https://doi.org/10.31449/inf.v46i2.3912 Informatica 46 (2022) 131–149 131 IoT-Enabled Remote Monitoring Techniques for Healthcare Applications – An Overview Shajulin Benedict E-mail: shajulin@iiitkottayam.ac.in or shajulinbenedict@mytum.de, www.sbenedictglobal.com Indian Institute of Information Technology Kottayam, Kerala, India Guest Professor, Technical University Munich, Garching, Germany Overview paper Keywords: healthcare, IoT, remote eHealth, social health Received: January 15, 2022 IoT-enabled remote healthcare monitoring applications have surged against countless other technologies to assist patients. Recently, the healthcare sector has sought a devastating escalation in augmenting ap- propriate monitoring technologies for effective remote monitoring and improved diagnostics during the pandemic era – i.e., the existing methods need to be revamped with sophisticated technologies. In this pa- per, remote monitoring techniques proposed for healthcare applications and their challenges are surveyed. In addition, a healthcare architecture for effective remote monitoring is explored. It was observed that most of the solutions focused on the application of edge analytics and deep learning mechanisms. The review aimed at guiding healthcare practitioners and developers to understand the pitfalls of existing approaches and to innovate solutions in newer dimensions. Povzetek: Podan je pregled IoT sistemov za pomoˇ c v zdravstvu. 1 Introduction As the COVID-19 pandemic goes ahead offering limited access to hospitals, there is a near certainty among pa- tients that they would be proactively prevented from dis- eases. The pandemic created direct and indirect impacts on chronic patients who visit hospitals. However, it ignited in- novations in the existing aid approaches – i.e., the growth in the healthcare industry has recently magnified. Several healthcare applications evolved to address the needs of the patients by evaluating the sensor values mounted on the body, in the home, or in environments. For instance, mobile applications that advise proper diet or telemedicine [76, 10], IoT-enabled sensor applications that monitor the health status of patients or remind appropriate medicines [23, 48, 70], AI-assisted applications that check the sleep issues of patients, online counseling, and so forth have marked upgraded functionalities in the recent past. In spite of inviting improvements and enabling strong technical requirements, the ICT solutions of the healthcare sector have not become mature to experiment with them on patients/individuals. A few notable challenges that exist in the domain include: 1. Patient Data security: Data theft, hacking, and the other security breaches in the patient data [5, 41] ham- per the realization of remote patient monitoring. 2. Availability: Most remote patient monitoring IoT-enabled applications utilize cloud databases or service-oriented infrastructures. Availability of these compute resources is challenged due to the emergence of modern execution models such as serverless com- pute environments. 3. Device inter-operability: There are several pro- prietary sensor devices involved in accomplishing healthcare applications. Managing the data transfers and connecting them using appropriate communica- tion protocols are challenging aspects for researchers and industry practitioners. 4. Cost/Performance efficiency: The cost and perfor- mance of applications grow into a tradeoff that needs to be elegantly handled in healthcare applications. For instance, scaling applications, enhancing privacy [27], or improving the reliability of applications could di- rectly or indirectly influence the operational costs of applications. A few researchers have implemented healthcare appli- cations using IoT, blockchain, and hierarchical-computing technologies. However, there exist several limitations and research gaps to be fulfilled for improving the diagnostics for real-world scenarios. The major contributions of this article are listed as fol- lows: 1. To illustrate a remote IoT-enabled health monitoring architecture that details the inner functional details; 2. To critically review on the existing remote monitoring healthcare techniques; and, 132 Informatica 46 (2022) 131–149 S. Benedict 3. To study the impact of datasets, devices, and IoT- enabled applications. The rest of the article is organized as follows: in Sec- tion 2, survey works related to the healthcare domain are discussed; in Section 3, a generic IoT-enabled healthcare application architecture is revealed; in Section 4, a few available remote monitoring techniques and their chal- lenges are presented; Section 5 reveals a few applications that are widely utilized in the market; and, finally, research directions and conclusions are expressed in Section 6, the last part of the article. 2 Related work Remote monitoring of the health status of patients has heightened the utility rate of healthcare applications since the inception of technologies such as IoT, serverless, edge, blockchain, and so forth. For instance, an increase in the number of filing patents and publications manifests the growth pace of healthcare applications [22, 42, 10]. The techniques involved in the remote monitoring of patients or individuals have expanded in various imple- mentations – for example, telemedicine, clinical trials, on- line counseling, psychological monitoring [43], and so forth, have diverged the markets using wearables, mobile phones, or implantable sensor units. Particularly, the fo- cus on the remote monitoring of the health status of pa- tients/individuals involved elderly [61, 75], chronic pa- tients, or preventive care individuals such as working pro- fessionals. Unique methods need to be adopted for the ef- ficient handling of healthcare applications. In the past, a few survey works were carried out by re- searchers to study healthcare applications. For instance, authors of [35] have surveyed the healthcare applications such as emergency monitoring, gesture determination us- ing mobile phones, knowledge-based decision support sys- tems, and so forth. Similarly, researchers of [9] have ex- plored the available communication protocols and tech- nologies for healthcare applications. However, these sur- vey works did not focus on remote monitoring health ap- plications. The need for delving into the remote monitoring tech- niques that have been practiced in recent years is multi- faceted: 1. To suggest the directions of research and the possible improvements in the existing monitoring techniques; 2. To promote newer insights while designing re- mote monitoring healthcare architectures incorporat- ing technologies such as serverless or AI methods; 3. To provide the amount of works/developments or competitors before investigating time for their inno- vations; and, so forth. This work focuses on investigating the remote IoT- enabled health-monitoring techniques and available solu- tions. 3 IoT-enabled healthcare architecture This section explains the generic remote health monitoring healthcare architecture and the significance of the compo- nents involved in it. Figure 1 illustrates the architecture and the functions of components. In addition, the section highlights a few notable existing healthcare monitoring ar- chitectures/frameworks. 3.1 Major components The major components involved in the architecture and their functionalities are listed in the following paragraphs. 3.1.1 User-interface Users, mostly patients and their well-wishers, are prompted with ease-to-use interfaces. The major features that an in- terface includes are: – Seamless Responsive Designs: The users or patients prefer to utilize multi-size screens of varying gad- gets such as mobile devices, laptops, or servers. The GUI design of remote monitoring systems, in general, includes a unique design with minimal variations to have a flexible layout of visibility features at the end devices. In doing so, the contents and visual representation of patient information are scalable with respect to the contents and screen size of the gadgets involved in the application. – Automated Bots: The web designs of the remote moni- toring healthcare applications involve automated soft- ware robots named bots. Bots are, in general, a piece of software instance that performs automated execu- tion of tasks [63]. In healthcare applications such as smart e-consulting or e-counseling, AI-assisted soft- ware robots are implemented to simplify the processes involved in performing tasks. These bots quicken the processes such that registration of patients to appro- priate hospitals and suitable available doctors happen in a short span of time. In the past, authors of [67] have studied the impor- tance of chatbots in assisting patients. These authors have studied the input data formats such as voice, text, or video of chatbots while interacting with patients; also, they have identified the requirements of natural language processing, reasoning, and so forth for ele- gantly automating the assisting processes. IoT-Enabled Remote Monitoring Techniques for. . . Informatica 46 (2022) 131–149 133 Figure 1: Generic IoT-enabled Healthcare Monitoring Architecture. – Multi-factor Authentication: The privacy of medical data or personal records is crucial in healthcare ap- plications [27, 65]. This feature prevents users’ data from being protected from hackers or malicious sus- pect devices. It is very unlikely that multiple pieces of programs that are responsible for independent authen- tication mechanisms such as username/password, iris recognition, biometric sensor units, and so forth, will be trapped by the hackers. In a few IoT-enabled remote monitoring healthcare applications, a single sign on (SSO) feature is im- plemented to improve the user-authentication experi- ences by seamlessly connecting with multiple cloud- enabled healthcare services. – Virtual Reality: The user interface of healthcare appli- cations needs support for virtual reality. In short, vir- tual reality incorporates human senses such as touch, sight, hearing, taste, and smell into programs and supportive hardware devices to provide a virtual en- vironment. Applications such as remote monitoring and counseling require a realistic environment of pa- tient diagnostics involving doctors from varying loca- tions. The user interface of such applications, there- fore, shall include these features for robustness and liveliness. 3.1.2 End devices Increasing the number of end devices and sensor units for continuous medical diagnostics or disease detection has prompted for delivery of superlative care by doctors to pa- tients in recent years. In fact, the detection of diseases evolves based on the screening processes of healthy or non-healthy individuals. It requires skillful listening to abnormalities if any. The major purpose of detecting diseases is to identify the risk indicators concerning diseases or probabilistic diseases. By doing so, individuals could reduce the long-term risk factor that might arise in the future. Whereas, diagnostics is the confirmation of the availability/non-availability of a partic- ular disease. IoT-enabled systems require real-time sensed data from different patients/individuals to detect diseases. By this, the spread of the diseases is prevented. More importantly, in order to perform diagnostics of diseases, IoT devices re- quire more prominent authenticated accurate data for con- firming the diseases that are often represented in a lay- ered structure[69]. In the past, researchers have utilized biomarkers to dictate the confirmation of diseases. Recently, researchers have coined the term Internet of Medical Things (IoMT) to establish connected IoT medical devices by extending the internet to tiny sensing objects. The end devices that are utilized in the healthcare appli- cation domain for remote monitoring the health status of individuals can be classified into 6 categories as discussed below: 1. In-body devices: Implanted sensors or medical things constantly monitor and add value to remote monitor- ing health applications. Many actuating things are often implanted in human bodies to stimulate near- failure organs. For instance, devices such as neuro- stimulators, cardiac defibrillators, insulin pumps, cochlear stimulators, and so forth are implanted to the patients for stimulating chemical actions for med- ical benefits. In recent years, the utilization of insulin pumps has tremendously increased as diabetic patients are prone to reach an uncontrolled augmentation of in- sulin. 2. On-body devices: The majority of the devices under the category of IoMT are wearable devices. These devices are attached to a human body in the form of wristwatches, dresses, rings, or adorable devices to frame wireless body area network (WBAN) [26]. These devices follow communication protocols such as IEEE802.15.4, which has a short-range commu- nication medium, to emit less energy. Most com- monly available on-body devices include accelerom- eters which are designed to measure acceleration 134 Informatica 46 (2022) 131–149 S. Benedict forces; gyroscopes which measure the angular rate or orientation angles; GPS sensors that sense the lati- tude/longitude of locations; heart-rate sensors which measure the heart rate of humans; and pedometers that count the steps taken by patients or athletes while run- ning or walking. 3. Portable devices: Medical things that are mobile in nature are termed portable devices. These devices are often directly connected with cloud server instances or fog nodes. Devices that constantly monitor the blood pressure or insulin level of patients are examples of portable sensor devices. 4. Static devices: Devices such as temperature sensors or air pollution sensor nodes [64] that are attached to environments such as smart homes/cities are denoted as static nodes. For instance, sending alarms or short messages to doctors/relatives of patients for detecting pneumothorax in X-rays is fixed to x-ray devices. The connectivity of these devices is much more reliable when compared to portable devices. Besides, the in- herent accuracy of these devices is stable in healthcare applications. 5. Ambulatory devices: IoT-enabled devices fitted to mo- bile vehicles such as ambulances are peculiar as they have to consider the real-time delivery of data to the hospital clouds servers. In spite of being error-prone due to the frequent hops in communication channels, the devices should deliberately handle switching-on stations without errors. Reliable measures need to be practiced so that patients could be saved due to the right medical prescriptions based on the sensor data. 6. Hospital devices: IoT has been utilized in hospitals not only to detect and monitor patients’ temperature, blood pressure, and so forth, but also to locate medi- cal kits. For instance, a 1000 bed hospital usually has over 200 wheelchairs. It is also mandatory to locate patients considering the safety measures of patients – i.e., an asthma patient needs pollution-free environ- ment; or, patients wanting for medical equipment such as defibrillators, nebulizers, oxygen pumps, and so forth, have to be guided to the nearest available wards in the hospital. Clearly, IoMT is beneficial to quickly identify the location of medical devices and serve pa- tients/hospitals. Figure 2 pictorially represents the de- vices utilized in the remote healthcare applications. The most commonly available IoT-enabled sensor de- vices for measuring health-related parameters include i) glucose monitors, ii) temperature sensors, iii) heart-rate monitors, iv) oxygen pulse monitors, v) electromyography sensors (ECG) for heart-care checks, vi) wheeze anomaly detection sensors, vii) movement disorder checks, viii) stress indicator, ix) posture indicator, x) lung status indi- cator, and so forth. 3.1.3 Edge/fog layers Edge and Fog nodes increase the capability of user experi- ence in the healthcare sector. These networked nodes pro- vide text, video, or image analytics with the help of robust AI mechanisms and sensor devices. For instance, monitor- ing patients in ambulances using edge devices or mobile devices assist technicians to deliver first-aid services with the advice of remotely available doctors before the patient was reached in hospitals; virtual reality enabled operating rooms are becoming a new normal for practicing edge sup- ported operations to patients. Edge and fog nodes reduce latency when compared to cloud-level analytics. This real-time delivery of findings is one of the major characteristics of healthcare applications. 3.1.4 Cloud services A large volume of scalable computations and analytics of healthcare data is possible in cloud infrastructures. In fact, the recent newer cloud computing execution model such as serverless clouds has manifested the feasibility of pro- viding reduced cost to the users. Typically, in healthcare applications, periodic monitoring of health-check devices is practiced. The frequency of remote monitoring is lower in some applications where the patients involved in the pro- cesses are quite normal. If a serverless execution model is not provided for such applications, the user could lead to huge costs due to the utilization of cloud server instances. In addition, the sensor data could be of higher size rang- ing from terabytes to zettabytes in healthcare applications, especially when video analytics of operations were in- volved. Obviously, there is a dire need for an automated scalable environment for healthcare applications. It could be noticed that to improve the prediction accu- racy in remote health monitoring applications cloud ser- vices requires long-term analytics obtained from a larger dataset. For instance, bots increase accuracy by improv- ing text analysis strategies; analytics of images is required for classifying, learning, or predicting health-related symp- toms such as tumor analysis, cancer analysis [50, 21], eye- retinal failure detections, and so forth; analytics of videos is required for learning the emotions of patients, neurological disorders, and sleep disorders. 3.1.5 Hospitals and doctors Remote healthcare monitoring architectures enable an ac- tive involvement of doctors or hospitals. For instance, mobile phones engage doctors and individuals; and, as- sociated servers involve hospital authorities for decision- making processes. These mobile devices and gadgets con- nect doctors/hospitals in a remote fashion to strengthen the assurance of patients. Observing the most existing remote monitoring appli- cations, doctors and hospitals are connected for provid- ing online prescriptions/diagnostics [18], scheduling doc- IoT-Enabled Remote Monitoring Techniques for. . . Informatica 46 (2022) 131–149 135 Figure 2: Devices Utilized in Remote Health-care Applications. tor appointments, sharing files/videos, communicating ef- fectively, and so forth. 3.2 Existing architectures and future insights Most of the existing architectures/frameworks developed for monitoring patients has shown similarities with the generic architecture. Table 3.2 reveals the key compo- nents of generic architecture adapted in the existing archi- tectures and the uniqueness found in them. For instance, authors of [49] developed healthcare monitoring system for Saskatchewan, a specific region in Canada; authors of [62, 16] included alerts, notifications, or early warn- ing systems for agile operations. In the table, “Y“ re- sembles “YES“. A few architectures submitted medical data to cloud services using publish/subscribe approaches ([56, 29]). There exist a few works in the healthcare moni- toring domain that improved the security aspects of med- ical data using blockchains ([59]); and, the data analyt- ics of healthcare data using novel deep learning algorithms ([33]). Additionally, frameworks that improve the human behavior/attitude by assessing the mental states had opened a novel research dimension in the healthcare sector. Apart from the existing healthcare monitoring frame- works/architectures, the following modules or techniques could be incorporated in the near future architectures of the remote healthcare monitoring systems: 1. Serverless-based cloud execution model – It is a known fact that cloud services or the associated re- sources are not required throughout the entire period of healthcare applications. For instance, a few ap- plications might only update the health status of re- mote patients on cloud storage devices once in a week. Considering the infrequent utilization of the services, it is sufficient to adopt serverless-based based cloud execution models for these applications; 2. Blockchain-enabled medical data protection systems – Tampering medical data or records is not tolerated in many countries or policies. Blockchain-enabled frameworks/architectures could solve these underly- ing issues by involving multi-party stakeholders; 3. End-to-End auditing framework – A very few efforts have been attained in the past to audit the functions or applications at end-to-end level. The remote mon- itoring healthcare applications execute logics that are computed on multiple nodes or servers of varied com- putational capabilities. By diligently auditing the ser- vices, the optimization of resources or efficiency of computations could be improved. 4 Remote monitoring techniques – taxonomy The remote monitoring techniques could be classified de- pending on several factors, as follows: i) based on the uti- lization of different computing systems, ii) based on impor- tance to serve tasks (priorities), iii) based on communica- tions involved, iv) based on accessibility features, v) based on level of intelligence, vi) based on specified metrics, and vii) based on focused delivery. This section explores the most available IoT-enabled remote monitoring techniques and their characteristics in detail. 4.1 Computing continuum assisted techniques Remotely monitoring the health-related parameters of healthcare applications involves varied computing devices surpassing from battery-operated sensor devices to scal- able clouds. Depending on the utility pattern of computing nodes, the remote healthcare monitoring applications could be either performance-efficient or cost-efficient. In most healthcare applications, edge nodes are pre- dominantly applied for the immediate analysis of sensor data. Authors of [14] have applied edge devices to reg- ister patients, authenticate them, and raise alarms using blockchains. Similarly, in [51], authors have designed a BodyEdge platform that connects most of the patients’ sen- sor data with edge nodes for improving the scalability; au- thors of [47, 73] have studied the impact of energy effi- ciency, and so forth. These edge devices are often battery- operated with limited processing capabilities. Edge nodes, in fact, deal with decentralized medical data obtained from the nearby sensor modules. In recent years, a few implementations including con- tainers such as docker containers in edge nodes have been 136 Informatica 46 (2022) 131–149 S. Benedict Table 1: A Comparison of Existing Healthcare Monitoring Architectures. Components [49] [62] [56] [16] [50] [29] [73] [59] [33] [38] UI Y Integration Units Y Y Integration Units Y - Integration Units Y - End Device Y Y Sensors (Wearables) Sensors (Wearables) Y Sensors (Wearables) Y Y Y Y Edge/Fog Hierarchical (Mesh) Fog - Fog Y - - Y Edge - Cloud services Y Y MQTT Y Y MQTT Y Y Y Y Hospital/ Doctors Y Y Y Y Y Y Y Y Y Y Uniqueness Saskatchewan (Canada) Alerts Publish/ Subscribe Early Warning Cancer care Elderly ECG Compression Blockchain security Deep Learning Persuasive Technology adopted to improve the lightweight migrations and execu- tions. For instance, authors of [34] have utilized raspberry pi-based architecture to execute containers on edge nodes for enabling the visual representations of healthcare appli- cations. Additionally, RFID clusters placed in the edge layer improve the connectivity of sensor nodes in some healthcare applications. Authors of [3] have utilized ac- tive RFID devices to establish RFID clusters and monitor patients at home. Compared to Edge nodes, Fog nodes handle a large vol- ume of data collected from multiple sensor modules of the vicinity [8]. Typically, fog nodes are responsible to process sensitive data and autonomously decide on performing cer- tain actions without the knowledge of the entire status of applications available in the cloud. Clouds, on contrary, are often utilized by many health- care applications. They are highly scalable with the ca- pability to adapt to the increasing data size and computa- tional requirements – i.e., the storage space for handling a large volume of data in the cloud is very high. Clouds are also capable of quickly processing/transferring the analy- sis observed from the large sensor data. In the recent past, authors of [52] have applied a cloud ecosystem to provide health diagnoses among students. Similarly, authors of [15] highlighted the importance of cloud services for analyzing health data in a hierarchical fashion. Obviously, clouds, due to the capability of their larger processing power, re- mote diagnostics within a limited time frame is possible. However, clouds are powered on for the entire duration of the remote monitoring healthcare applications. Overriding the continuous execution of cloud instances, serverless cloud environments spawn compute nodes based on the trigger received from sensor nodes. Serverless doesn’t mean that the computations are performed without servers. Rather, it is an execution model of clouds where the users are benefited from the limited utility of servers. Depending on the availability of servers, the serverless framework could either power on and boot up the machines or it could utilize the available idle server instances. The scalability component of the different continuum of computational units involved in the remote monitoring healthcare applications varies – i.e., increasing the num- ber of server instances and cloud-enabled services, scales the spanning of applications across the globe. Similarly, it could be noticed that the cost of including only server- less functions for healthcare applications would drastically reduce their operational/infrastructural costs of them. On contrary, this method reduces the serviceability when com- pared to involving the combination of edge, fog, cloud, and serverless environments. 4.2 Priority-enabled remote monitoring Remotely monitoring the health status of individuals is clouted by the priority in attending tasks/requirements. For instance, a healthcare monitoring application should prior- itize the monitoring features involving healthcare workers such as doctors and monitoring patients – i.e., in a health- care remote monitoring system, monitoring the health sta- tus of healthcare workers, especially in isolated wards, is quite important than serving the other devices or individu- als. Also, cloud services have to be prioritized with sufficient intelligence in an automated fashion. In general, the re- quests are served on a first-come-first-serve basis. How- ever, policies and protocols need to be provided in the re- mote healthcare monitoring applications so that notified prioritized activities are executed. Depending on the tasks specified in Figure 4.7, priori- ties could be set up for remotely monitoring the healthcare applications, as follows: 1. Critical Tasks Availing a secured data channel to ex- press the health status of patients to hospitals/doctors is one of the crucial tasks in remote monitoring health- care applications. Healthcare applications have to pri- oritize these tasks and execute them within the limited computational node availability. 2. Periodic Tasks There exist tasks in the remote health- care sector to pursue routine checks and monitoring mechanisms. Routine checking is not only specific to the patients but also to the machines involved in send- ing data. For instance, monitoring the patient’s tem- perature, pressure, glucose, and so forth is a common practice in the health sector domain. However, mon- itoring the health status of the robotic machines in- volved in the operations or learning the failure cases of machines using IoT-enabled systems are crucial tasks. 3. Preventive Tasks A few healthcare tasks are preventive in nature. Mechanisms involved in preventing medi- IoT-Enabled Remote Monitoring Techniques for. . . Informatica 46 (2022) 131–149 137 cal failures or diseases require lots of artificial intelli- gence embedded in the system [28, 46]. For instance, learning algorithms such as random forests, support vector machines, linear regressions, deep learning models, and so forth could be applied to preventing the patients from severity in the impacts. A few al- gorithms such as convolutional neural networks are applied to early detect cancer symptoms from images relating to skin, lungs, and brain. 4. Federated Tasks In remote monitoring, collaborative engagement of multiple peers or medical things is in- volved. Examples such as analyzing the patient simi- larity or emotion analysis require collaborative learn- ing involving sensors from various hospitals and or- ganizations. A federated learning mechanism needs to be incorporated into these systems. In federated learning processes, the global learning models are re- fined by the distributed local learning models. This way, a robust process of targeting multiple machines and organizations is possible for healthcare applica- tions. Applications such as collaborative drug discov- ery with the involvement of sensor data and machines from multiple organizations are carried out using fed- erated mechanisms. 5. Alarming/Actuating Tasks Healthcare monitoring also involves tasks to send notifications or alarms by actu- ating appropriate actuators. Hospital beds might have to be tilted to 60 degrees to comfort patients, espe- cially for patients with Parkinson’s disease. Similarly, eye-related operations are supported with laser posi- tioning equipment in eye hospitals. Based on the feed- back given by the doctor, the position of the lasers has to be accurately organized in real-time. 6. Educational Tasks Providing training or awareness of- ten prevents the spread of diseases such as malaria, COVID-19 [54], and so forth. Tasks relating to aware- ness have increased due to the inclusion of mobile de- vices and mobile health applications in recent years. Although these tasks are not crucial to execute in real-time, they have to get a wider reach with mini- mal costs and failure rates. Additionally, these tasks have to support multiple formats such as audio, video, or texts that choose the most appropriate protocols. These tasks have to increase the public reach in the form of successful campaigns incorporated in wear- able gadgets. Table 2 reveals the comparison of tasks in the remote monitoring healthcare domain. The importance of certain characteristics is specified in the star ratings for tasks. It could be observed that the periodic tasks have to be cost- efficient whereas critical tasks must be completed within a specific time-bound. The critical tasks have to provide more priority to performance efficiency than cost efficiency as they are crucial life-saving tasks. Similarly, educational tasks or alarming tasks aim at reaching a larger mass of connected people or automated systems for delivering mes- sages. 4.3 Communication-specific RM The remote monitoring of health applications requires an apt selection of communication protocol standards for con- necting sensors. IoMT devices are connected using WIFI, Bluetooth4.0, GPS, infrared, Zigbee, and so forth (see Fig- ure 4.7). High bandwidth requirements are often the scenario in healthcare-related applications, especially when operations were held by doctors using videos or robotic engines. To compensate the high-speed bandwidth requirements, 5G, NB-IoT [12, 57], or similar connections are often set up in the modern hospital premises. The utility of 5G is im- mense: for instance, authors of [11] have studied the im- portance of 5G and the design criteria such as channel se- lection options, bandwidth efficiency, and so forth, in the healthcare applications. Particularly, narrow-band commu- nications for healthcare have profoundly been utilized in the healthcare sector. The main reason is that the NB-IoT communication medium could provide low-energy support to sensors. However, due to the limited bandwidth support and security breaches [59], 5G overrides NB-IoT. There ex- ist a few works relating to NB-IoT in the past. For instance, the authors of [31] studied the uplink/downlink transmis- sion efficiencies of healthcare sensor nodes when NB-IoT was applied as communication links. A few researchers have improved the transmission efficiency of communi- cation links by designing software-defined networks. In the work by Farag et al. [58], the authors have designed an SDN approach to channelize sensor nodes for efficient communications with limited traffic delays. In addition, ISO/IEEE 11073 protocols are imposed on healthcare devices for establishing efficient secure commu- nications and the delivery of data over these secured con- nections. Authors of [79] implemented the IEEE 11073 protocol to integrate tiny biosensors to the cloud using Con- strained Application Protocol (CoAP). The important features of the communications involved in the remote health monitoring applications include: 1. Unique identification: Several devices might intend to connect to each other or the cloud using various means of connectivity options such as wifi, Bluetooth, or in- frared. A unique identification mechanism is required in healthcare applications to avoid the wrong delivery of insights to medical practitioners or users. 2. Interoperability feature: The devices manufactured from different vendors such as Samsung, OPPO, cisco, intel, and so forth, might need robust interop- erability features [55] so that the healthcare applica- tions are effectively laid in a vicinity. To ensure inter- operability, the exchange of data in a specified format and the conversion of protocols are mandatory pro- cesses. There exist a few protocol conversion mech- 138 Informatica 46 (2022) 131–149 S. Benedict Table 2: Comparison of Priority-Enabled Tasks – Characteristics. Tasks Time/Speed Cost Efficient Performance Efficient Wider Reach Critical ***** * **** *** Periodic * **** ** ** Preventive ** * **** *** Federated * *** *** **** Alarming *** ** *** ***** Educational ** **** ** **** anisms which could be incorporated into the gateway devices or edge devices for solving the interoperabil- ity issues. 3. Cooperative Aspect: In some cases, cooperative decision-making features and collaborative involve- ment of devices are required to accomplish the re- mote healthcare monitoring of patients. For instance, studying the behavior of patients could be analyzed by sensing data across hospitals. Here, an inter-hospital communication system that augments the collabora- tive learning of patients is desired. Similarly, sharing the findings of patient information across multi-specialty hospitals also claims for a co- operative mechanism in the healthcare sector. 4.4 Accessibility-specific RM Access to healthcare services is bound to financial capabil- ities. Accessing international medical equipment or hospi- tal facilities requires uninterrupted services with the abil- ity to audit the necessity of facilities/equipment. In addi- tion, healthcare services should automatically assign scal- able computational units or storage units for the delivery of medical assistance. Depending on the accessibility feature of remote mon- itoring assistance, the healthcare services could be clas- sified into local or global remotely accessible monitoring services. In local accessibility setup, the underlying med- ical services comprise intra-hospital medical things such as oximeter measurements, blood pressure monitoring ser- vices, and so forth. In a global accessibility set up, the ser- vices involve inter-hospital services and doctors for moni- toring the health status of patients. In general, the global accessibility services are meant for collecting the health status of pandemics such as COVID-19, examining infec- tious diseases, analyzing the causes of deaths across coun- tries, and so forth. 4.5 Intelligent level specific RM Associating artificial intelligence to the internet of medi- cal things is quite important for automating processes and equipping with more accurate results – an option to deliver proactive healthcare to patients/users. An array of applica- tions have evolved in the recent past with the inclusion of AI methods for assisting healthcare services. For instance, predicting the hospital risks in a remote fashion or devel- oping AI-powered robots to assist patients have been well appreciated among the healthcare communities. Technologies such as computer vision, which embodies AI into it, obtain information from images or video frames to provide meaningful insights. For instance, performing remote clinical trials could be effectuated using computer vision. Similarly, AI-driven big data processing of medical machines [33] or patients encourages multi-specialty hos- pitals or concerned doctors to proactively strengthen the prescriptions with utmost accuracy. Multi-label classification-based AI methods, which uti- lize robust learning algorithms, improve the decision sup- port systems. These approaches promote automation in re- mote health monitoring applications. Additionally, tailor-made solutions could be adopted us- ing ontologies [2] and semantic technologies [68, 78] for enabling health successes. For instance, suggesting the timing and duration of regular exercises, closely monitor- ing the performance of healthcare, and so forth. The incorporation of AI in remote health monitoring ap- plications increases the revenue due to the involvement of many doctors or hospitals. In addition, it improves the cost and accuracy efficiency of patients/users. For instance, au- thors of [72] provided an approach to handle the trade-off between sensing health details and advising remedies ir- respective of the less availability of health datasets using adversarial sensing method. 4.6 Performance metric-specific RM Remote monitoring of patients can be fine-tuned based on the metrics applied. For instance, the metrics such as makespan of healthcare applications, availability of service requests, scheduling features of applications, and so forth, could be improved by skillfully choosing the metrics in al- gorithms or implementations. The most common metrics that typically improve the performance of healthcare applications are listed as fol- lows: 1. Round-Time: This metric determines the time taken for delivering the service requests. This round-time metric involves time to select the doctor, choose the right services, and so forth. In [36], authors have sim- ulated a case study to manifest the importance of re- sponsiveness metrics while designing healthcare ap- plications in cloud environments. The authors re- vealed the scalable feature of clouds which improved IoT-Enabled Remote Monitoring Techniques for. . . Informatica 46 (2022) 131–149 139 the round-time of sensor applications. Also, authors of [45] discussed how low-latency could be improved in healthcare applications when edge devices were in- cluded in the monitoring system. 2. Reliability: Reliability metric checks the availability of services or medical things for processing applica- tions. It includes features for evaluating the availabil- ity of services. A few researchers have studied the re- liability feature of healthcare applications. Notably, authors of [44] designed oneM2M protocol that in- cluded a fault-tolerant algorithm for increasing the re- liability in the gateway layer of sensor networks. 3. Energy consumption: As similar to the performance of services, this metric, if fine-tuned, attempts to reduce the energy consumption of applications. In fact, the energy consumption of applications has to be diligently handled, especially for executing appli- cations in power scarce locations or power-constraint devices. This metric is one of the most crucial ones for assessing healthcare applications. Authors of [18] proposed energy-efficient optimization-based cluster- ing approach for connecting sensor nodes of the pa- tient monitoring system. Their results, when applied with the Particle Swarm Optimization (PSO) tech- nique have manifested to diagnose diseases with a minimum energy value. 4. End-to-end encryption: Providing end-to-end encryp- tion for healthcare applications could improve their security and privacy features of them. Blockchain fea- tures are incorporated in some healthcare applications to improve security aspects of them [30, 40]. A few authorization approaches are studied in [74]. How- ever, a tradeoff exists between the performance and security aspects of applications. 5. Inter-operability count, The inter-operability feature specifies the number of sinking devices of a medical thing. A higher number of interoperability metrics re- veals the capabilities of the device to connect to more healthcare applications. 4.7 Focused delivery-specific RM The mechanisms applied for healthcare applications could be focused on solving specified objectives – i) Emergency purposes [39], ii) Elderly Care, iii) Disability Care [60], and iv) Behavioral/Mental Care [37, 38, 81]. The mech- anisms implemented to solve these specified objectives are unique. For instance, focused delivery of healthcare services to elderly people requires specialized care for bathing, fall detection, medicine reminders, personal hy- giene, and emotional care. Most preferably, these elderly people staying in old-age homes are depressed due to emo- tional avoidance from family or relatives. IoT-enabled sys- tems could drive them to play comforting music or their preferred games, protect them from more-likely falls by actuating airbags, warm up them during winter seasons us- ing heat-actuated jackets, and so forth. Researchers have started to work on these focused deliveries of medical as- sistance in recent years. For instance, authors of [29] pro- posed a three-tier framework involving medical centres to care the elderly people; authors of [13] investigated the pro- cedure to handle older adults considering scheduled medi- cal consultations. Authors of [37, 38] introduced a novel persuasive tech- nology approach for improving the behaviors or mental states of human beings. The persuasive technology, asso- ciated with IoT-enabled wearable gadgets, could improve the cost involved in hiring mental healthcare professionals. For instance, AI-assisted chat bots could control the suicide attempts of young minds if they are semantically designed to tailor the needs of defaulters. 5 Remote monitoring healthcare applications – discussions Remote monitoring healthcare applications are drastically shaping the future, especially during the post COVID-19 pandemic era. These healthcare applications modernize the approach of living standards as appropriate management procedures, sensor nodes, communication methodologies, detection/diagnostic algorithms, tools, and datasets have been evolved in the recent past. 5.1 Applications – categories Establishing an intuitive understanding of the character- istics or requirements of the existing remote monitoring healthcare applications is an initial primordial step to de- liver newer techniques and innovations. In fact, a few orthogonal research dimensions were identified based on i) applying managerial techniques, ii) addressing specific health concerns, and iii) utilizing assistive technologies. 5.1.1 Management-oriented The involvement of IoT sensors and associated technolo- gies has fixated in a few healthcare applications for re- motely managing hospital premises or knowledge acqui- sition. Mechanisms need to be channelized to manage sec- torial growth in healthcare. Hospital management Clinical trials, especially during the post-COVID era, have seen a shift of focus in managing hospital premises with the advent of IoT technologies. The most diverse remote monitoring which manages hospital premises involve: 1. organizing medical equipment and devices based on the knowledge shared by the IoT gadgets; 2. optimizing the location layout of medical equip- ment such as wheelchairs, oxygen defibrillators, and 140 Informatica 46 (2022) 131–149 S. Benedict Figure 3: Taxonomy of Remote Healthcare Monitoring. so forth, for distributing them across the hospital premises; 3. designating manpower such as doctors, nurses, clean- ing staff, and security guards considering their will- ingness to serve wards and overseeing the need of de- partments; 4. automatizing the collaborative involvement of ma- chines and sensors to coordinate the out/in patients during the stay in hospital premises; 5. managing assets in pharmaceutical department of hos- pitals, and, so forth. Drug management In addition to managing hospital premises, IoT sensors and remote monitoring solutions are often utilized to trigger the well-being of patients by man- aging the drug delivery processes. There exist a few drug management solutions as listed below: 1. Drug Reminders – Solutions and medical kits were de- veloped to constantly remind patients of the intake of drugs. In doing so, chronic patients are benefited at large. There have been several cases of deaths or med- ical emergencies as patients such as diabetes consume overdose or low dose insulin owing to repressing the consumption of drugs. 2. Drug Identification – In some situations, it is required to identify the complications due to drugs. Particu- larly, the previous history of patients with respect to the drugs has to be evaluated based on IoT systems or medical records. Investigating the real-time effect of drugs using IoT-enabled applications has bolstered the reduction of side effects to patients. 3. Drug Governance – Pharmaceutical companies and drug delivery units of countries require a machine- assisted system that senses and administers optimal supply chain management. Appropriate communi- cation technologies and processing of data have to be handled in these machine-enabled applications for providing better logistics. Data management One of the key points to converge IoT and healthcare applications is data management – i.e., a clear plan to perform data acquisition from sen- sor nodes considering geographical locations or large-scale data. Healthcare applications need to consider the contin- uum of computing devices located in edge, fog, or cloud environments [49]. Handling data in edge nodes based on the latency and real-time requirement of applications has to be automatized in the data-related IoT healthcare applications. There exist a few data management tools for healthcare applications such as Enterprise Data Ware- houses (EDWs) for processing data collected from diverse sensor nodes in real-time. A few companies such as Snap- Logic have delivered a platform-as-a-service model to elas- tically scale data integration services. 5.1.2 Health-specific IoT-enabled healthcare applications that target addressing health issues could be classified depending on i) chronic IoT-Enabled Remote Monitoring Techniques for. . . Informatica 46 (2022) 131–149 141 diseases, ii) Disease-specific or iii) Preventive care dis- eases. Chronic Diseases Remotely monitoring chronic diseases with the intention of reducing the death rate and improving cost efficiency is one of the most awaited purposes of ap- plications in this pandemic era. Chronic patients such as diabetic Mellitus, cardiovascular diseases, respiratory dis- eases, malignant neoplasms, cancer patients, and so forth, have to periodically visit hospitals if remote monitoring was not extended to the patients. It is to be noticed that over 85 percent of elderly people are prone to at least one of the above-mentioned chronic diseases. Obviously, the costs involved in the patient monitoring processes have been re- duced due to the IoT-enabled healthcare system in such sit- uations. Common Diseases The majority of the diseases relating to general infections or deficiencies could be remotely as- sisted using IoT-enabled healthcare systems. The solutions are most welcomed by individuals, including transnational healthcare aspirants owing to the following reasons: 1. Avoidance of hospital appointments, 2. Mobile-assisted interactions, 3. Frequent virtual meetings with doctors or hospital ad- ministrations, and, 4. Tailor-made solutions, including robotic solutions. In fact, home assistance in a remote fashion plays a vi- tal role in specific health-conscious diseases. In the past, a home nurse would be appointed to monitor the vital signs and administer medicines. With the advent of IoT-enabled mechanisms, home-based medical assistance is enthused for these specific-category patients. For instance, patients belonging to traumatic brain injury (TBI) or spinal cord injury prefer home assistance as traveling to hospitals for visiting the doctors is sometimes dangerous, especially for patients living in crowded societies. Tailor-made robots for home-assisted medical delivery are often practiced to pro- vide therapy such as speech therapy or physiotherapy. Preventive Care Preventive healthcare or prophylaxis is introduced decades ago to avoid potential risk factors of the living. IoT-enabled systems are designed and innovated to consider the sector of people requiring preventive care through appropriate remote healthcare monitoring mecha- nisms. It is also important to advise concerned patients to avoid their routine habits such as improper dietary, chewing to- bacco, smoking, consuming alcohol, and so forth, by un- derstanding the level of glucose, blood pressure, and body mass. In succinct, IoT-enabled devices and frameworks could automatically guide the patients and prevent them from many of the diseases with limited visits to the con- cerned hospitals/doctors. 5.2 Tools, libraries, and datasets – remote monitoring mechanisms Connected IoT devices and hospital premises have to in- clude sophisticated tools, including AI tools, to collect the right sensor data and prevent or diagnose diseases in a re- mote fashion. The most commonly utilized tools, libraries, and datasets for predicting diseases are discussed based on the remote monitoring techniques discussed earlier. 5.2.1 Tools for computing continuum The computing continuum consisting of edge, fog, cloud or serverless containers has most commonly utilized in IoT- enabled healthcare applications. It is often crucial to un- derstanding the most available tools that enable the right computing continuum for executing applications. For in- stance, offloading sensor data from medical things to edge or cloud or fog-connected cloud should be automatized in the healthcare application framework. The important functions for establishing similar tools to enable the computing continuum of IoT healthcare appli- cations are listed as follows: 1. Dynamic Configurations – Setting configurations that switch between edge or fog or cloud for executing healthcare tasks is required for incorporating comput- ing continuum in applications. For instance, the con- figurations could be set up using YAML files and pro- cessed at runtime using programs written in nodejs or golang. 2. Flow-specific Representations – The tool that pro- vides computing continuum in an automatic fashion needs specific tools to represent tasks and subtasks in a flow model. Obviously, workflow-specific tools such as kubeflow or workflow-description language- based tools will be utilized for specifying the flow of tasks. 3. Reliability Features – One of the crucial ingredient for developing tools that promote a continuum of com- puting in healthcare tasks is to provide reliability fea- tures. Reliability measures ensure the consistency delivery of computing resources to complete the as- signed tasks. 4. Statefulness – Stateful services, including the health status database, are important for completing work- flow tasks or for recreating container instances during failures. In IoT-enabled healthcare applications, there is a high possibility that the applications fail due to connectivity failures or the non-availability of appro- priate resources. Most importantly, a healthcare ap- plication that requires a cold-start computing instance could take a long execution time than executing it in a warm-start instance. 142 Informatica 46 (2022) 131–149 S. Benedict 5.2.2 Tools for prioritizing tasks Healthcare applications could easily lead to delayed re- sponses owing to the increasing number of remote mon- itoring requirements and decreasing number of available resources, including hospital accessibility or doctor avail- ability. Patient prioritization is, therefore, a crucial com- ponent of healthcare tools. There exist tools to prioritize tasks for delivering a fair prioritization of tasks. For in- stance, Clinical Priority Assessment Criteria (CPAC) is fol- lowed in New Zealand, Australia, and a few other countries to prioritize the need of outpatients. This approach is trans- formed to IoT-enabled healthcare applications for servicing the requests of online patients based on the availability of resources, including doctors. 5.2.3 Tools for communications As discussed earlier, several sensor nodes or wearables are involved in submitting the sensor data over a secured channel to edge, fog, or cloud compute instances. These sensor nodes communicate to the higher-level device or peer nodes using various communication protocols such as WIFI, Bluetooth4.0, LoRA, Infrared, LiDR, and so forth. 4G/5G/NarrowBand technologies are involved to connect the communication medium of devices. Tools are often required to automatically suggest con- necting to devices within hospital premises and outside hospital premises. For instance, the data rate outside hos- pitals could be very less when compared to the hospital premises. Obviously, the teleconference approach, espe- cially in video mode, to remotely monitor the health status of individuals has to be diligently handled by applications. Similarly, the devices are prone to electromagnetic interfer- ence. Hence, tools need to assist the application to utilize appropriate communication links. 5.2.4 Accessibility features Accessibility in healthcare applications has two dimen- sions: 1. Interface – The framework should involve interfaces to provide feasible and effective communications to patients/individuals. For instance, a patient might not have the strength to type in available gadgets to com- municate to doctors; the patient might find it difficulty to infer the suggestions delivered by doctors. In such a scenario, multiple assisting technologies should be combined to enable efficient communication and ac- cessibility of end devices or doctors/hospitals. 2. Device Accessibility – In addition, accessibility of dif- ferent connected medical things that follow different standards or protocols to collect data or deliver data is a challenge. Tools should enable the best possible approach to efficiently access devices or actuators be- longing to the application. 5.2.5 AI-enabled tools The inclusion of AI techniques in healthcare increases the detection accuracy or assisting nature of applications. However, appropriate tools and techniques have to be inte- grated into the system for achieving better results and expe- riences. The most notable approaches of utilizing AI tech- niques for healthcare applications could be listed as fol- lows: 1. NLP-assited interfaces: Natural Language Processing (NLP) methods are required in healthcare to deal with the large volume of healthcare records or datasets. NLP algorithms could parse the text messages from a large volume of data and extract the significance of the context for representing them through interfaces. The programs or algorithms associated with these meth- ods have to become robust and error-free – i.e., careful handling of the dataset is required for the NLP logic to represent the intended content of doctors or patients. 2. Deep-Learning or Models for inferences: Deep learn- ing or similar neural network-based learning models have manifested in the healthcare sector for their fast predictions or inferences without compromising the expected accuracy. The applications of deep learning models in health- care are immense. For instance, deep learning mod- els are utilized to detect anomalies in X-ray images, CT scans, MRI scans, and so forth; to analyze doc- uments or health records and predict diseases; and, so forth. For instance, authors of [15] have proposed deep learning-based hierarchical learning approach to improve the response time of sensor-enabled classifi- cation problems; also, authors of [53] applied neural networks to classify diseases. 3. Recommendation systems: AI is most commonly uti- lized as recommendation system in healthcare appli- cations. Several recommendations for providing ap- propriate diets or awareness about the spread of dis- eases are carried out using recommendation systems. In addition, drug recommendation is also carried out from the perspective of doctors to patients. These rec- ommendation systems have to collect sensed data in a secure fashion [19] from various sensors or med- ical records and reason recommendations depending on the presented contexts. Recently, in [24], authors have proposed a word similarity measure method us- ing learning algorithms which improves the recom- mendations of selecting online IoT-enabled medical services. 4. Predictions and Classifications: AI embodies sev- eral statistical and optimization techniques to predict health conditions or classify diseases. There exist sev- eral prediction and classification problems in the past: for instance, the application of decision trees, support IoT-Enabled Remote Monitoring Techniques for. . . Informatica 46 (2022) 131–149 143 Table 3: Remote Monitoring Techniques in Healthcare Applications. No Focused Perf. Metrics Computing Priority- level Comm. Protocols AI/ML [77] Tele ECG (Heart Rate) * Cloud Periodic WIFI * [71] Elderly * * Periodic WIFI * [16] * Exec. Time CPU Load Fog * WIFI * [25] Disability (Speech Disorder) * Mobile/ Edge Periodic WIFI NLP [20] Heart disease * Android/ Edge Continuous WIFI, Bluetooth * [31] Heart disease Throughput * * NBIoT * [50] Cancer care * Cloud, Hadoop * WBAN, WIFI, Zigbee ML/DL [32] Cancer care Sensitivity Cloud * WIFI ANN/PSO [1] Diabetes care Accuracy Cloud/ Fog/Edge Periodic WBAN Decision Support [6] * Energy Simulation Critical RFID, WBAN IEEE802.15.4 * [4] Cancer care Security * Critical WIFI Kernels/ ML [15] * Response Time Edge, VMs * Hierarchical DL [34] * * Container Edge Periodic WIFI, Zigbee, Bluetooth * [80] Chronic Diseases Energy harvesting Edge, Cloud Periodic Bluetooth4.0 * [3] * Round Time Energy WBN cluster Simulation * RFID, WBN * [7] * Reliability, Response Time Fog, Edge * 5G, Fibre/xDSL * [17] Non Focused Latency, Computation Cost Cloud Periodic WIFI, 5G RS [39] Emergency * Hadoop, Cloud Critical 6LoWPAN, RFID * 144 Informatica 46 (2022) 131–149 S. Benedict vector machines, K-nearest neighbor algorithms, lin- ear regressions, and so forth, were quite common in the healthcare domain. In addition, several unsuper- vised learning algorithms such as reward-based agent systems and reinforcement learning methods were in- corporated in the applications. In a few medical diagnosis approaches, researchers have proposed federated learning mechanisms to pro- tect the privacy of patients by protecting the data within local compute resources. By incorporating fed- erated learning models, fine-tuned local learning mod- els were updated in global models for fast predictions. In fact, these learning mechanisms are beneficial for mobile-assisted learning systems [66] where the local models are executed in battery-powered mobile de- vices. 5. Generative Adversarial Networks (GANs): In a few cases, the electronic medical records could not be ex- posed to the public for exploration which keeps the su- pervised learning models such as Convolutional Neu- ral Networks (CNNs) a failure. GANs, an unsuper- vised AI learning model, support the learning pro- cess in such cases. GANs, in general, involve both the generator model and discriminator model for the learning dataset to increase the positivity of the pre- dictions. For instance, the electronic medical records for Zika, a mosquito-borne virus infection, in Kerala are very rare. In such situations, GANs could pro- duce some additional datasets without compromising the accuracy of predictions. 5.3 Comparison of remote monitoring applications Table 3 illustrates the remote monitoring healthcare ap- plications that were developed by researchers and practi- tioners in the past. It highlights the target groups such as the elderly, common diseases addressed, and so forth; the performance metrics analyzed in the applications such as execution time, CPU load, energy, round-time, and so forth; the computational units involved in the applications such as containers, VMs, Edge, Fog, Cloud, serverless, or hierarchical compute instances; the priority-level initi- ated in addressing the tasks; the communication protocols which connected sensors with gateways or healthcare ap- plications/services; and the intelligence level incorporated in the applications. The notable observations from the existing remote mon- itoring healthcare applications are listed as follows: 1. Majority of the applications utilized WIFI-based com- munication protocol to connect sensor nodes or hospi- tal devices; 2. Among the available AI-assisted healthcare applica- tions, deep-learning algorithms, and their variants were incorporated in the solutions for better accuracy and resolutions; 3. Almost all applications that were not simulated in- cluded a compendium of computing instances such as edge, fog, cloud, or VMs in a hierarchical fashion for pursuing the detection of diseases or providing remote medical assistance. 6 Conclusions The prevalence of IoT-enabled healthcare applications has urged the inclusion of novelties to support individuals and doctors during the pandemic era. Investigating the remote monitoring techniques in the healthcare sector is a vital role in delivering superlative care by doctors to patients. In this paper, the most predominantly applied remote monitoring techniques were studied. Based on the findings, a generic remote monitoring healthcare architecture was elaborated and the taxonomy of remote monitoring techniques was il- lustrated. Additionally, techniques practiced in the existing remote monitoring healthcare applications and associated research directions were enlightened for the researchers. Compliance with ethical standards – Funding: This study was funded by IIIT-Kottayam Faculty Research Fund. – Conflict of interest/Competing interests: The author declares that he has no conflict of interest. – Ethics approval: This article does not contain any studies with human participants or animals performed by any of the authors. – Consent to participate: Yes – Consent for publication: Yes – Authors’ contributions: The corresponding author did the entire survey work. – Informed consent: Informed consent was obtained from all individual participants included in the study. References [1] Abdel-Basset M., Manogaran G., A. Gamal and V . Chang, A Novel Intelligent Medical Decision Support Model Based on Soft Computing and IoT, in IEEE In- ternet of Things Journal, 7(5), pp. 4160–4170, 2020. https://doi.org/10.1109/jiot.2019.2931647 [2] Abdullah A., Ontology Middleware for Integra- tion of IoT Healthcare Information Systems in EHR Systems., Computers. 7(51), pp. 1–15, 2018. https://doi.org/10.3390/computers7040051 IoT-Enabled Remote Monitoring Techniques for. . . Informatica 46 (2022) 131–149 145 [3] Abuelkhail, A., Baroudi, U., Raad, M. et al. Internet of things for healthcare monitoring applications based on RFID clustering scheme. Wireless Netw, V ol. 27, pp. 747–763, 2021. https://doi.org/10.1007/s11276- 020-02482-1 [4] Ali Burak Unal and Mete Akgun and Nico Pfeifer, ESCAPED: Efficient Secure and Private Dot Prod- uct Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare, arXiV , 2012.02688, pp. 9982-9995, 2020. [5] Alromaihi S., W. Elmedany and C. Balakrishna, Cy- ber Security Challenges of Deploying IoT in Smart Cities for Healthcare Applications, 2018 6th Interna- tional Conference on Future Internet of Things and Cloud Workshops (FiCloudW), pp. 140-145, 2018. https://doi.org/10.1109/w-ficloud.2018.00028 [6] Aktas F., Ceken C. and Erdemli Y .E., IoTBased Healthcare Framework for Biomedical Applications, J. Med. Biol. Eng. V ol. 38, pp. 966–979, 2018. https://doi.org/10.1007/s40846-017-0349-7 [7] Akrivopoulos O., I. Chatzigiannakis, C. Tselios and A. Antoniou, On the Deployment of Health- care Applications over Fog Computing infrastructure, IEEE 41st Annual Computer Software and Applica- tions Conference (COMPSAC), pp. 288-293, 2017. https://doi.org/10.1109/compsac.2017.178 [8] Aladwani T., Scheduling IoT Healthcare Tasks in Fog Computing Based on their Importance, in Proce- dia Computer Science, V ol.163, pp. 560–569, 2019. https://doi.org/10.1016/j.procs.2019.12.138 [9] Alam A.A., H. Malik, M. I. Khan, T. Pardy, Kuusik and Y . Le Moullec, A Survey on the Roles of Communication Technologies in IoT- Based Personalized Healthcare Applications, in IEEE Access, V ol. 6, pp. 36611-36631, 2018. https://doi.org/10.1109/access.2018.2853148 [10] A.S. Albahri and Jwan K. Alwan and Zahraa K. Taha and Sura F. Ismail and Rula A. Hamid and A.A. Zaidan and O.S. Albahri and B.B. Zaidan and A.H. Alamoodi and M.A. Alsalem, IoTbased telemedicine for disease prevention and health promotion: State- ofthe-Art, in JNCA, 173(102873), pp. 1–52, 2021. https://doi.org/10.1016/j.jnca.2020.102873 [11] Amin Ul Haq, Victor Ejianya, Jalaluddin Khan, Asif Khan, Mudassir Khalil, Amjad Ali, Ghufran A. khan, Mohammad Shahid, Bilal Ahamad, Amit Yadav et al., A New Approach for Enhancing the Services of the 5G Mobile Network and IOT-Related Commu- nication Devices Using Wavelet-OFDM and Its Ap- plications in Healthcare, V ol. 2020, pp. 1–13, 2020. https://doi.org/10.1155/2020/3204695 [12] Anand S. and S. K. Routray, Issues and challenges in healthcare narrowband IoT, 2017 International Con- ference on Inventive Communication and Computa- tional Technologies (ICICCT), pp. 486-489, 2017. https://doi.org/10.1109/icicct.2017.7975247 [13] Ara ujo I talo Linhares De, Costa Junior Evilasio, Duarte Paulo, Santos Ismayle De Sousa, Oliveira Pedro Almir Martins De, Mendes C ıcero Marcelo Oliveira, Andrade Rossana Maria De Castro, Towards a Taxonomy for the Development of Older Adults Healthcare Applications, in Proc. of the 53rd Hawaii International Conference on System Sciences, 2020. https://doi.org/10.24251/hicss.2020.430 [14] Aujla G.S. and A. Jindal, A Decoupled Blockchain Approach for Edge-Envisioned IoT-Based Health- care Monitoring, in IEEE Journal on Selected Areas in Communications, 39(2), pp. 491-499, Feb. 2021. https://doi.org/10.1109/jsac.2020.3020655 [15] Azimi I., J. Takalo-Mattila, A. Anzanpour, A. M. Rahmani, J. Soininen and P. Liljeberg, Empowering Healthcare IoT Systems with Hi- erarchical Edge-Based Deep Learning, 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineer- ing Technologies (CHASE), pp. 63-68, 2018. https://doi.org/10.1145/3278576.3278597 [16] Bahar Farahani and Farshad Firouzi and Victor Chang and Mustafa Badaroglu and Nicholas Con- stant and Kunal Mankodiya, Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare, in Future Generation Computer Systems, V ol. 78, pp. 659-676, 2018. https://doi.org/10.1016/j.future.2017.04.036 [17] Bhattacharya P., S. Tanwar, U. Bodkhe, S. Tyagi and N. Kumar, BinDaaS: BlockchainBased Deep- Learning as-a-Service in Healthcare 4.0 Appli- cations, in IEEE Transactions on Network Sci- ence and Engineering, 8(2), pp. 1242-1255, 2021. https://doi.org/10.1109/tnse.2019.2961932 [18] Bharathi R., T. Abirami, S. Dhanasekaran, Deepak Gupta, Ashish Khanna, Mohamed Elhoseny, K. Shankar, Energy efficient clustering with dis- ease diagnosis model for IoT based sustainable healthcare systems, Sustainable Computing: Informatics and Systems, 28(100453), 2020. https://doi.org/10.1016/j.suscom.2020.100453 [19] Cansu Eken, and Hanim E, Security Threats and Recommendation in IoT Healthcare, doi. 10.3384/ecp17142369, pp. 369-374, 2016. [20] Chao Li and Xiangpei Hu and Lili Zhang, The IoT-based heart disease monitoring system for 146 Informatica 46 (2022) 131–149 S. Benedict pervasive healthcare service, in Procedia Com- puter Science, V ol. 112, pp. 2328-2334, 2017. https://doi.org/10.1016/j.procs.2017.08.265 [21] Danish Jamil, Diagnosis of Gastric Cancer Using Ma- chine Learning Techniques in Healthcare Sector – A Survey, in Informatica – An Int. J. of Computing and Informatics (Slovenia), 45(7), pp. 147–166, 2021. https://doi.org/10.31449/inf.v45i7.3633 [22] David Charlas Amos, Remote patient monitoring, patent, https://lens.org/010-983-959-342-733, 2017. [23] De Morais Barroca Filho I., De Aquino Junior G.S., IoT-Based Healthcare Applications: A Review. In: Gervasi O. et al. (eds) Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lec- ture Notes in Computer Science, V ol. 10409, 2017. https://doi.org/10.1007/978-3-319-62407-5_4 [24] Dehai Zhang, Xiaoqiang Xia, Yun Yang, PoYang, Cheng Xie, Menglong Cui, Qing Liu, A novel word similarity measure method for IoT-enabled Healthcare applications, Future Generation Com- puter Systems, V ol. 114, pp. 209-218, 2021. https://doi.org/10.1016/j.future.2020.07.053 [25] Dubey H., J. C. Goldberg, K. Makodiya, and L. Mahler, A multi-smartwatch system for assessing speech characteristics of peo- ple with dysarthria in group settings, in Pro- ceedings e-Health Networking, Applications https://doi.org/10.1109/healthcom.2015.7454559 [26] Elhayatmy G., Dey N., Ashour A.S., Internet of Things Based Wireless Body Area Network in Healthcare. In: Dey N., Hassanien A., Bhatt C., Ashour A., Satapathy S. (eds) Internet of Things and Big Data Analytics Toward Next-Generation Intelli- gence. Studies in Big Data, vol 30. Springer, Cham. 2018. https://doi.org/10.1007/978-3-319-60435-0_1 [27] Faris A. Almalki and Ben Othman Soufiene, EP- PDA: An Efficient and PrivacyPreserving Data Aggregation Scheme with Authentication and Authorization for IoT-Based Healthcare Appli- cations, Wireless Communications and Mobile Computing, 2021(5594159), pp. 1–18, 2021. https://doi.org/10.1155/2021/5594159 [28] Fouad H., Azza S. Hassanein, Ahmed M. Soli- man, Haytham Al-Feel, Analyzing patient health information based on IoT sensor with AI for im- proving patient assistance in the future direction, in Measurement, 159(107757), pp. 1–11, 2020. https://doi.org/10.1016/j.measurement.2020.107757 [29] Guizani K. and S. Guizani, IoT Healthcare Mon- itoring Systems Overview for Elderly Population, 2020 International Wireless Communications and Mobile Computing (IWCMC), pp. 2005-2009, 2020. https://doi.org/10.1109/iwcmc48107.2020.9148446 [30] Haoxiang Wang, IoT based Clinical Sensor Data Management and Transfer using Blockchain Technol- ogy, in Journal of ISMAC, 2(3), pp. 154-159, 2020. https://doi.org/10.36548/jismac.2020.3.003 [31] Hassan Malik and Muhammad Mahtab Alam and Yannick Le Moullec and Alar Kuusik, NarrowBand-IoT Performance Analysis for Healthcare Applications, in Procedia Com- puter Science, V ol. 130, pp. 1077-1083, 2018. https://doi.org/10.1016/j.procs.2018.04.156 [32] Indra P., Manikandan M., Multilevel Tetrolet trans- form based breast cancer classifier and diagnosis system for healthcare applications. J Ambient In- tell Human Comput V ol. 12, pp. 3969–3978, 2021. https://doi.org/10.1007/s12652-020-01755-z [33] Ifrim Claudia, and Pintilie, Andreea-Mihaela and, Apostol, Elena Simona and, Dobre, Ciprian and Pop, Florin, The Art of Advanced Healthcare Ap- plications in Big Data and IoT Systems, 2017. https://doi.org/10.1007/978-3-319-45145-9_6 [34] Jaiswal K., S. Sobhanayak, A. K. Turuk, S. L. Bibhudatta, B. K. Mohanta and D. Jena, An IoT- Cloud Based Smart Healthcare Monitoring Sys- tem Using Container Based Virtual Environment in Edge Device, International Conference on Emerg- ing Trends and Innovations In Engineering And Technological Research (ICETIETR), pp. 1–7, 2018. https://doi.org/10.1109/icetietr.2018.8529141 [35] Jun Qi, Po Yang, Geyong Min, Oliver Amft, Feng Dong, Lida Xu, Advanced internet of things for per- sonalised healthcare systems: A survey, Pervasive and Mobile Computing, V ol. 41, pp. 132-149, 2017. https://doi.org/10.1016/j.pmcj.2017.06.018 [36] K. Kadarla, S. C. Sharma, T. Bhardwaj and A. Chaud- hary, A Simulation Study of Response Times in Cloud Environment for IoT-Based Healthcare Workloads, 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), pp. 678-683, 2017. https://doi.org/10.1109/mass.2017.65 [37] Kolenik T, Gams M. Increasing Mental Health Care Access with Persuasive Technology for Social Good, in IJCAI 2021 Workshop on AI for Social Good, 2021. [38] Kolenik T. and M. Gams, Persuasive Technology for Mental Health: One Step Closer to (Mental Health Care) Equality?, in IEEE Technology and Society Magazine, 40(1), pp. 80-86, March 2021, https://doi.org/10.1109/mts.2021.3056288 IoT-Enabled Remote Monitoring Techniques for. . . Informatica 46 (2022) 131–149 147 [39] Lakkis S.I., and M. Elshakankiri, IoT based emergency and operational services in medical care systems, 2017 Internet of Things Business Models, Users, and Networks, pp. 1-5, 2017. https://doi.org/10.1109/ctte.2017.8260983 [40] Leila Ismail, Huned Materwala, and Alain Hennebelle, A Scoping Review of Integrated Blockchain-Cloud (BcC) Architecture for Healthcare: Applications, Challenges and So- lutions, in Sensors, 21(3753), pp. 1–23, 2021. https://doi.org/10.3390/s21113753 [41] Manogaran G., Thota C., Lopez D., Sundarasekar R., Big Data Security Intelligence for Healthcare Industry 4.0. In: Thames L., Schaefer D. (eds) Cybersecurity for Industry 4.0. Springer Series in Advanced Manufacturing. Springer, Cham. 2017. https://doi.org/10.1007/978-3-319-50660-9_5 [42] Matthew McGrath and Evan Alexander Dewhirst, Method Of Medical Imaging Using Multiple Arrays, patent, https://lens.org/041-801-178-563-669, 2021. [43] Martin Gjoreski, Bhargavi Mahesh, Tine Kolenik, Jens UWE-Garbas, Dominik Seuss, Hristi- jan Gjoreski, Mitja Lustrek, Matjaz Gams, and Veljko Pejovic, Cognitive Load Monitor- ing With Wearables–Lessons Learned From a Machine Learning Challenge, in IEEE Ac- cess, V ol. 9, pp. 103325–103336, 2021, https://doi.org/10.1109/access.2021.3093216 [44] Min Woo Woo and JongWhi Lee and Kee- Hyun Park, A reliable IoT system for Personal Healthcare Devices, in Future Generation Com- puter Systems, V ol. 78, pp. 626-640, 2018. https://doi.org/10.1016/j.future.2017.04.004 [45] Muneeb Ejaz, Tanesh Kumar, Ivana Kovacevic, Mika Ylianttila and Erkki Harjula, Health-BlockEdge: BlockchainEdge Framework for Reliable LowLa- tency Digital Healthcare Applications, in Sensors, 21(2502), 2021. https://doi.org/10.3390/s21072502 [46] Naghshvarianjahromi N., S. Majumder, S. Ku- mar,and M. J. Deen, Natural BrainInspired Intel- ligence for Screening in Healthcare Applications, in IEEE Access, V ol. 9, pp. 67957-67973, 2021. https://doi.org/10.1109/access.2021.3077529 [47] Nidhya R., Karthik S., Smilarubavathy G., An End- to-End Secure and EnergyAware Routing Mecha- nism for IoT-Based Modern Health Care System. In: Wang J., Reddy G., Prasad V ., Reddy V . (eds) Soft Computing and Signal Processing. Advances in In- telligent Systems and Computing, vol 900, 2019. https://doi.org/10.1007/978-981-13-3600-3_35 [48] Noemi S., Pieroni A., Luca D.N., Francesca F., E- health-IoT universe: A review. International Journal on Advanced Science, Engineering and Information Technology, pp. 2328-2336, 2017. [49] Onasanya, A., Lakkis, S. and Elshakankiri, M., Implementing IoT/WSN based smart Saskatchewan Healthcare Systems, in Wire- less Networks, V ol. 25, pp. 3999–4020, 2019. https://doi.org/10.1007/s11276-018-01931-2 [50] Onasanya, A. and Maher E., Smart integrated IoT healthcare system for cancer care, in Wireless Networks, doi: https://doi.org/10.1007/s11276-018- 01932-1, 2019. [51] Pace P., G. Aloi, R. Gravina, G. Caliciuri, G. Fortino and A. Liotta, An Edge-Based Architec- ture to Support Efficient Applications for Health- care Industry 4.0, in IEEE Transactions on Indus- trial Informatics, 15(1), pp. 481-489, Jan. 2019. https://doi.org/10.1109/tii.2018.2843169 [52] Prabal Verma, Sandeep K. Sood, Cloud- centric IoT based disease diagnosis health- care framework, Journal of Parallel and Dis- tributed Computing, V ol. 116, pp. 27-38, 2018. https://doi.org/10.1016/j.jpdc.2017.11.018 [53] Priyan Malarvizhi Kumar, S. Lokesh, R. Varathara- jan, Gokulnath Chandra Babu, P. Parthasarathy, Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier, Future Generation Com- puter Systems, V ol. 86, pp. 527-534, 2018. https://doi.org/10.1016/j.future.2018.04.036 [54] Ravi Pratap Singh, Mohd Javaid, Abid Haleem, Rajiv Suman, Internet of things (IoT) applica- tions to fight against COVID19 pandemic, in Diabetes Metabolic Syndrome: Clinical Re- search Reviews, 14(4), pp. 521–524, 2020. https://doi.org/10.1016/j.dsx.2020.04.041 [55] Redowan Mahmud, Fernando Luiz Koch, and Ra- jkumar Buyya, Cloud-Fog Interoperability in IoT- enabled Healthcare Solutions, In Proceedings of the 19th International Conference on Distributed Computing and Networking (ICDCN ’18). Associ- ation for Computing Machinery, pp. 1–10, 2018. https://doi.org/10.1145/3154273.3154347 [56] Rita Zgheib, Emmanuel C., R emi B., Engineer- ing IoT Healthcare Applications: Towards a Seman- tic Data Driven Sustainable Architecture, EAI In- ternational Conference on Ambient Assisted Living Technologies based on Internet of Things, Budapest, JUNE 14–15, 2016. https://doi.org/10.1007/978-3- 319-49655-9_49 [57] Routray S.K. and S. Anand, Narrowband IoT for healthcare, 2017 International Con- ference on Information Communication and 148 Informatica 46 (2022) 131–149 S. Benedict Embedded Systems (ICICES), pp. 1-4, 2017. https://doi.org/10.1109/icices.2017.8070747 [58] Sallabi F., F. Naeem, M. Awad and K. Shuaib, Managing IoT-Based Smart Healthcare Systems Traffic with Software Defined Networks, Inter- national Symposium on Networks, Computers and Communications (ISNCC), pp. 1–6, 2018. https://doi.org/10.1109/isncc.2018.8530920 [59] Salahuddin M.A., A. Al-Fuqaha, M. Guizani, K. Shuaib and F. Sallabi, Softwarization of Inter- net of Things Infrastructure for Secure and Smart Healthcare, in Computer, 50(7), pp. 74-79, 2017. https://doi.org/10.1109/mc.2017.195 [60] Santiago Meli a, Shahabadin Nasabeh, Sergio Luj an-Mora, MoSIoT: Modeling and Simulat- ing IoT Healthcare-Monitoring Systems for Peo- ple with Disabilities, in International Journal of Environmental Research and Public Health and Cristina Cachero, 18(6357), pp. 1–25, 2021. https://doi.org/10.3390/ijerph18126357 [61] Sebbak F., Benhammadi, F. Majorityconsensus fu- sion approach for elderly IoT-based healthcare ap- plications, Ann. Telecommun, V ol. 72, pp. 157–171, 2017. https://doi.org/10.1007/s12243-016-0550-7 [62] Selvaraj S., Sundaravaradhan S., Challenges and opportunities in IoT healthcare systems: a sys- tematic review, SN Appl. Sci., 2(139), 2020. https://doi.org/10.1007/s42452-019-1925-y [63] Shajulin Benedict, Badami, R., Bhagyalakshmi, M., APM Bots: An Automated Presentation Maker for Tourists/Corporates Using NLP-Assisted Web Scrap- ing Technique, in proceedings of Advanced Network Technologies and Intelligent Computing. ANTIC 2021, Communications in Computer and Information Science, vol 1534, doi: https://doi.org/10.1007/978- 3-030-96040-7_49 [64] Shajulin Benedict, Revenue oriented air quality prediction microservices for smart cities, 2017 International Conference on Ad- vances in Computing, Communications and Informatics (ICACCI), 2017, pp. 437-442, https://doi.org/10.1109/icacci.2017.8125879 [65] S. Sharma, K. Chen and A. Sheth, Toward Practi- cal Privacy-Preserving Analytics for IoT and Cloud- Based Healthcare Systems, Remote Monitoring Tech- niques, in IEEE Internet Computing. 22(2), pp. 42-51, 2018. https://doi.org/10.1109/mic.2018.112102519 [66] Shah Nazir, Yasir Ali, Naeem Ullah, and Ivn Garcıa- Magarino, Internet of Things for Healthcare Using Effects of Mobile Computing: A Systematic Lit- erature Review, in Wireless Communications and Mobile Computing, 2019(5931315), pp.1–20. 2021. https://doi.org/10.1155/2019/5931315 [67] Sheth A., H. Y . Yip and S. Shekarpour, Extend- ing Patient-Chatbot Experience with Internet- of-Things and Background Knowledge: Case Studies with Healthcare Applications, in IEEE Intelligent Systems, 34(4), pp.24-30, 2019. https://doi.org/10.1109/mis.2019.2905748 [68] Sivadi B., Thirumaran, M., Vijender Kumar S., IoT Sensor Data Integration in Healthcare using Semantics and Machine Learning Approaches, in A Handbook of Internet of Things in Biomedi- cal and Cyber Physical System, pp.275300, 2020. https://doi.org/10.1007/978-3-030-23983-1_11 [69] Somayeh Nasiri, Farahnaz Sadoughi, Afsaneh Dehnad, Mohammad Hesam Tadayon, Hossein Ah- madi: Layered Architecture for Internet of Things- based Healthcare System: A Systematic Literature Review. in Informatica – An Int. J. of Computing and Informatics (Slovenia), 45(4), pp. 543–562, 2021. https://doi.org/10.31449/inf.v45i4.3601 [70] Subramanian, Balaji, Nathani, Karan, Rath- nasamy, Santhakumar, IoT Technology, Appli- cations and Challenges: A Contemporary Sur- vey. Wireless Personal Communications, 2019. https://doi.org/10.1007/s11277-019-06407-w [71] Tan B. et al., Wi-Fi based passive human motion sensing for in-home healthcare applications, 2015 IEEE 2nd World Forum on Internet of Things (WF- IoT), pp. 609-614, 2015. https://doi.org/10.1109/wf- iot.2015.7389123 [72] Tang F., L. Zeng, F. Wang and J. Zhou, Ad- versarial Precision Sensing with Healthcare Ap- plications, 2020 IEEE International Conference on Data Mining (ICDM), pp. 521-530, 2020. https://doi.org/10.1109/icdm50108.2020.00061 [73] Tekeste T., H. Saleh, B. Mohammad and M. Is- mail, Ultra-Low Power QRS Detection and ECG Compression Architecture for IoT Healthcare De- vices, in IEEE Transactions on Circuits and Systems I: Regular Papers, 66(2), pp. 669-679, Feb. 2019. https://doi.org/10.1109/tcsi.2018.2867746 [74] Tsu-Yang Wu, Tao Wang, Yu-Qi Lee, Weimin Zheng, Saru Kumari, and Sachin Kumar, Improved Au- thenticated Key Agreement Scheme for Fog-Driven IoT Healthcare System, in Security and Communi- cation Networks, 2021(6658041), pp. 1–16, 2021. https://doi.org/10.1155/2021/6658041 [75] Tun SYY , Madanian S, Mirza F. Internet of things (IoT) applications for elderly care: a reflective re- view, Aging Clin Exp Res, 33(4), pp.855-867, 2020. https://doi.org/10.1007/s40520-020-01545-9 IoT-Enabled Remote Monitoring Techniques for. . . Informatica 46 (2022) 131–149 149 [76] Ud Din, A. Almogren, M. Guizani and M. Zuair, A Decade of Internet of Things: Anal- ysis in the Light of Healthcare Applications, in IEEE Access, vol. 7, pp. 89967-89979, 2019. https://doi.org/10.1109/access.2019.2927082 [77] Ungurean L. and A. Brezulianu, An internet of things framework for remote monitoring of the healthcare parameters, Adv. Electr. Comput. Eng., 17(2), pp. 11– 16, 2017. https://doi.org/10.4316/aece.2017.02002 [78] Venceslau A., V . Vidal and R. Andrade, Context- driven Abnormal Semantic Event Recognition for Healthcare Applications, 2021 IEEE Inter- national Conference on Pervasive Computing and Communications Workshops and other Af- filiated Events (PerCom Workshops), pp. 434- 435, 2021. https://doi.org/10.1109/percom work- shops51409.2021.9431117 [79] Wei Li, Cheolwoo Jung and Jongtae Park, IoT Healthcare Communication System for IEEE 11073 PHD and IHE PCD-01 Integration Us- ing CoAP, KSII Trans. on Internet and Infor- mation Systems, 12(4), pp. 1396–1414, 2018. https://doi.org/10.3837/tiis.2018.04.001 [80] Wu T., J. Redout e and M. R. Yuce, A Wireless Im- plantable Sensor Design With Subcutaneous Energy Harvesting for LongTerm IoT Healthcare Applica- tions, in IEEE Access, vol. 6, pp. 35801-35808, 2018. https://doi.org/10.1109/access.2018.2851940 [81] Xainglan Peng, Research on Emotion Recognition Based on Deep Learning for Mental Health, in In- formatica – An Int. J. of Computing and Infor- matics (Slovenia), 45(1), pp. 127 – 132, 2021. https://doi.org/10.31449/inf.v45i1.3424 150 Informatica 46 (2022) 131–149 S. Benedict