously function as expected. The applications should have an effective transmission speed to enable the healthcare providers and the smart devices to interact with minimum delay without obstructing system throughput. Lastly, the applications should have strong and reliable security features to safeguard patients’ health data from unauthorized access and hacking.
Issues in Real-time Healthcare Applications
In connection to the numerous e-health aspects, it is also crucial to determine the challenges encountered during the healthcare systems’ development. These issues encompass cloud and fog computing, cost, testing, scalability, interoperability, acceptability, unobtrusiveness, availability, reliability, and portability, among other challenges. Remote healthcare monitoring systems depend on employing cutting-edge technologies in their development to aid critical older population and chronically ill patients.
Acceptance among the Older population and Critically Sick Patients
It is critical to comprehend the notion of the served population regarding smart devices’ utilization such as tablets, smartwatches, and smartphones and the drawbacks encountered by them. The majority of patients find it more straightforward to employ smart devices for rhythm monitoring than conventional monitoring gadgets. To enhance the useability and acceptability of smart gadgets, the design stage has to incorporate the challenges that impact the lack of confidence, like lack of guidance and instructions and lack of knowledge, and medical challenges such as the high cost of modern gadgets and concentration or motor skills. Exposure therapy, use of bezel like in Samsung wear, utilization of large buttons, and use of sign and friendly designs instead of small text can aid minimize the gap between modern technology and older adults and lessen the lack of confidence.
Power Consumption
Batteries are deemed the key drawback for sensors. The elderly could find it challenging to charge the batteries, particularly for people ailing from dementia. Minimizing sensor power consumption assures longer monitoring and is more sustainable in the long-term with minimal maintenance. Enhanced employment of the network’s hardware devices, as well as the health monitoring network, can be attained using suitable programming techniques. Wireless biomedical sensor networks’ biological effect is among the major issues in healthcare systems. The radio frequency’s radiation for an extended period has negative effects on an individual’s health. Since sensors are crucial health monitoring systems’ components and the intercomponent communication is not replaceable, the radio frequency communication’s biological impact should be crucially examined.
Reliable Communication and Availability
The monitoring subsystem has the function of generating alerts, taking proper actions, and monitoring the patients. These capabilities necessitate the patient’s status real-time monitoring. As a result, delay and congestion should be managed in the network, particularly in crucial situations. Additionally, the healthcare architecture must ensure that the network is robust and highly available. Since cost and redundancy are positively related, a balance between the two is required.
Conclusion
Deployment of real-time and remote health information applications requires utilization of the 6 Vs. of big data. The volume describes the amount of healthcare information generated and increases rapidly every day. In most cases, the applications use a 3-tier architecture to manage all gathered patients’ information. Velocity relates to the massive amount of data generated instantly whenever needed for re-time processing. Furthermore, it also indicates the frequency of healthcare data analyzed, processed, and generated. Variety refers to health information gathered from diverse resources that facilitates large-scale needs. Furthermore, data from diverse resources assist in improving healthcare outcomes by presenting customized care and accurate diagnosis.
Additionally, variation minimizes healthcare costs through timely disease detection. It also helps healthcare providers predict and manage high risks to quickly and effectively detect healthcare fraud and health risks. The real-time healthcare applications leverage the value of the gathered data by acquiring all possible value. The value includes knowledge regarding recognition of emergency situations and patient evaluation. What is more, the variability is evident in how the applications manage changes that happen during healthcare data processing. In real-time healthcare tracking, multiple data types are used, such as text, signal, and images. Additionally, the healthcare data in the application undergoes the processes of alignment and extraction.
The veracity entails the aspects of data trustworthiness and consistency, meaning the applications assume the current granularity and performance of the involved architectures, algorithm methodologies, tools, and platforms for big data and IoT technologies requirements. Various sensors are involved in healthcare applications to ensure data veracity. Healthcare professionals should ensure all deployed applications comply with all regulations to safeguard patients’ private information. This comparative study shows that all real-time monitoring healthcare applications should deploy IoT, Big data, and Machine learning techniques to deliver effective results.