Health monitoring using biomedical sensors has witnessed significant attention in recent past due to the evolution of a new research area in sensor network known as Wireless Body Area Networks (WBANs). In WBANs, a number of implantable, wearable, and off-body biomedical sensors are utilized to monitor various vital signs of patient’s body for early detection, and medication of grave diseases. In literature, a number of Medium Access Control (MAC) protocols for WBANs have been suggested for addressing the unique challenges related to reliability, delay, collision and energy in the new research area. The design of MAC protocols is based on multiple access techniques. Understanding the basis of MAC protocol designs for identifying their design objectives in broader perspective, is a quite challenging task. In this context, this paper qualitatively reviews MAC protocols for WBANs. Firstly, 802.15.4 and 802.15.6 based MAC Superframe structures are investigated focusing on design objectives. Secondly, different multiple access techniques such as TDMA, CSMA/CA, Slotted Aloha and Hybrid are explored in terms of design goals. Thirdly, a two-layered taxonomy is presented for MAC protocols. First layer classification is based on multiple access techniques, whereas second layer classification is based on design objectives and characteristics of MAC protocols. Critical and qualitative analysis is carried out for each considered MAC protocol. Comparative study of different MAC protocols is also performed. Finally, some open research challenges in the area are identified with initial research directions.

Radiative communication using electro-magnetic (EM) fields amongst the wearable and implantable devices act as the backbone for information exchange around a human body, thereby enabling prime applications in the fields of connected healthcare, electroceuticals, ...

Imagine a world where you never have to worry about your assets running out of power. UWB asset tracking can make this dream a reality! Self-powered, batteryless technology will be the key for widespread use of UWB - spanning a wide range of IoT devices - tags, product packaging, labels. In this article, we will discuss how UWB technology can be used to track assets without the need for a battery.

The invention discloses a kind of self-positioning intelligent domestic energy information sockets merged based on UWB with PLC, belong to electric power internet of things field, the self-positioning intelligent domestic energy information socket includes shell, it include integrated treatment simplified element module in shell, power line carrier wave information processing module, ultra-wideband impulse radio message processing module, multidirectional antenna element, AC power source input interface, AC power source output interface, DC power supply unit and data information interface, the self-positioning intelligent domestic energy information socket greatly improves current home intelligent electric power utilization system in communication reliability, network visibility, short slab in terms of system availability and Technical Economy, it effectively solves low pressure level electric power Internet of Things and promotes and applies difficulties, it is obviously improved the utilization efficiency of energy information associated terminal and data, efficiency of transmission, management effect Rate and reliability.

COPD is a progressive disease that may lead to death if not diagnosed and treated at an early stage. The examination of vital signs such as respiration rate is a promising approach for the detection of COPD. However, simultaneous consideration of the demographic and medical characteristics of patients is very important for better results. The objective of this research is to investigate the capability of UWB radar as a non-invasive approach to discriminate COPD patients from healthy subjects. The non-invasive approach is beneficial in pandemics such as the ongoing COVID-19 pandemic, where a safe distance between people needs to be maintained. The raw data are collected in a real environment (a hospital) non-invasively from a distance of 1.5 m. Respiration data are then extracted from the collected raw data using signal processing techniques. It was observed that the respiration rate of COPD patients alone is not enough for COPD patient detection. However, incorporating additional features such as age, gender, and smoking history with the respiration rate lead to robust performance. Different machine-learning classifiers, including Naïve Bayes, support vector machine, random forest, k nearest neighbor (KNN), Adaboost, and two deep-learning models—a convolutional neural network and a long short-term memory (LSTM) network—were utilized for COPD detection. Experimental results indicate that LSTM outperforms all employed models and obtained 93% accuracy. Performance comparison with existing studies corroborates the superior performance of the proposed approach.

Ultra-Wideband (UWB) technology, with its excellent positioning and sensing capabilities, is exhibiting clear signs of becoming a principal element in the transformation of connectivity experiences across all aspects of life

Combining game technology, data science and neuroscience to surface precise signals and disrupt the way companies recruit, retain and develop talent.