- Studying 56 papers in the period of (2015–2019) that cover several features related to RPMS, including IoT, WBAN, cloud computing, fog computing, and CDSS.
- Providing a comprehensive survey that summarizes the state of the art of RPM systems, tools, technologies, recent applications, and techniques.
- Highlighting all the steps in building efficient and effective RPMs, in addition to the challenges and future directions at each stage.
- Discussing the importance of artificial intelligence (AI) in building medically intuitive monitoring systems.
- Providing a case study of remote patient monitoring for chronic diseases patients that tries to cover several limitations of the state-of-the-art architectures.
2. Materials and Methods
2.1. Selection Criteria
2.2. Results Statistical Analysis
3. Main Components of the RPM System
- Provide patient assurance: RPMs could provide (24/7) care at home through wearable sensors, which are used to frequently measure patient vital signs, provide a real-time recommendation based on patient status.
- Increase patient awareness and responsibility: the continuous collecting of patient data increase patient awareness about his/her health status.
- Provision of low-cost solutions: depending on RPMs decreases the cost of hospitalization and admissions, consequently, saving on the total cost of healthcare services. Figure 3 shows the general form of the patient monitoring system.
3.1. Data Acquisition
- Implanted sensors: sensors that are implanted inside the patient’s body (under the patient’s skin).
- External sensors: sensors that attached directly to the patient’s skin or separated with about (2–5) CM.
WBAN Challenges in RMS
3.2. Storage Server
3.2.1. Cloud Computing
3.2.2. Fog Computing in RPMs
3.3. Back-End System
4. Disease-Specific Remote Patient Monitoring Systems
4.1. Heart Disease Monitoring Systems
4.2. Fall Detection Monitoring Systems
4.3. Mental Health Systems
4.4. Diabetes Monitoring System
4.5. Vital Sign Monitoring and Health Assessment Systems
4.6. Other Diseases Monitoring Systems
5. The Role of Artificial Intelligence in RPMs
5.1. Rule-Based Systems (Expert Systems)
5.2. Machine Learning Techniques
- Supervised and non-supervised algorithms: Several types of supervised machine learning algorithms are used in RPMs, analyzing medical data in order to predict patient future events. For example, El-Rashidy et al.  used supervised ML algorithms (i.e., rule-based classifier, non-linear classifier, instance-based classifier, tree-based classifier, etc.) to analyze patient’s medical records and predict mortality among them. Each classifier used a different learning algorithm to build a model that best fit between input and output with a good generalization capability. Shamer et al.  developed a quality assessment model that was used to predict readmission, several ML algorithms were integrated to build an ensemble model to make predictions. The same model was used for predicting complications in ICU units , cardiovascular [129,130] Diabetes [131,132,133,134], sepsis [135,136], and COVID-19 [136,137,138]. ML is also used to provide timely medical services to patients. One such example is called Home Smart Health (HSH) . HSH used a body sensor network and a personal sensor network for building a smart environment that has the capability to meet patient’s needs. ML (supervised and non-supervised) is used to analyze patients’ data (sensor data) to understand patient behavior and provide specific services for each patient.
- Reinforcement learning (RL): ML models that learn by the trial-and-error concept, the learning process is repeated until the optimal solution is reached. RL is used in various monitoring systems. For example, Nuayto et al.  built RPMs for continuous monitoring of bio signs through a heterogeneous sensor transceiver. The proposed architecture used reinforcement learning (constrained Markov decision process (CMDP)) to minimize cost while maintaining the optimal quality of service (QoS). Wipawee et al.  used Q-learning (reinforcement learning) to provide a monitoring system. They used a distributed routing mechanism to route information to the nearest sink. Others use reinforcement learning to find the optimal treatment for a patient with anemia.
- Deep learning (DL): This is a new area of ML that simulates the human thinking process. DL provides healthcare applications the ability to analyze huge data at exceptional speed with promising accuracy. For example, El-Sappagh et al.  used the DL model to predict patients with Alzheimer’s based on patient vital signs and X-ray images. Other common applications use DL models to specify the most critical features in patients’ imaging data, it is considered a promising solution in oncology image analysis. DL also has an increasing impact on natural language processing (NLP) . In RPMs, NLP contributes to understanding the clinical notes on patients to provide efficient monitoring, transcribe interactions from patients, and provide conversational AI supportive tools such as chatbots . For more details, [21,141,144] provide comprehensive surveys about using ML in RPMs.
5.3. Human-Computer Interaction
5.4. Physical and Processing Robots
6. Case Study: Chronic Diseases Monitoring System
- Lightweight biosensors are attached to a patient body. They continuously monitors patient vital signs like glucose level, vision level, fatigue level (EEG), activity level, blood pressure, body temperature, etc. Then all vital signs are gathered and sent to the central control unit. Note that ZigBee is used to deliver vital signs from sensors to central devices. If mobile applications notice that there is no patient record, the system will send a message to the patient via text or call to check the sensor or batteries. In case of no response after a short time system will automatically call the caregiver to check the patient’s state.
- Social media patient’s activities (Facebook comments and tweets) are also tracked continuously and analyzed using components for handling unstructured data. All gathered raw data are then transported to the central control unit (CCU). In some cases, a smartphone may be used as a central control unit.
- Our proposed framework provides two monitoring modes, the online and offline monitoring systems. The offline mode runs via the first layer CDSS that is installed on a personal server (discussed in the next step), and online via a cloud server (discussed in step 4), distributed her, and second layer CDSS. In the personal server, each patient transmits his/her vital signs, then all patient’s data are transmitted to the cloud hospital server.
- In case the internet connection is interrupted or unplugged, the system will not work properly, and the patient will not be able to connect with the system. To overcome this challenge, a light CDSS was added to the patient side to monitor the patient until the internet problem was fixed. The CDSS’s first layer helps patients with advice and recommendations based on the patient profile (i.e., EHR) and a small knowledge base. The knowledge base will continually update by discovering and extracting knowledge from the EHR. CDSS in the first layer resolves the human-computer interaction issues and provides a simple and user-friendly GUI that does not require experience in dealing with computers or smart apps.
- Periodically, patient data is transmitted to a stand-alone device where a wireless area network is created between it and another system component (Caregiver provider, family, emergency system), which permits them to access and check the patient status and retrieve patient information during monitoring system. In case the system detects abnormal signs, it will fire the alarm and send an alarm message to the network. Note that Wi-Fi IEEE 802 is used to transmit data between CCU, cloud server, and the CDSS second layer.
7. Study Results
Challenges and Future Directions
- Not all smart devices support the automatic transmission of patient data to the cloud or the fog nodes without patient intervention. Therefore, a new generation of mobiles should work on providing the automatic and accurate transfer of data . For example, in 2016 Android worked on improving the sampling rate constraints and permitting third-party applications to sample from various sensors.
- The accuracy of sensing devices (i.e., sensors) has still not reached a stable state; therefore, various challenges include working on enhancing signal processing and transmission. For example, Kim et al.  introduced a group of analog-front-end solutions that address the tradeoff between the quality of transmission and power consumption.
- The RPM systems are developed to solve the problem of patient monitoring regardless of time and place. Therefore, the design of WSN should maintain the mobility, transmission rate, data rate, and network coverage issues . For example, building monitoring systems that utilize both fog computing and cloud computing may provide various capabilities such as mobility, low latency, and low bandwidth consumption.
- Managing and integrating the massive data extracted during patient monitoring are considered a daunting task. To take full advantage of the extracted data, various data mining and knowledge extraction tools should be developed to have deep insights into these data to improve knowledge outcomes and decrease costs .
- The internet is considered the primary medium for data transmission in any RPMs. This raises the need for ironclad privacy and security protocols to protect data from different attacks such as data eavesdropping modification and impersonations. The problem worsens due to the fact that most wireless body area network devices used in patient monitoring are limited in memory, processing, and energy capabilities . Therefore, it is considered impossible to provide full monitoring systems based on them. Accordingly, privacy and security issues need additional work, to provide an acceptable solution in the different layers of monitoring [170,171]. A comprehensive survey of security and privacy in patient monitoring can be found in .
- Encryption could be used to prevent data eavesdropping. Therefore, working on symmetric and asymmetric key encryption algorithms could help to provide a high level of security for patient’s data .
- Managing large networks is also a complex challenge. Therefore, working on developing role-based access control systems may help in reducing the complexity in administration, especially with large healthcare systems.
- Monitoring systems could be used for a small number of patients in clinics or may be scaled up to be used by a large number of users in hospitals. This results in the rapid growth of demands for physicians as well as healthcare organizations. Accordingly, RPMs should be scalable in terms of applications, networks, and services .
- RPM systems are very time-sensitive and require the guarantee of several QoS criteria such as maintainability, reliability, and availability. This is due to the fact that such systems put patient’s lives in danger in critical health problems .
- The power consumption of WBAN sensors is a big challenge for RPMs. Usually, the capacity of batteries is consumed in sensing, processing, and transmitting of data, so that it requires frequent recharging. It may be considered the weakest point in RPMs as frequent charging for batteries is considered a big burden for patients. Therefore, the optimization of power consumption is considered one of the main points in various studies. Some studies working on improving the current protocols such as Zigbee and Bluetooth are [11,132,175]. Others work on extending the lifetime of the sensor battery by utilizing medium access control (MAC) protocols with low power consumption .
- Providing continuous monitoring in the healthcare sector requires the use of various sensors that are mostly manufactured by different manufacturers. The lack of standardization techniques hinders the ability of devices to communicate and transmit data among them effectively. Therefore, working on standards and data integration protocols is considered a pressing need to provide data and device interoperability. From the application side, some monitoring applications require approval for use from some bodies such as the FDA. To overcome this delay, participants must come up with medical guidelines that work on speeding up the deployment of medical applications.
- The development of complete RPMs that allow patients to integrate with various hardware and software service providers and different sources of data (heterogeneous sources with different standards and formats) is a challenge that needs to be addressed in future studies.
- The patient’s EHR system may include various components such as laboratory systems, hospital information systems, etc. Each component may have different standards (i.e., HL7, OpenEHR, and ISO/IEEE) and different terminologies (i.e., LONIC, SNOMED CT, and CPT4). Therefore, working on a unified standard is essential to maintaining syntax and semantic interoperability .
- CDSS should work based on a patient’s EHR data, in addition to vital signs data sensed from wearable sensors. Therefore, CDSSs should provide specific services based on each patient’s data. On the other hand, CDSSs interfaces should maintain a brain-computer interface (BCI) and human-computer interaction (HCI) in order to support the dynamic creation of an application interface according to patient’s moods .
- Based on the surveyed literature presented in this paper, we could not categorize whether the existing RPM solutions are easily compatible with security and privacy legislation. Nonetheless, as healthcare solutions undergo a digital transformation, the paradigm needs to be implemented with the compliance of different legislative frameworks such as the general data protection regulation (GDPR) and network and information security (NIS) directive (NISD) requirements . While the GDPR is a privacy directive that instructs how organizations should handle personal data, the NISD emphasizes strengthening organizations’ security capability from the service infrastructure viewpoints. The work in  identified a set of different measures that can be integrated with m-health systems to adopt GDPR-compliant security and privacy schemes. Recently, the work in  provides a case study of the “WELCOME” research project, an integrated system for chronic patients’ monitoring, diagnosis, detection, and treatment. In the study, the authors propose a framework for the security and privacy of m-health applications adhering to the GDPR guidelines. Policy enforcement is necessary to monitor and guarantee that the digital information systems strictly follow specific policies in dealing with medical information.
- The advanced message queuing protocol (AMQP) and message queuing telemetry transport (MQTT) are the two most common data transfer protocols used to exchange data between IoT systems and edge or cloud servers. Although both of these schemes are non-healthcare-specific protocols, they can be integrated with HL7, which is here and now the most widely adopted data interaction standard in medical applications . In MQTT, a broker receives messages from the publishers then routes the messages to the respective subscribers. While AMQP provides similar functionality as MQTT, it also facilitates queues in the broker to store the message when the consumer does not access the messages. Large organizations that include many IoT devices require a higher level of data integrity. Therein, both AMQP and MQTT can simultaneously be deployed for different clusters and regions [181,182]. As such, the coexistence of AMQP and MQTT protocols is conceivable in HL7-facilitated organizations. However, to determine the suitability for the HL7 framework, more research on lightweight publish-subscribe network protocols is required from practical and implementation contexts.
Conflicts of Interest
|AHP||Analytical hierarchy process|
|CDSS||Clinical decision support system|
|CHMS||Cloud health monitoring system|
|COPD||Chronic obstructive pulmonary diseases|
|EHR||Electronic health record|
|HL7||Health Level Seven|
|IoT||Internet of Things|
|MAC||Medium Access Control|
|PMS||Patient monitoring systems|
|PRISMA||Preferred Reporting Items for Systematic Reviews and Meta-Analyses|
|QoS||Quality of service|
|SCI||Spinal cord injury|
|SNOMED-CD||Systematized nomenclature of medicine-clinical terms|
|TDMA||Flexible time division multiple access|
|UMLS||Unified Medical Language System|
|WBAN||Wireless body area network|
|XML||Extensible Markup Language|
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|Key Words||Databases||Total Publication Identified|
|1||Remote patient monitoring||326||619||506||699||800||2950|
|2||Remote patient monitoring AND clinical decision support system||4||160||118||267||29||578|
|3||Remote patient monitoring AND ontology||16||18||23||237||44||338|
|4||Remote Patient monitoring AND data mining||24||46||42||84||23||219|
|5||Remote patient monitoring AND wireless body Area network||16||15||30||102||10||173|
|6||Remote patient monitoring AND ontology AND (cloud computing OR Fog computing)||8||7||85||42||2||144|
|7||Remote patient monitoring AND ontology AND cloud computing and wireless body area network AND clinical decision support system||1||0||5||2||3||11|
|Very Low||2.4 GHz||70–100 m||Zigbee|
|Medium||1 MHZ||10 M||Bluetooth|
|High||2.4 GHZ||100 M||Wi-Fi|
|#||Diseases||Collected Data||Sensor||Transmission Protocol|
|||Heart diseases||ECG||ECG monitor node||Wi-Fi (HTTP, MQTT)|
|||Heart diseases||ECG||ECG fabric sensor embedded on the patient’s chair||Bluetooth|
|||Pain assessment||Facial expression (sEMG)||Wearable sensor with a bio-sensing facial mask||Wi-Fi|
|||Heart diseases||Spo2, blood pressure, ECG||Wi-Fi|
|||Heart diseases||ECG||Wearable smart clothing||Bluetooth|
|||Dementia||Changes in behaviors and Functional health||Electrodermal Activity (EDA), Photoplenthys (PPG), Accelerometer (ACC)||Wi-Fi|
|||Chronic diseases||Monitor medication adherence||Smart home sensors||Wi-Fi|
|||Chronic diseases||Monitor medication adherence||Wristband wearable sensor||Bluetooth|
|||Fall detection||Monitor mentions and predict falls||Accelerometer, Cardiotachometer||ZigBee|
|||Heart diseases||Spo2, HR||Wireless pulse oximeter||Wi-Fi|
|||Hypertension||Blood pressure||Electronic blood pressure measurement||Bluetooth|
|Factor||Cloud Computing||Fog Computing|
|Location identification||Partially supported||Fully supported|
|Number of servers||Few||Large|
|Service location||With the Internet||At the edge of the local network|
|99.30%||Ontology, interoperability, CDSS||115,477 records collected from of 36,162 type 2 diabetic patients||Chronic diseases|||
|-||Ontology, sensors||Ontology tested on “SPARQL” Query||Cardiovascular|||
|87%||Fuzzy logic, ontology reasoning||The system evaluated in Taichung Hospital in central Taiwan||Diabetes|||
|97.67%||Fuzzy ontology CBR||60 real cases from Mansoura university hospitals||Diabetes|||
|Machine learning||90 patients with gestational diabetes||Diabetes|||
|92%||Case base finding||323 real cases||COPD diseases|||
|90–95%||Machine learning (24 classifier combination)||85 patients||Real time monitoring|||
|89%||Machine learning, ontology||Real-time patient data form Biosensors||Mental disorders|||
|-||Ontology-driven||English lung cancer dataset (LUCADA), approximate (115,000) patient recode||Lung cancer|||
|Factor||HL7 v3||HL7 FIHR|
|Year of initiation||1997||2011|
|Architecture||Massages||RESTful web services|
|Tooling required||Yes, just compiler||No|
|Adoption degree||Low||Expected to be high|
|Character support?||Yes (conceptually)||Yes (UTF8)|
|Massage format support||Realm||Global standard|
|||Ubiquitous monitoring system||🗸||🗸||🗸||🗸||🗴||🗸||🗸|
|||Vital signs gathering and processing||🗸||🗴||🗴||🗸||🗴||🗴||🗴|
|||Tracking daily activities||🗴||🗴||🗸||🗸||🗴||🗴||🗴|
|||EXP carried on healthy volunteers||🗴||🗸||🗸||🗸||🗴||🗴||🗴|
|||Context aware monitoring||🗴||🗸||🗸||🗸||🗴||🗴||🗴|
|||Diabetes and Diet monitoring||🗴||🗸||🗴||🗴||🗴||🗴||🗸|
|||Monitor patients with depression||🗸||🗴||🗸||🗸||🗴||🗸||🗸|
|2014||Health care system designed to help people with depression to return to their normal life, the system consists of three main component, (1) personal server to monitor patient behavior such as sleep activity, (2) interactive agent that interact and collect information from the user through questionnaire (3) DSS that analyze patient collected|
|2015||RPM system based on cloud computing, system propose proactive monitoring based on data mining functions, system combine CDSS that designed to respectively train and test the new data and adapt the system to predict vascular for whole the next year.||67%|
|2014||RPM system for chronic diseases, system monitor vital signs (Body temperature, blood pressure, and heart rate) then send it to the cloud, the system permits patients and physicians to watch health data. In addition to that, they also provide visualization watch that classifies each vital sign according to special criteria.||95%|
|2015||RPM for monitoring cardiovascular diseases that use wearable sensors to collect vital signs (Blood pressure, galvanic skin response (GSR) that indicate stress level, Electrocardiogram (ECG)), the system proposes a contribution to optimizing system effectiveness by analyzing data in the local device (smartphone), it was done using a machine learning algorithm (SVM) that classify patient data and extract the clinical features to determine patient condition “continued risk” or “no longer risk”.||90.5%|
|2019||A monitoring system for Cognitive heart failure (CHF) patients, it consists of three tiers (first layer: biosensors for monitoring patient data. Second layer: a web server that store and maintain data integrity layer between different healthcare providers, this layer also analyze data and sends an alert message via text message or emails. Third layer: back-end server backup and recovery layer by making an offline backup)||----|
|[email protected] project|
|2016||A remote monitoring system for cardiovascular diseases, the system has client/server architecture. Client-side: located at the patient side, consists of a set of biomedical sensors that measure patients of vital signs (ECG, SPO2, Chest impedance, respiration, blood pressure), then the measured sensors send through the gateway to the server-side. ADSL or mobile broadband (UTMS/GSM) used to transmit data. Server Side: installed at health service facilities, process and analyze data from gateway using the expert system, and make it available for consultation, and finally patient record in the patient information system (HIS). The system also provides an alarm system that sent by a short message to the physician, patient, and relatives.|
|The system is used to track patient health status via two main sensors (temperature sensor and blood pressure sensor). Sensors are connected to a microcontroller that tracks patient status.|
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