RF Sensing Technologies for Assisted Daily Living in Healthcare: A Comprehensive Review

The aim of radio-frequency (RF) sensing for assisted living is to deliver automatic support and monitoring for older people in their homes, impaired patients living independently, individuals in need of continuous support, and people suffering from chronic diseases that require them to stay in care-homes or at hospitals. RF sensing technologies have the potential to improve the quality of living of elderly people or disabled individuals in need of timely assistance. This paper provides a comprehensive review on three of the most innovative RF sensing technologies for activities of daily living in healthcare sector (namely active radar, passive radar, and wireless channel information and RFID sensing) and presents some of the open challenges that need to be addressed

Human activity classification using micro-Doppler signatures and ranging techniques

PhD ThesisHuman activity recognition is emerging as a very import research area due to its potential applications in surveillance, assisted living, and military operations. Various sensors including accelerometers, RFID, and cameras, have been applied to achieve automatic human activity recognition. Wearable sensor-based techniques have been well explored. However, some studies have shown that many users are more disinclined to use wearable sensors and also may forget to carry them. Consequently, research in this area started to apply contactless sensing techniques to achieve human activity recognition unobtrusively. In this research, two methods were investigated for human activity recognition, one method is radar-based and the other is using LiDAR (Light Detection and Ranging). Compared to other techniques, Doppler radar and LiDAR have several advantages including all-weather and all-day capabilities, non-contact and nonintrusive features. Doppler radar also has strong penetration to walls, clothes, trees, etc. LiDAR can capture accurate (centimetre-level) locations of targets in real-time. These characteristics make methods based on Doppler radar and LiDAR superior to other techniques. Firstly, this research measured micro-Doppler signatures of different human activities indoors and outdoors using Doppler radars. Micro-Doppler signatures are presented in the frequency domain to reflect different frequency shifts resulted from different components of a moving target. One of the major differences of this research in relation to other relevant research is that a simple pulsed radar system of very low-power was used. The outdoor experiments were performed in places of heavy clutter (grass, trees, uneven terrains), and confusers including animals and drones, were also considered in the experiments. Novel usages of machine learning techniques were implemented to perform subject classification, human activity classification, people counting, and coarse-grained localisation by classifying the micro-Doppler signatures. For the feature extraction of the micro-Doppler signatures, this research proposed the use of a two-directional twodimensional principal component analysis (2D2PCA). The results show that by applying 2D2PCA, the accuracy results of Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) classifiers were greatly improved. A Convolutional Neural Network (CNN) was built for the target classifications of type, number, activity, and coarse localisation. The CNN model obtained very high classification accuracies (97% to 100%) for the outdoor experiments, which were superior to the results obtained by SVM and kNN. The indoor experiments measured several daily activities with the focus on dietary activities (eating and drinking). An overall classification rate of 92.8% was obtained in activity recognition in a kitchen scenario using the CNN. Most importantly, in nearly real-time, the proposed approach successfully recognized human activities in more than 89% of the time. This research also investigated the effects on the classification performance of the frame length of the sliding window, the angle of the direction of movement, and the number of radars used; providing valuable guidelines for machine learning modeling and experimental setup of micro-Doppler based research and applications. Secondly, this research used a two dimensional (2D) LiDAR to perform human activity detection indoors. LiDAR is a popular surveying method that has been widely used in localisation, navigation, and mapping. This research proposed the use of a 2D LiDAR to perform multiple people activity recognition by classifying their trajectories. Points collected by the LiDAR were clustered and classified into human and non-human classes. For the human class, the Kalman filter was used to track their trajectories, and the trajectories were further segmented and labelled with their corresponding activities. Spatial transformation was used for trajectory augmentation in order to overcome the problem of unbalanced classes and boost the performance of human activity recognition. Finally, a Long Short-term Memory (LSTM) network and a (Temporal Convolutional Network) TCN was built to classify the trajectory samples into fifteen activity classes. The TCN achieved the best result of 99.49% overall accuracy. In comparison, the proposed TCN slightly outperforms the LSTM. Both of them outperform hidden Markov Model (HMM), dynamic time warping (DTW), and SVM with a wide margin

Convergent communication, sensing and localization in 6g systems: An overview of technologies, opportunities and challenges

Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust

Recent Advances in Motion Analysis

The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application

1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

Beam scanning by liquid-crystal biasing in a modified SIW structure

A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

Biosensors for Diagnosis and Monitoring

Biosensor technologies have received a great amount of interest in recent decades, and this has especially been the case in recent years due to the health alert caused by the COVID-19 pandemic. The sensor platform market has grown in recent decades, and the COVID-19 outbreak has led to an increase in the demand for home diagnostics and point-of-care systems. With the evolution of biosensor technology towards portable platforms with a lower cost on-site analysis and a rapid selective and sensitive response, a larger market has opened up for this technology. The evolution of biosensor systems has the opportunity to change classic analysis towards real-time and in situ detection systems, with platforms such as point-of-care and wearables as well as implantable sensors to decentralize chemical and biological analysis, thus reducing industrial and medical costs. This book is dedicated to all the research related to biosensor technologies. Reviews, perspective articles, and research articles in different biosensing areas such as wearable sensors, point-of-care platforms, and pathogen detection for biomedical applications as well as environmental monitoring will introduce the reader to these relevant topics. This book is aimed at scientists and professionals working in the field of biosensors and also provides essential knowledge for students who want to enter the field

IoT Applications Computing

The evolution of emerging and innovative technologies based on Industry 4.0 concepts are transforming society and industry into a fully digitized and networked globe. Sensing, communications, and computing embedded with ambient intelligence are at the heart of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Industry 4.0 technologies with expanding applications in manufacturing, transportation, health, building automation, agriculture, and the environment. It is expected that the emerging technology clusters of ambient intelligence computing will not only transform modern industry but also advance societal health and wellness, as well as and make the environment more sustainable. This book uses an interdisciplinary approach to explain the complex issue of scientific and technological innovations largely based on intelligent computing

Improved Detection of Human Respiration Using Data Fusion Basedon a Multistatic UWB Radar

This paper investigated the feasibility for improved detection of human respiration using data fusion based on a multistatic ultra-wideband (UWB) radar. UWB-radar-based respiration detection is an emerging technology that has great promise in practice. It can be applied to remotely sense the presence of a human target for through-wall surveillance, post-earthquake search and rescue, etc. In these applications, a human target’s position and posture are not known a priori. Uncertainty of the two factors results in a body orientation issue of UWB radar, namely the human target’s thorax is not always facing the radar. Thus, the radial component of the thorax motion due to respiration decreases and the respiratory motion response contained in UWB radar echoes is too weak to be detected. To cope with the issue, this paper used multisensory information provided by the multistatic UWB radar, which took the form of impulse radios and comprised one transmitting and four separated receiving antennas. An adaptive Kalman filtering algorithm was then designed to fuse the UWB echo data from all the receiving channels to detect the respiratory-motion response contained in those data. In the experiment, a volunteer’s respiration was correctly detected when he curled upon a camp bed behind a brick wall. Under the same scenario, the volunteer’s respiration was detected based on the radar’s single transmitting-receiving channels without data fusion using conventional algorithm, such as adaptive line enhancer and single-channel Kalman filtering. Moreover, performance of the data fusion algorithm was experimentally investigated with different channel combinations and antenna deployments. The experimental results show that the body orientation issue for human respiration detection via UWB radar can be dealt well with the multistatic UWB radar and the Kalman-filter-based data fusion, which can be applied to improve performance of UWB radar in real applications