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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Compact and Flexible Microwave Devices will equip you with essential insights into the transformative potential of RF and microwave technologies, crucial for driving innovation in communication systems, wearables, and advanced industries.
Microwave devices are an integral part of modern-day communication technology, present in everything from wireless internet connections to self-driving cars. This ever-evolving technology has the potential to revolutionize wearables, sensors, and 5G/6G networks. This volume explores the design and analysis of RF and microwave devices, including types of practical antenna design, antenna arrays, metasurfaces, and device-to-device communications. The innovative potential of microwave devices has the power to revolutionize everyday human life, providing more accurate and intuitive sensing to improve quality of life. Compact and Flexible Microwave Devices is a comprehensive guide to these ground-breaking technologies that introduces cutting-edge applications for integration with next-generation communication systems, the healthcare industry and Industry and Web 4.0.
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Veröffentlichungsjahr: 2025
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 A Systematic Survey on Wearable Biomedical Sensors Using Flexible Microwaves Devices
1.1 Introduction
1.2 Literature Survey
1.3 Procedure and Working
1.4 Next-Generation Wearables: A Strategic Plan for the Future
1.5 Conclusion
References
2 Metamaterials in 5G and the Path towards 6G: Enabling Advanced Wireless Communication
2.1 Introduction
2.2 Basics of Metamaterials
2.3 Metamaterials in 5G Communication
2.4 Towards 6G: Future Challenges and Metamaterials Integration
2.5 Case Studies and Applications
2.6 Future Directions and Research Challenges
2.7 Conclusion
References
3 Optimization of Time-Modulated Circular Antenna Array for MIMO Systems through Evolutionary Algorithms
3.1 Introduction
3.2 Design Equations
3.3 Evolutionary Optimization Method Used
3.4 Cost Function Formulation
3.5 Computational Result
3.6 The Convergence Curves of DE, PSO, and NPSO
3.7 Conclusions
Acknowledgement
References
4 Fractal Antenna MIMO Arrays: A Promising Multiband Solution for Modern 5G Wireless Automotive Applications
4.1 Introduction
4.2 Various Forms of Antenna Array
4.3 Feed Networks for Antenna Array
4.4 Fractal Geometries
4.5 Fractal Antenna Arrays
4.6 Recent Trends in the Fractal Antenna Arrays
4.7 Conclusion
References
5 Multiband Fractal Antenna: A Novel Miniaturization Technique
5.1 Introduction
5.2 Fractal Designs
5.3 Iterative Function System
5.4 Advantages and Limitations of Fractal Geometry in Antennas
5.5 Compact Printed Multiband Fractal Antenna: Novel Design Approach
5.6 Conclusion
References
6 Applications of Metasurfaces for Modern Planar Antenna Design
6.1 Metasurfaces – Introduction and Characteristics Relationship
6.2 Characteristics Mode Analysis (CMA)
6.3 Realization and Evaluations of MS-Based Antenna Models
6.4 Frequency Selective Surfaces and Its Types
6.5 Relationship Between Mutual Coupling Reduction and FSS Superstrate in CP-MIMO Antennas
6.6 Conclusion
References
7 A Review on Electromagnetic Metamaterial Absorbers and Its Application
7.1 Introduction
7.2 Types of Electromagnetic Metamaterial Wave Absorbers
7.3 Type of Metamaterial
7.4 The Metamaterial (MTM) Absorbers Theory
7.5 Different Approaches for Analysis of Microwave Absorption
7.6 Narrowband Metamaterial Absorbers
7.7 Electromagnetic Metamaterial Design Consideration
7.8 Applications of Electromagnetic Metamaterial Absorbers Are Classified on Frequency Range
7.9 Applications and Research Area of Electromagnetic Metamaterial Absorber
7.10 Advancements in Metamaterials in Recent Times
7.11 Conclusion and Prospects for the Future
References
8 RF-Based Wearable PPG Sensor for Real-Time Cardiovascular Health Monitoring
8.1 Introduction
8.2 Motivation
8.3 The Fundamental of Photoplethysmography
8.4 RF-Based Wearable Sensors and Applications in Healthcare Monitoring
8.5 Methodology
8.6 Experimental Setup of EH Module Integrated with PPG Sensor
8.7 Data Analysis
8.8 Impact of Wearable Sensors in the Market
8.9 Conclusion and Future Scope
References
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 3
Table 3.1 Control parameter values for 12-elements antenna array.
Table 3.2 Optimized value for
N
=12 elements using DE, PSO, and NPSO algorithm.
Chapter 4
Table 4.1 Element distribution for fractal linear arrays for even elements.
Table 4.2 Detailed analysis of recent research papers regarding fractal antenn...
Chapter 5
Table 5.1 Features of fractal antennas.
Table 5.2 Result summary after simulation.
Table 5.3 Result summary after simulation.
Table 5.4 Comparison between simulated and measured results.
Chapter 6
Table 6.1 Literature survey on recently reported metasurface-based 5G antennas...
Chapter 7
Table 7.1 Popular communication bands and their respective applications.
Table 7.2 Physical material categorized.
Chapter 8
Table 8.1 Comparison of distinct wireless technologies.
Table 8.2 RF-based wearable sensor in healthcare monitoring.
List of Illustrations
Chapter 1
Figure 1.1 Wearable devices market in 2023-2030 in US dollars.
Figure 1.2 Localization and tracking system using wearable sensors [51].
Figure 1.3 Diagrammatic illustration of the fundamental workings of a deep lea...
Figure 1.4 Systemic illustration of wearable sensor devices used in e-health.
Figure 1.5 Biosensors detecting sweat concentration.
Figure 1.6 Diagrammatic representation of a closed-loop neuronal recording and...
Chapter 2
Figure 2.1 Beamforming null steering.
Figure 2.2 Some of the issues associated with the 6G wireless communication sy...
Chapter 3
Figure 3.1 Representation of a generalized n-elements asymmetric circular ante...
Figure 3.2 Flow chart of DE technique.
Figure 3.3 Flow chart of NPSO technique.
Figure 3.4 Radiation pattern of 12-elements TMASCAA using DE.
Figure 3.5 Radiation pattern of 12-elements TMASCAA using PSO.
Figure 3.6 Radiation pattern of 12-elements TMASCAA using NPSO.
Figure 3.7 Switch-ON time sequence for 12-element TMASCAA using evolutionary a...
Figure 3.8 Progressive phase delay for 12-element TMASCAA using evolutionary a...
Figure 3.9 Convergence curve for 12-elements TMASCAA.
Chapter 4
Figure 4.1 Yagi-Uda antenna array configuration [2].
Figure 4.2 Log periodic dipole array [2].
Figure 4.3 Microstrip patch array [2].
Figure 4.4 Array of two-point source [6].
Figure 4.5 Broadside array arrangement [6].
Figure 4.6 Arrangement of antennas in end fire configuration.
Figure 4.7 (a) Series fed. (b) Corporate fed.
Figure 4.8 Koch curve fractal for various iteration.
Figure 4.9 Koch snowflake fractal structure.
Figure 4.10 Sierpinski gasket fractal antenna [7].
Figure 4.11 Sierpinski carpet configurations up to 2nd iteration [14].
Figure 4.12 Minkowski curve up to 2nd iteration [11].
Figure 4.13 Linear array of uniformly spaced isotropic sources [14].
Chapter 5
Figure 5.1 Sierpinski gasket with subsequent stages showing initiator and gene...
Figure 5.2 Classes of fractals.
Figure 5.3 Structure of proposed antenna.
Figure 5.4 Iterative geometry. (a) Main radiator, (b) Main radiator with 4 sub...
Figure 5.5 S
11
parameter of main patch.
Figure 5.6 S
11
parameter of main patch with reduced ground structure.
Figure 5.7 S
11
parameter vs frequency: I iteration.
Figure 5.8 S
11
parameter vs frequency plot: II iteration.
Figure 5.9 S
11
parameter vs frequency plot: III iteration.
Figure 5.10 VSWR curve.
Figure 5.11 3D polar plot for directivity.
Figure 5.12 3D polar plot for gain.
Figure 5.13 Radiation pattern curve.
Figure 5.14 Beamwidth curve.
Figure 5.15 Surface current distribution.
Figure 5.16 S
11
parameter vs frequency plot.
Figure 5.17 VSWR vs frequency plot.
Figure 5.18 3D polar plot for directivity.
Figure 5.19 3D polar plot for gain.
Figure 5.20 Radiation pattern curve.
Figure 5.21 Beamwidth curve.
Figure 5.22 Return loss comparison between simulated and tested results.
Figure 5.23 VSWR comparison between simulated and tested results.
Chapter 6
Figure 6.1 Classification of materials based on their properties.
Figure 6.2 (a) Wire medium layout (b) frequency behaviour of the effective die...
Figure 6.3 (a) SRR (b) frequency behaviour of the effective dielectric medium.
Figure 6.4 First fabricated metamaterial designs composed of SRRs and rods/str...
Figure 6.5 Invisible cloaking technique.
Figure 6.6 CMA steps to design a simple rectangular patch.
Figure 6.7 Impedance bandwidth enhancement techniques based on CM.
Figure 6.8 Circular polarization CM-based antenna designs.
Figure 6.9 Multi-mode multi-port CM-based antenna designs.
Figure 6.10 CM-based pattern optimization and synthesis techniques.
Figure 6.11 CM-based scattering control.
Figure 6.12 CMA to control EM coupling.
Figure 6.13 Metasurface-based antenna: (a) perspective view, (b) metasurface, ...
Figure 6.14 Frequency selective surfaces operating principle.
Figure 6.15 Equivalent circuits of conventional FSS.
Figure 6.16 Grouping of basic geometries of FSS.
Figure 6.17 Different types of available convoluted FSS (a) meandered cross di...
Figure 6.18 Different types of fractal-shaped FSSs.
Figure 6.19 (i) MIMO antenna with FSS layers (a) and (b), (c) feed, and (d) pa...
Figure 6.20 Schematic of the SO FSS wall loaded 2-element and 4-element MIMO a...
Chapter 7
Figure 7.1 Shows the current scenario’s electromagnetic absorber.
Figure 7.2 Classification of material based on permittivity(ε) and permeabilit...
Figure 7.3 Common designs for impedance matching resonant absorbers.
Figure 7.4 Comparison of refraction in a left-handed metamaterial with an ordi...
Figure 7.5 Impedance matching in metamaterial absorber.
Figure 7.6 Working of electromagnetic metamaterial absorber.
Figure 7.7 The image displays a planar MTM absorber, with (a) representing the...
Figure 7.8 Displays an image showing a saw-toothed MTM absorber, accompanied b...
Figure 7.9 Flow chart showing metamaterial absorber simulation step.
Figure 7.10 Application metamaterial absorber with respect to different freque...
Figure 7.11 Application of electromagnetic metamaterial absorber.
Chapter 8
Figure 8.1 System architecture of RF-based wearable sensor for real-time monit...
Figure 8.2 PPG-based wearable sensor: Examples of wrist-finger-ear worn device...
Figure 8.3 The representation of the PPG waveform generated by the absorption ...
Figure 8.4 Oxy and Deoxy Hemoglobin Absorption for Red and IR Wavelength of Li...
Figure 8.5 Overview of energy harvesting sources and their applications [46].
Figure 8.6 Block diagram of a complete passive sensing system.
Figure 8.7 Antenna design and performance analysis. (a) Geometry of antenna de...
Figure 8.8 RF circuit performance analysis. (a) Reflection co-efficient. (b) O...
Figure 8.9 Flowchart of energy harvesting module integrated with PPG sensor.
Figure 8.10 PPG sensor and signal conditioning circuits. (a) LED Driver Circui...
Figure 8.11 Measurement of real-time PPG signal using the proposed RF-based PP...
Figure 8.12 PPG signals obtained from the developed sensor.
Guide
Cover Page
Series Page
Title Page
Copyright Page
Preface
Table of Contents
Begin Reading
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Compact and Flexible Microwave Devices
Edited by
Dilip Kumar Choudhary
Indrasen Singh
Manoj Kumar Singh
and
Amit Kumar Jain
This edition first published 2025 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 9781394275557
Cover image: Generated with AI using Adobe FireflyCover design by Russell Richardson
Preface
In recent years, the rapid evolution of wireless communication systems, wearable technologies, and miniaturized electronic platforms has spurred an increasing demand for microwave devices that are not only compact and efficient but also mechanically flexible and adaptable. The convergence of these design requirements presents exciting challenges and opportunities in the field of microwave engineering. This edited volume, Compact and Flexible Microwave Devices, brings together recent advances and cutting-edge research that address these pressing needs.
The book aims to serve as a comprehensive reference for researchers, engineers, and graduate students working at the intersection of microwave technology, materials science, and applied electromagnetics. Contributions from leading experts across academia and industry cover a broad spectrum of topics, including novel materials and fabrication techniques, reconfigurable and metasurface-based devices, wearable and conformal antennas, flexible RF components, and system-level applications.
Each chapter has been carefully selected to offer both theoretical insights and practical design strategies, enabling readers to understand the underlying principles while also gaining hands-on knowledge of modern fabrication and characterization techniques. Special emphasis has been placed on the integration of compact and flexible devices into real-world applications, such as Internet of Things (IoT), biomedical diagnostics, remote sensing, and next-generation communication systems.
We extend our deepest gratitude to the contributors for their valuable chapters and to the reviewers for their insightful feedback. This volume is the result of their collective expertise, effort, and dedication. We also thank the editorial and production teams for their support throughout the publishing process.
It is our hope that this book will inspire further innovation and interdisciplinary collaboration, ultimately contributing to the advancement of flexible and miniaturized microwave technologies that shape the future of communication and sensing systems.
Dilip Kumar Choudhary
Vellore Institute of Technology, Vellore, India
1A Systematic Survey on Wearable Biomedical Sensors Using Flexible Microwaves Devices
Warisha Fatima1, Shailendra Kumar2, Mohd Javed Khan2* and Indrasen Singh3
1Department of Physics, Integral University, Lucknow, India
2Department of ECE, Integral University, Lucknow, India
3School of Electronics Engineering VIT, Vellore, TN, India
Abstract
Microwave devices play a vital role in medical sensors and imaging technology, offering non-invasive monitoring and diagnosis. The Internet of Things (IoT) framework is pivotal in healthcare, with radio frequency and microwave technologies enabling wireless sensory data transmission. Wireless power transmission is crucial for wearable, portable, and flexible sensors without bulky batteries. Electronic health (e-health) leverages digital and communication technologies to enhance healthcare delivery and outcomes. Wearable biomedical sensors revolutionize health maintenance, collecting physiological data for personalized insights, early detection, and continuous monitoring. These sensors encompass accelerometers, gyroscopes, heart rate monitors, skin temperature, electrocardiograms (ECG), and blood oxygen levels. They empower users with real-time health information, including blood glucose levels and heart rate patterns, benefiting those with cardiovascular diseases and the elderly through remote monitoring systems. Flexible materials like fabrics and polymers enhance sensor adaptability to body contours. Microwave devices enable non-invasive tissue characterization and tumor monitoring, offering insights into tumor features and dielectric properties. Ongoing research explores novel applications and technological advancements to enhance the functionality and adaptability of microwave-based wearable gadgets in healthcare and beyond.
Keywords: Wearable sensors, real-time monitoring, microwave devices
1.1 Introduction
Interest in wearable health monitoring devices has grown as a result of the quick development in wireless communications and physiological sensing technologies [1, 2]. Wearable sensors can be used for monitoring and diagnostic purposes. They are currently capable of motion detection in addition to physical and metabolic sensing. The extent of the issues that these advances could potentially aid with is difficult to overemphasize. Many people with neurological, cardiovascular, and pulmonary conditions, including seizures, hypertension, dysrhythmias, and asthma, may benefit from physiological monitoring for both diagnosis and continued care. Motion detection technology installed in a home may help reduce the risk of falls and maximize a person’s freedom and involvement in the community [3–7]. A potential new method of gathering physiological data without causing patients any inconvenience is through wearable wellness and physical activity monitoring devices [8–10]. Various operating techniques can be used by microwave sensor devices, depending on the needs of the particular application [11, 14]. Wearable microwave sensing devices work by probing biological tissues with electromagnetic waves to record complex physiological data with amazing accuracy and precision. This innovative method gets around several of the drawbacks of conventional monitoring approaches, including discomfort, obtrusiveness, and limited mobility. These sensors allow people to monitor their health continuously by blending in perfectly with regular clothing. This allows for early detection of abnormalities and proactive management of chronic illnesses [16–21]. These technologies include time-of-flight (ToF) approaches for accurate ranging, frequency-modulated continuous wave (FMCW) radar for distance measuring, and Doppler radar for motion detection [22–26]. Furthermore, developments in microwave technology have produced extremely selective and sensitive sensors that can pick up on minute alterations in the target’s characteristics [38, 39]. The Wireless Body Area Network (WBAN) [41], which typically consists of several tiny, portable, ultralow powers, effective biosensors, is an example of a recently developed health monitoring gadget. Through dedicated networks, wearable gadgets with sensors positioned throughout the body may communicate [29]. Figure 1.1 shows that the size of the worldwide market for wearable medical devices was estimated at USD 30 billion in 2022 and is projected to reach approximately USD 300 billion by 2032, with a compound annual growth rate (CAGR) of 25.1% anticipated over the forecast period of 2023 to 2032 [48].
Figure 1.1 Wearable devices market in 2023-2030 in US dollars.
Source: Precedence Research.
This research attempts to shed light on the revolutionary potential of microwave wearable sensors in transforming healthcare monitoring and spurring the transition to proactive, individualized healthcare paradigms by combining views from a variety of diverse viewpoints.
1.2 Literature Survey
A literature survey on wearable biomedical sensors reveals a growing market in developing safer and more efficient methods for essential tasks. The development and uptake of these sensors have been driven by the rising incidence of chronic illnesses and the increased focus on preventative healthcare [34, 35, 37]. This study explores wearable biological sensors’ prospective uses, difficulties, and technological developments. Below, we provide a summary of key findings and insights from existing literature. In order to present an overview of cutting-edge technology and wearable devices intended for use in the electronic healthcare framework, the paper is divided into three sections, each of which covers one of these three subjects in detail. Systems for the indoor positioning are dealt with in subsection 1.2.1. Subsection 1.2.2 is concerned with the wearable sensors dealing in fall detection. Subsection 1.2.3 gives an overview of the microwave wearable sensing devices.
1.2.1 System for Indoor Positioning
Every application must have the ability to automatically identify items and people. RFID is remote object identification and localization technology that can be applied to patient monitoring. The domain of RFID and wireless sensor networks (WSNs) has experienced substantial growth in the past few years [40, 42]. Presently, there is a tendency to incorporate state-of-the-art wireless technologies into everyday locations, converting them into Smart Spaces that encompass all relevant IoT technologies. RFID is extremely important because of its ability to remotely identify and distinguish objects and people, even in congested areas and electromagnetically challenging conditions, such as private residences or retirement communities. The idea behind this is that it’s getting more and more crucial to keep an eye on how senior citizens move and behave in order to spot any age-related conditions or issues early on (such as senile dementia and Alzheimer’s disease) [43]. The following section shows how such devices, by witnessing individuals as well as non-intrusively evaluating their patterns of behavior, can be used effectively to localize people in their daily lives. Many data points can be stored on an RFID chip and then sent to the cloud for additional processing [45, 47]. An antenna-like electronic circuit board and a microchip are integrated into tags, also known as RFID “transponders,” which are able to transmit radio signals that contain information, primarily the tag’s distinctive identification number [63]. According to how they obtain energy to react to an RFID reader, tags can be categorized as “passive” or “active,” depending on whether they’re equipped with their own source of electricity and frequently transmit their ID signal or if they only use the tiny energy that the reader emits and collects via a small antenna [49]. A few RFID tags are “semi-passive,” meaning they use tiny batteries and only send out signals when they detect a reader signal. A device called an indoor positioning system (IPS) makes it possible to track and locate people or items in limited spaces, usually buildings where GPS signals might not be available or dependable [50]. IPS uses a variety of techniques and tools to precisely pinpoint the location of an object or person. This final technology, as shown in Figure 1.2, foreshadows the use of multiple UWB receivers (also known as anchors), dispersed extensively throughout the testing room, and a substantial length of time for the analysis of the incoming data. The primary benefit is that it makes it possible to prevent the possible effects of fading and shadowing, which might happen at specific frequencies indoors. In order to analyze people’s movements and the relative heights of the tags, a suitable data processing unit was developed and used to estimate the distance of the tags from the reader in addition to the computation of the two angular positions that were previously displayed. The reader’s evaluation of the strength of the received signal indicators (RSSI) allowed this process to be completed. In this regard, Figure 1.2 describes a system where a wearable sensor gathers data, which is then sent to a smartphone. The smartphone forwards an alert to cloud computing for analysis, while also storing the data in a database for review, showcasing a streamlined process for real-time localization and tracking.
Figure 1.2 Localization and tracking system using wearable sensors [51].
1.2.2 Fall Detection System
A fall detection system is a piece of technology intended to identify when someone falls, especially in circumstances when they might not be able to ask for assistance on their own. The system may sound an alert to notify emergency services, caregivers, or other designated contacts when a fall is detected. While some fall detection systems are put in homes or care facilities, others are integrated into wearables like smart watches or pendants. The diagram shown in Figure 1.3 provides a visual representation of a fall detection system utilizing deep learning and wearable sensor data. It captures the process from the initial fall detection by the sensor, through data transmission over the GSM network, to the delivery of an alarm message to a designated receiving terminal. The system’s reliance on satellite communication indicates its potential for wide-area coverage, ensuring that alerts are promptly communicated, and assistance can be mobilized quickly, enhancing safety for individuals prone to falls, particularly the elderly or those with mobility challenges [62, 64].
Figure 1.3 Diagrammatic illustration of the fundamental workings of a deep learning– based fall detection system that uses data from wearable’s sensor.
This study demonstrated that the existing microwave system may reliably identify falls in senior adults who live alone in communities or in cohousing, obviating the requirement for continuous care. Usually, these systems use sensors like gyroscopes or accelerometers to identify abrupt motion changes that point to a fall. These sensors for detecting falls in wearable device-based fall detection systems are integrated into the wearable device via a wrist band, for example, that the subject wears. These systems track several data, including pulse oximetry, oxygen saturation (SPO2), heart rate variability (HRV), electrocardiogram (ECG), and kinematic properties detected by magnetometers, gyroscopes, and accelerometers. These sensors provide the tilt angle orientation, pitch, and oscillation information. For data processing and event classification, the retrieved information is routed to the computation unit. To find the linear and angular acceleration, utilize the accelerometer and gyroscope values. There were two accelerometer sensors: a chest sensor and a thigh sensor. The system underwent two stages of classification: first, it created an inconsistent signal lasting five seconds and paused if the accelerometer unexpectedly crossed a threshold. Five seconds later, the body position was evaluated by the system based on the orientation of the sensor. Under the assumption that the individual is standing, it can be assumed that the sensor’s x axis aligned with the thigh was zero. Comparably, the system detects lying and validates the fall event if the x axis of the sensor positioned at the chest was high. The sensor’s output was transferred via the ZigBee network to a PC, where all processing was done remotely in order to save battery power. Using a single accelerometer to track extremely complicated human movement is challenging. A fresh layout was suggested with six accelerometers that were positioned on the thighs, wrists, and neck [28]. Although the system had a high degree of accuracy, its numerous sensors made it difficult to utilize in everyday situations. Regarding a waist-borne fall detection device, multiple fall detection algorithms provided varying levels of performance. To determine which of the twenty-one distinct algorithms was most appropriate, an analysis was conducted [32]. The use of Wi-Fi or radar as fall detection sensors represents the final significant advancement in sensor technology after 2014. It looks into fall detection from a whole new perspective without using a new sensor. This type of fall detection method’s primary concept is utilizing wireless technology to monitor changes in the environment and establishing a connection between human activity and wireless signals [12]. In addition to protecting users’ privacy, it enables users to carry out daily tasks organically and constantly without wearing any equipment on their bodies [27]. This offers a fresh path into the future. A relatively non-invasive technology called WiFall is offered, which is an interesting example of a totally remote fall detection system. The physical layer channel state information (CSI) is used to detect human movement indoors before learning the specific patterns linked to falls and other behaviors that are of interest. The sensing, learning, and alerting components are the three core parts of the WiFall system. According to the trial data, WiFall had an average false alarm rate of 15% and 90% detection accuracy [65–67].
1.2.3 Wearable Sensor Device
The incorporation of radiofrequency (RF) and microwave technology to create wearable and passive sensors has drawn a lot of attention in recent years. Reliability, light weight, dainty size, and miniaturization are important considerations in the construction of cutting-edge microwave peripheral sensing systems. Even though radar is a highly well-known microwave technology, researchers are nevertheless interested in RF sensing since it has the ability to monitor novel biological characteristics and drastically simplify sensing systems. This segment examines various developments in RF-based wearable sensing covering the design of antennas and tags, their uses, read-out circuits, and constituent incorporation in the demanding electromagnetic environment that wearables are used in. The first publicly accessible platform discussed is UHF RFID, which is used for single and multi-parameter sensing.
1.2.3.1 UHF RFID: From Identification to Multi-Port Sensing
In the 860–960 MHz range, Ultra High Frequency (UHF) Radio Frequency Identification (RFID) is a well-established technology. Compact transponders enable UHF RFID, an expandable technology, to operate at intermediate distances [13]. Now RFID antennas are able to be mass-produced in any form or substance, such as elastomers, paper, plastics, washable e-textiles [30], and bandages of medical quality [31], which can be applied straight to the human body. Commercial-off-the-shelf (COTS) tags have made it possible to measure physiological values like temperature, pressure, and moisture. Chemical properties (such as pH and chlorides) and bio-electric signals (such as skin resistance, ECG, and EMG) are also sensed by additional experimental equipment [32, 33]. Compressing several functionalities onto a single device is an additional benefit. Multiport RFID devices can be produced by boosting the total quantity of implanted ICs, which in turn increases the number of sensors that can be operated simultaneously. It can serve as a single antenna with numerous embedded ICs, or alternatively they can be designed as an array of single-IC antennas placed closely to one another. The Identification Division Multiplexing (ID-DM) technology multiplexes the gathered sensor data on-the-air. It enables the management of multiple data at once using the accepted EPC protocol, based on the ID associated with every IC. Sensor-oriented multi-chip RFID systems can function in one of two ways: either in a multi-parametric sensing mode, where each port operates independently and provides useful data, or in a single-parameter sensing mode, where each port constructively contributes to only one piece of outcome.
Multi-chip RFID systems that are focused on sensing have two possible modes of operation: (i) multi-parametric sensing, where each port functions independently and provides relevant data, or (ii) single-parameter sensing, where each port coherently contributes to a single output data. Here, a number of wearable RFID sensors that employ both strategies are evaluated to show how bio-friendly materials and RFID sensing can be used to give wearables new capabilities in a non-pervasive way. Figure 1.4 presents a systemic illustration of wearable sensor devices used in e-health. It shows a network where individuals (User 1, User 2, User N) are equipped with wearable biosensors. These devices collect health data and transmit it to a server via a data transmission system. The server processes and stores this information in a cloud database, which is accessible to health instructors. This setup enables continuous health monitoring and allows instructors to provide personalized feedback or interventions, illustrating the potential of wearable technology in enhancing healthcare delivery and patient outcomes [72].
Figure 1.4 Systemic illustration of wearable sensor devices used in e-health.
1.2.3.2 Bio-Radar in Microwave Wearable Application
Even though radar is a highly well-known microwave technology, researchers are nevertheless interested in RF sensing since it can monitor novel biological characteristics and drastically simplify sensing systems. Vital indicators, like cardiac and respiratory signals, can be recorded by radar systems, eliminating the need for touch sensors or electrodes. These devices typically send electromagnetic pulses (EM waves) in the direction of the subject’s abdominal wall based on the Doppler effect, which are then reflected and picked up by the radar. The physiological intelligence is contained in the phase modulation of the received signal, which is interpreted as a result of the chest-wall displacement altering the EM waves’ travelled route [41]. The system in question, hereinafter referred to as the “Bio-Radar system,” offers a variety of features that allow for uninterrupted surveillance that can detect variations in vital sign patterns and, in turn, assist in identifying unexpected events, such as a car driver’s fatigue or a baby’s airway obstruction during sleep [41]. Patients who are bedridden and in a serious condition can also be observed, and this may aid in the diagnosing process. It is also possible to use it for psychological purposes, such as measuring stress response. Bio-radar applications that leverage wearables and semi-wearable integration may necessitate complete set-up integration in customized objects. This improves the primary system’s low-key nature and expedites the manufacturing process, and it gives the person being watched greater comfort. Antennas are an important component in the bio-radar context that determine the radar’s and the target’s field of sight. Consequently, one way to empower the entire system is to fully integrate them into the application objects deception. It is possible to use textile antennas to achieve this purpose [44]. Various fabrication techniques can be employed to create wearable electronic textiles from a variety of materials. The ultimate use is always linked to the materials and fabrication techniques chosen. Because of this, the study of e-textiles requires knowledge of multiple disciplines, including textiles, materials, electronics, mechanics, and computer engineering [15]. The diagram in Figure 1.5 illustrates a biosensor system designed to monitor sweat concentration. It features a sensor applied to the skin, likely on the arm, which absorbs sweat to analyze its composition. The sensor transmits data as a microwave signal to a chip that interprets the parameters. This chip is responsible for recognizing the specific sweat parameters and displaying the results on a screen. This technology could be pivotal for health monitoring, allowing for non-invasive tracking of physiological changes through sweat analysis, potentially providing insights into hydration levels, stress, and other health-related metrics [77].
Figure 1.5 Biosensors detecting sweat concentration.
1.3 Procedure and Working
1.3.1 Sensor Architecture
Frequency Selection:
Take into account the intended use when selecting an appropriate frequency range. Microwave sensors typically function in the GHz band.
Antenna Design:
Create antennas that are appropriate for wearable technology. Antennas that are conformal, flexible, and compact are frequently favored.
Sensor Configuration:
Choose the sensor arrangement that best suits the particular parameter that needs to be measured. For example, a radar-type sensor would work well for breathing monitoring.
1.3.2 Principle of Measurement
Recognize the relationship that exists between the target physiological parameter and microwave signals. For example, differences in heart rate or respiration can alter a tissue’s dielectric characteristics, which microwave sensors can pick up on.
Based on the needs of the application, select a suitable sensing method, such as impulse radar, frequency-modulated continuous wave (FMCW) radar, or Doppler radar.
1.3.3 Signal Encoding
Utilize the microwave sensor to gather raw data.
To obtain pertinent data on the metabolic parameter, use signal processing techniques including feature extraction, filtering, and noise reduction.
When analyzing and interpreting signals, make use of sophisticated algorithms like machine learning techniques for anomaly or pattern identification.
1.3.4 Calibration and Verification
To guarantee measurement accuracy and dependability, calibrate the sensor system.
Verify the sensor’s performance with controlled trials and by contrasting it with other systems or techniques.
As needed, iterate the calibration and design processes to increase precision and dependability.
1.3.5 Connectivity with Wearable Technology
Include the microwave sensor in a wearable form factor, like a clothing item, wristband, or patch.
Make sure the wearable gadget is cozy, unobtrusive, and appropriate for extended usage.
Include the electronics required for power management, wireless transmission, and data processing.
1.3.6 Examining and Implementing
To evaluate the wearable sensor system’s usability, dependability, and performance, put it through a rigorous testing process under real-world circumstances.
Resolve any problems or restrictions found throughout the testing process.
Install the sensor system for planned uses, such as wellness management, sports performance tracking, or remote health monitoring.
1.3.7 Perks of Microwave Wearable Sensors
Portability:
Because compact wearables are small, light, and convenient to carry, users can wear them all day without feeling burdened. Without limiting mobility, this portability allows for constant tracking and monitoring of multiple parameters.
Comfort:
Designed to be worn directly on the body, compact wearables frequently have soft materials and ergonomic layouts that provide comfort even after extended use. There is a decreased chance of discomfort or annoyance for users, which improves their entire experience and encourages them to wear the device consistently.
24/7 Monitoring:
Wearable devices may be worn constantly because of their lightweight design and tiny size, which makes it possible to monitor health indicators, activity levels, sleep cycles, and other metrics around-the-clock. This ongoing observation offers a thorough picture of the health and behavior of users throughout time.
Real-Time Feedback:
Vibrating alerts, LED indicators, or notifications on linked devices are examples of real-time feedback methods that are frequently included in compact wearables. Users’ motivation and engagement are increased because they may make decisions regarding their productivity, fitness, or health right away thanks to this instant feedback.
Wireless Communication:
