update the individual patient record but also act as a source for AI-driven healthcare analytics at large. Wearable healthcare devices have seen a rise in recent years owing to the popularity of healthier living. Automated at home healthcare management and monitoring chronic conditions have been the other key drivers in the surge of IoT. In a way, it aids practitioners in making well-informed decisions resulting in quicker diagnosis for effective treatment. Thus, increasing innovations in wearable devices like insulin monitors to portable blood pressure monitors, and it is ever-expanding. Advances in healthcare have made it feasible for an emerging field like Remote Healthcare Management [26]. Key attributes to the possible success of Remote Healthcare Management are as follows.
1.5.2 Creating Efficient Communication Framework for Remote Healthcare Management
Patients to rehabilitate at home are a common condition post-treatments or part of a few during which there is a possibility of relapse due to inadequate care. In today’s scenario, patients are provided with AI-powered wearable technology that enables remote monitoring once they have been discharged or in cases where equipment supports is required for treating them. It brings about significant benefits like early warnings of deterioration in patients to allow targeted interventions, also minimize administrative ordeal of hospitalization and readmission. A quick response to any fluctuation in health conditions is made feasible with IoT. Therefore, remote monitoring services become dependable round the clock.
Sensors used in IoT devices are linked together yet separately identified over a communication infrastructure [27]. IoT has three layers of communication: sensors that have the physical interface. This system provides connectivity and server where all the sensory data is stored and processed, as shown in Figure 1.8. The first two layers are simple and can be very cost-effective and predominately setup at the patient’s end. The third layer is traditionally a cloud where an array of services is provided with the help of AI algorithms performance big data analytics. The cloud layer is interconnected with local layers through the multi-hop network, making it susceptible to challenges of reliability, availability, and soundness. With varying latency and bandwidth, traditional cloud computing architecture needs to be reviewed as the patient is the end-user, and in emergencies, establishing connections could have adverse effects. In remote healthcare monitoring, there is always an increase in the number of connected devices, and with it comes the sensory data, which could cause potential overload on the communication infrastructure. Currently, many types of research are underway to make this architecture effective, enabling layer two to pre-process using computational capabilities and close loop architecture. Allowing this layer creates a system where essential and critical services can be locally controlled and, in turn, reducing the load on IoT communication infrastructure through effective task and resource management.
Figure 1.8 IoT architecture for healthcare.
The overall objective was to minimize the effects of varying latency and bandwidth between gateways and servers here; it is the traditional cloud computing. It is tested with hierarchical computing architecture where existing machine learning methods can help in a fog-enabled IoT system. At a local level, it explores the feasibility of delivering adaptive transmission of data inside a closed-loop environment [27].
1.5.3 Developing Autonomous Capability is Key for Remote Healthcare Management
Remote monitoring is a robust model of healthcare. The advent of wearable devices aided in the smart healthcare application, in turn, enabling quicker clinical diagnosis and ease of disease prediction. The host of sensors is deployed in healthcare, uniquely placed inside the body either as implants or sensing devices over the physical body. Many of these can also communicate with small handheld devices such as smartphones or digital assistants. Dynamic and Interoperable Communication Framework (DICF) is primarily designed [28] to improve efficiency and enhance the decision-making capabilities of these wearable sensors. It optimizes various constraints of a sensor, namely, lifetime, storage capacity, handling multiple communication channels, and decision-making. The radio transmitter is used by sensors to interact with Aggregator Mobile Device (AMD); it can be a mobile platform. Information received is distinguished by the physiological source or based on sensor types. Periodic sensing for the collection of data varies with the kind of sensor and the human body. This periodic sensing is called a session. A session is, in turn, capable of sensing both periodic and event while monitoring is in progress. Pattern and range are clearly defined for transmitting data from AMD to service provider or clinic. So, when their data which does not fall within this range, an abnormality detected. AMD quickly generates an emergency notification and prioritizes transmission. In such events of AMD transmits completed sensed data to the clinic or the service provider without requesting the permission of end-users or patients.
Before the trigger of emergency is actuated, local machine learning algorithm checks for early diagnosis and first aid suggestions. Machine learning starts to establish causes and conditions using sensory data with varied conditions of patients and different patient data. Data collected and analyses at a local level significantly increase as different learning algorithms are applied to the results for further analysis. AMD performs the role of the hub by collecting data and relaying it to the clinic of the service provider, data accumulation at periodic intervals, and data gathering post applying algorithms. With the capacity of AMD, there is a significant improvement in processing capability at the local level. Data is stored as an Electronic Medical Record (EMR), which is interconnected with the notification system and monitoring unit. This enables a process of classification and decisions based on regression models. This decision-making model improves the performances of wearable devices in terms of event detection and emergency interval identification in a remote monitoring system. DICF aids in building a robust sensor dependent personal healthcare system. It also facilitates data collection, event detection, analysis, and communication with interconnected wearable sensors. AI-based remote healthcare system is shown in Figure 1.9.
Figure 1.9 AI-based remote healthcare management.
1.5.4 Enabling Data Privacy and Security in the Field of Remote Healthcare Management
Internet of Medical Things (IoM) has gained specialization in recent years with growing applications. Both embedded and wearable sensors gather comprehensive information about patients. It is shared with medical professionals for diagnosis. The sensitive nature of this data gives rise to protection and privacy, which is currently a significant challenge of the IoM. It uses anonymity-based authentication to mitigate privacy issues. To ensure the session is secure, upon mutual authentication, both medical professionals and medical sensors utilize private session keys. Research in this area of authenticity and privacy of data is still at the nascent stage. Currently, available authentication features are not suitable to achieve the privacy goals in terms of its features. At this juncture, there is not a credible and efficient authentication program in this segment. One of the key issues in achieving the goal is two-factor security in the event of loss or tampered smart card. Research is deplored to investigate the adversarial model, which is expected to mitigate various redundancies and ambiguities. This paper explores a methodology with 12 independent criteria
