The quality of sleep affects the patient’s health, along with the observation of vital life signs such as body temperature and sweat in sleep, is essential in the monitoring of sleep as well as clinical diagnosis. However, traditional methods in recording physiological change amidst sleep is difficult without being intrusive. The smart pillow is developed to provide a relatively easy way to observe one’s sleep condition, employing temperature and humidity sensors by implanting them inside the pillow in strategic positions. With the patient’s head on the pillow, the roles of sensors are identified as main, auxiliary or environmental temperature, based on the differences of value from three temperature sensors, thus the pattern of sleep can be extracted by statistical analysis, and the body temperature is inferred by a specially designed Fuzzy Logic System if the head-on position is stable for more than 15 min. Night sweat is reported on data from the humidity sensor. Therefore, a cloud-based health-sensing system is built in the smart pillow to collect and analyze data. Experiments from various individuals prove that statistical and inferred results reflect normal and abnormal conditions of sleep accurately. The daily sleeping information of patients from the pillow is helpful in the decision-making of diagnoses and treatment, and users can change their habits of sleep gradually by observing the data with their health professional.

The advancement of Internet of Things (IoT) has made it practical to discover, localize and pinpoint smart sensing devices based on the situational context, relevancy,and characteristics to query data intelligently, or conduct actions. Furthermore, the development of large-scale applications must deal with data collection and data sensing from a massive number of ubiquitous components, ultimately converging into 5G mobile networking. Additionally, IoT involves managing the expectations of Big Data sourced from many heterogeneous sources. This paper provides an overview of biomimetic methodologies, which represent a viable solution for large-scale data delivery through the aggregation of information with large-scale IoT technologies.

The outbreak of novel coronavirus (COVID-19) resulted in the clinical decision that reduced direct contact is optimal, especially for senior citizens residing in nursing homes. A smart pillow adapted for the Remote Healthcare System is presented in this paper, whose core is a Bluetooth (BT) host equipped with temperature and pressure sensors. The measurement of Core Body Temperature (CBT) from the perspective of heat transfer is first analyzed, with two proven effective methods introduced—classical Zero-Heat-Flux (ZHF) and Dual-Heat-Flux (DHF)—then finally the similarities between the Smart Pillow and ZHF are demonstrated. A pressure pad is inserted inside the pillow to detect occupancy and the specific position of the head on the pillow that meets clinical diagnostic needs. Furthermore, a real-time proactive monitoring mode is enabled for urgent warnings, which forces the pillow to keep detecting and reporting data in a defined time duration but results in rapid battery drain of the pillow. In this way, the system can detect the CBT and in-bed situation of the inhabitant without being physically present to determine critical measurements. Utility of this system can be extended to elderly people living alone in regional or remote areas, such that medical help can be dispatched as soon as possible in case of medical emergency.

A significant portion of the Hospital Information Systems currently consists of various individual legacy applications that have to be integrated, to deliver a more unified solution. The performance, reliability and other factors of these applications can alter the performance, reliability and other characteristics of integrated Solution, the Smart Hospital Management System (SHS). The actual evaluation of these parameters of these applications is outside the scope of this document. The SHS being an infrastructure component relies heavily on the actual resources made available to it for its proper functioning, operation and maintenance. This article aims to deliver an approach in architecting solutions which can be utilised as framework to address common issues in integration of enterprise level solutions. The methodologies discussed in TOGAF version 9 are utilised to demonstrate the feasibility of proposed solution. This paper introduces the problem space/scenarios, constraints, requirements, enablers, risks, sample legacy application architectures and proposed integration solution presented with TOGAF components. The growing number of waiting lists, rising pressure on medical professionals and accountability for medical negligence are only part of the motivation to take initiative towards holds a core model integration strategy in various legacy infrastructure systems.

In the proposed work we present a combination of two paradigms: Wireless Sensor Networks (WSN) and Computer Vision applied for Motion Analysis. In this work the Computer Vision provides high-level behavioural monitoring and analysis, whereas Wireless Sensors capture detailed parameters of a moving object. Fusion of sensory information received from both types of sensors provides micro-level and macro-level details. These combined details can be used in various application areas. In considered applications, one of the areas can be Robotics. In this case this strategy can be used to monitor health of robots under certain actions and situations. Another important application domain is health care and rehabilitation of injured persons. In this application, movement of an injured body portion is measured after its treatment. Apart from the analysis of motion we also propose optimized movement advice to patients. Optimum motion advice is very useful in case of sports injury to recover strength and performance. In this paper we produce experimental work performed by simulating different movements of hands and legs in free space. The experimental simulation provides a broad range of data on motion analysis with visualization. The third area of application that is explored is elderly patient condition monitoring and motion analysis for health monitoring.

The application of an software agent-based computational technique that implements Extended Kohonen Maps (EKMs) for the management of Sensor-Actuator networks (SANETs) in health-care facilities. The agent-based model incorporates the BDI (Belief-Desire-Intention) Agent paradigms by Georgeff et al. EKMs perform the quantitative analysis of an algorithmic artificial neural network process by using an indirect-mapping EKM to self-organize. Current results show a combinatorial approach to optimization with EKMs provides an improvement in event trajectory estimation compared to standalone cooperative EKM processes to allow responsive event detection for patient monitoring scenarios. This will allow healthcare professionals to focus less on administrative tasks, and more on improving patient needs, particularly with people who are in need for dedicated care and round-the-clock monitoring.