Impulse Radio Ultra-wideband Research Articles

Indoor Activity and Vital Sign Monitoring for Moving People with Multiple Radar Data Fusion

The monitoring of human activity and vital signs plays a significant role in remote health-care. Radar provides a non-contact monitoring approach without privacy and illumination concerns. However, multiple people in a narrow indoor environment bring dense multipaths for activity monitoring, and the received vital sign signals are heavily distorted with body movements. This paper proposes a framework based on Frequency Modulated Continuous Wave (FMCW) and Impulse Radio Ultra-Wideband (IR-UWB) radars to address these challenges, designing intelligent spatial-temporal information fusion for activity and vital sign monitoring. First, a local binary pattern (LBP) and energy features are extracted from FMCW radar, combined with the wavelet packet transform (WPT) features on IR-UWB radar for activity monitoring. Then the additional information guided fusing network (A-FuseNet) is proposed with a modified generative and adversarial structure for vital sign monitoring. A Cascaded Convolutional Neural Network (CCNN) module and a Long Short Term Memory (LSTM) module are designed as the fusion sub-network for vital sign information extraction and multisensory data fusion, while a discrimination sub-network is constructed to optimize the fused heartbeat signal. In addition, the activity and movement characteristics are introduced as additional information to guide the fusion and optimization. A multi-radar dataset with an FMCW and two IR-UWB radars in a cotton tent, a small room and a wide lobby is constructed, and the accuracies of activity and vital sign monitoring achieve 99.9% and 92.3% respectively. Experimental results demonstrate the superiority and robustness of the proposed framework.

Contactless Simultaneous Breathing and Heart Rate Detections in Physical Activity Using IR-UWB Radars.

Vital signs monitoring in physical activity (PA) is of great significance in daily healthcare. Impulse Radio Ultra-WideBand (IR-UWB) radar provides a contactless vital signs detection approach with advantages in range resolution and penetration. Several researches have verified the feasibility of IR-UWB radar monitoring when the target keeps still. However, various body movements are induced by PA, which lead to severe signal distortion and interfere vital signs extraction. To address this challenge, a novel joint chest–abdomen cardiopulmonary signal estimation approach is proposed to detect breath and heartbeat simultaneously using IR-UWB radars. The movements of target chest and abdomen are detected by two IR-UWB radars, respectively. Considering the signal overlapping of vital signs and body motion artifacts, Empirical Wavelet Transform (EWT) is applied on received radar signals to remove clutter and mitigate movement interference. Moreover, improved EWT with frequency segmentation refinement is applied on each radar to decompose vital signals of target chest and abdomen to vital sign-related sub-signals, respectively. After that, based on the thoracoabdominal movement correlation, cross-correlation functions are calculated among chest and abdomen sub-signals to estimate breath and heartbeat. The experiments are conducted under three kinds of PA situations and two general body movements, the results of which indicate the effectiveness and superiority of the proposed approach.

A 27 dB Sidelobe Suppression, 1.12 GHz BW−10dB UWB Pulse Generator With Process Compensation

This brief presents an on-off keying (OOK) driven impulse radio ultra-wideband (IR-UWB) pulse generator (PG) for 3-5 GHz applications, featuring high sidelobe suppression (SLS), large bandwidth ( BW <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-10dB</sub> ), and robustness against process variations. To eliminate the need for a dedicated off-chip pulse-shaping filter, a transistor-sizing-based pulse shaping scheme is adopted to generate a broadband pulse with a triangular envelope, ensuring enhanced SLS with extended signal bandwidth. The proposed process compensating circuit mitigates the effects of process variations on the propagation delay of the logic gates to provide stable pulse timing characteristics. Implemented in 65-nm CMOS process, the prototype PG provides a UWB pulse with an amplitude of 634 mV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pp</sub> , BW <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-10dB</sub> of 1.12 GHz, and 27 dB SLS, while occupying a total chip area of 964 μm × 669 μm. The proposed PG provides enhanced pulse characteristics with modest energy consumption among the reported works. Based on the proposed figure-of-merit considering the SLS and quality of pulse, the PG in this work shows a competitive FOM of 3.36×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> .

Hybrid CNN-LSTM Network for Real-Time Apnea-Hypopnea Event Detection Based on IR-UWB Radar

Polysomnography (PSG) is the gold-standard for sleep apnea and hypopnea syndrome (SAHS) diagnosis. Because the PSG system is not suitable for long-term continuous use owing to the high cost and discomfort caused by attached multi-channel sensors, alternative methods using a non-contact sensor have been investigated. However, the existing methods have limitations in that the radar-person distance is fixed, and the detected apnea hypopnea (AH) event cannot be provided in real-time. In this paper, therefore, we propose a novel approach for real-time AH event detection with impulse-radio ultra-wideband (IR-UWB) radar using a deep learning model. 36 PSG recordings and simultaneously measured IR-UWB radar data were used in the experiments. After the clutter was removed, IR-UWB radar images were segmented by sliding a 20-s window at 1-s shift, and categorized into two classes: AH and N. A hybrid model combining the convolutional neural networks and long short-term memory networks was trained with the data, which consisted of class-balanced segments. Time sequenced classified outputs were then fed to an event detector to identify valid AH events. Therefore, the proposed method showed a Cohen’s kappa coefficient of 0.728, sensitivity of 0.781, specificity of 0.956, and an accuracy of 0.930. According to the apnea-hypopnea index (AHI) estimation analysis, the Pearson’s correlation coefficient between the estimated AHI and reference AHI was 0.97. In addition, the average accuracy and kappa of SAHS diagnosis was 0.98 and 0.96, respectively, for AHI cutoffs of ≥ 5, 15, and 30 events/h. The proposed method achieved the state-of-the-art performance for classifying SAHS severity without any hand-engineered feature regardless of the user’s location. Our approach can be utilized for a cost-effective and reliable SAHS monitoring system in a home environment.

Quantified assessment of hyperactivity in ADHD youth using IR-UWB radar

Research on the quantification of hyperactivity in youth with attention-deficit/hyperactivity disorder (ADHD) has been limited and inconsistent. The purpose of this study was to test the discriminative value of impulse-radio ultra-wideband (IR-UWB) radar for monitoring hyperactive individuals with ADHD and healthy controls (HCs). A total of 10 ADHD patients and 15 HCs underwent hyperactivity assessment using IR-UWB radar during a 22-min continuous performance test. We applied functional ANOVA to compare the mean functions of activity level between the 2 groups. We found that the mean function of activity over time was significantly different and that the activity level of the ADHD group slightly increased over time with high dispersion after approximately 7 min, which means that the difference in activity level between the two groups became evident at this period. Further studies with larger sample sizes and longer test times are warranted to investigate the effect of age, sex, and ADHD subtype on activity level function.

Robust Cardiac Rate Estimation of an Individual

Impulse-radio ultra-wideband (IR-UWB) radar devices can be used to monitor vital signs, such as respiratory and cardiac rates. For this purpose, the phase of an echo signal received from the chest or back of a human is usually used; subsequently, respiratory rate estimation (RRE) and cardiac rate estimation (CRE) can be achieved by estimating two fundamental frequencies corresponding to the respiratory and cardiac rates, respectively. However, the interference caused by undesirable terms would inevitably be induced in an echo signal; thus, the cardiac rate information contained in the phase of the received signal is often smeared and destroyed. Among several sources of interference such as high-order harmonics of respiratory rate, random body movement of an individual, and system noise, the body movement of an individual is the most critical for reliable CRE, making it a very challenging task. To address this problem, we herein propose a new framework comprising four steps: 1) RRE via differentiation and spectrum analysis for an unwrapped phase of received signal, 2) coarse estimation of cardiac rate candidates, 3) selection of three promising candidates for the desired cardiac rate based on a histogram of several candidates, and 4) determination of the desired cardiac rate via a fuzzy logic rule-based method. In experiments using 7.29 GHz IR-UWB radar hardware, we observed that our proposed framework is capable of performing accurate and robust real-time CRE even in the presence of body movement.

Attention-Based LSTM for Non-Contact Sleep Stage Classification Using IR-UWB Radar.

Manual scoring of sleep stages from polysomnography (PSG) records is essential to understand the sleep quality and architecture. Since the PSG requires specialized personnel, a lab environment, and uncomfortable sensors, non-contact sleep staging methods based on machine learning techniques have been investigated over the past years. In this study, we propose an attention-based bidirectional long short-term memory (Attention Bi-LSTM) model for automatic sleep stage scoring using an impulse-radio ultra-wideband (IR-UWB) radar which can remotely detect vital signs. Sixty-five young (30.0 ± 8.6 yrs.) and healthy volunteers underwent nocturnal PSG and IR-UWB radar measurement simultaneously; From 51 recordings, 26 were used for training, 8 for validation, and 17 for testing. Sixteen features including movement-, respiration-, and heart rate variability-related indices were extracted from the raw IR-UWB signals in each 30-s epoch. Sleep stage classification performances of Attention Bi-LSTM model with optimized hyperparameters were evaluated and compared with those of conventional LSTM networks for same test dataset. In the results, we achieved an accuracy of 82.6 ± 6.7% and a Cohen's kappa coefficient of 0.73 ± 0.11 in the classification of wake stage, REM sleep, light (N1+N2) sleep, and deep (N3) sleep which is significantly higher than the conventional LSTM networks (p < 0.01). Moreover, the classification performances were higher than those reported in comparative studies, demonstrating the effectiveness of the attention mechanism coupled with bi-LSTM networks for the sleep staging using cardiorespiratory signals.

A Digitally Controlled Multichannel Spectrally Efficient Low-Power Transceiver for Ultra-Wideband Cognitive Radio Applications

A multichannel transceiver design with low hardware complexity, flexibility and efficient spectrum use intended for Impulse Radio (IR) Ultra-Wideband (UWB) apertures, is proposed in this paper. The transceiver supports 14 channels and is implemented in TSMC CMOS process. The transmitter adopts a symmetrical triangular narrow pulse generator, a multiband LC-VCO, a mixer and a Variable-Gain Amplifier. A digital control of transmitter output power and carrier frequency is achieved by a current-steering Digital-to-Analog Converter (CS-DAC). The receiver follows the noncoherent Energy Detection (ED) scheme including Low-Noise Amplifier (LNA) which takes the benefits of the body biasing technique to further reduce power consumption, squarer and comparator. The LNA achieves high gain of up to 30[Formula: see text]dB allowing to enhance the receiver sensitivity which is [Formula: see text]83.7[Formula: see text]dBm at 25[Formula: see text]Mbps. The transmitter provides output pulses with a width of 3[Formula: see text]ns, a pulse repetition rate of 20[Formula: see text]ns and a maximum Power Spectral Density (PSD) of [Formula: see text]42[Formula: see text]dBm/MHz. The power consumptions of transmitter and receiver are 35.6[Formula: see text]pJ/bit and 0.207[Formula: see text]nJ/bit at 25[Formula: see text]Mbps, respectively. Maximizing utilization of the scarce wireless spectrum, enhancing spectral flexibility and efficiency and reducing power consumption are the main contributions brought by this work, enabling this transceiver to be able to deal with the current debate on producing single multifunctional Cognitive Radio (CR) UWB systems.

A Computationally Lightest and Robust Neural Network Receiver for Ultra Wideband Time Hopping Communication Systems

This work systematically develops and proposes the simplest form of Neural Network (NN) architecture for efficient detection of signals in an impulse radio (IR) ultra wideband (UWB) time hopping (TH) communication system. The simplicity of the NN receiver ascribes to the subtle change in the training method, which contrary to the conventional ones, requires the network to be trained with only 10000 symbols in an ideal condition without any channel impairments to allow the receiver to learn only the desired signal patterns. The detection efficiency of the NN receiver is attributed to a key parameter entitled as an optimal average cost function which is formulated from the soft decision statistics that is reached by the NN receiver in 3000 training epochs owing to the prudent selection of all hyper-parameters. Both the simplicity and detection efficiency of the NN receiver are quantified by i) a comparison as well as a comprehensive performance evaluation conducted with the classic Rake receiver structures in a broad range of chaotic communication scenarios involving mobility and ii) derivation of simple expressions to compute the computational complexities. The performance results demonstrate that the proposed NN receiver cuts-down the computational complexity by 76% while attaining and maintaining a very high performance gain of 30 dB in signal-to-noise ratio on average for all test scenarios.

Ultra-wide band energy harvesting for ultra-low power electronics applications

In this work, the feasibility of energy harvesting in the useful UWB band (i.e., 3.1-10.6 GHz) is analytically investigated. A typical UWB communications/EH chain in this band is modeled and analyzed, considering the spectral constraints imposed by the federal communications commission (FCC) to UWB signaling. Based on the developed model, accurate analytical expressions are derived for the average received powers of two common types of impulse radio UWB (IR-UWB) signaling waveforms. Numerical simulations on the system-level show excellent agreement with the obtained analytical expressions. Moreover, the DC power levels expected from spectrally constrained IR-UWB waveforms are extremely low (less than 0.3 microwatt) and, accordingly, provide useful guidelines for the design and development of ULP electronics applications in the sub-microwatt range.

Implementation of a Multimedia Computing Healthcare Service Using a Voice Signal-Analysis Module and IR-UWB

As the population ages and the number of people with chronic diseases increases, society is changing from the existing diagnostic- and treatment-oriented medical services to preventive- and management-oriented medical services. This paradigm is named Ubiquitous Smart Healthcare. Various multimedia-based healthcare services are becoming especially key to health management in the smart age. In other words, technology, research, and integration of multimedia healthcare services, such as video data, voice data, IoT networks, and the like, are proceeding. Due to well-being trends and lifestyles of the aging society, medical services are rapidly becoming ubiquitous, in which the individual and the medical professional cooperate closely in hospital-centered treatment services to provide health management and promotion, and prevention of diseases. To realize this, various bio-signal measurement techniques based on multimedia are being studied and applied. Therefore, in this paper, we propose a remote voice signal-measurement system that combines two Impulse Radio Ultra Wide Band (IR-UWB) systems to provide a capable IR real-time location service, and we design and implement them. Also, we can reduce the complexity of existing voice signal–measurement algorithms, and we optimize the system by proposing a filter suitable for voice signal–measurement algorithms. As a result, we confirm that the voice signal can be measured by extending the measurement distance (based on wireless communications) by only about 3 m to 10 m. By applying the measurement and analysis method for voice-signal data in the wireless communication system environment, a healthcare service with improved reliability can be applied. In particular, it is possible to provide a multimedia healthcare computing environment in which new diagnostic data can be found and utilized in the field of multimedia-based healthcare service technology.

Fully Flexible Textile Antenna-Backed Sensor Node for Body-Worn UWB Localization

A mechanically flexible textile antenna-backed sensor node is designed and manufactured, providing accurate personal localization functionality by application of Decawave’s DW1000 Impulse Radio Ultra-Wideband (IR-UWB) Integrated Circuit (IC). All components are mounted on a flexible polyimide foil, which is integrated on the backplane of a wearable cavity-backed slot antenna designed for IR-UWB localization in Channels 2 and 3 of the IEEE 802.15.4-2011 standard (3744 MHz–4742.4 MHz). The textile antenna’s radiation pattern is optimized to mitigate body effects and to minimize absorption by body tissues. Furthermore, its time-domain characteristics are measured to be adequate for localization. By combining the antenna and the bendable Printed Circuit Board (PCB), a mechanically supple sensor system is realized, for which the performance is validated by examining it as a node used in a complete localization system. This shows that six nodes around the body must be deployed to provide system coverage in all directions around the wearer. Even without using sleep mode functionalities, the measurements indicate that the system’s autonomy is 13.3 h on a 5 V 200 mAh battery. Hence, this system acts as a proof of concept for the joining of localization electronics and other sensors with a full-textile antenna into a mechanically flexible sensor system.

Design of 3–5 GHz tunable memristor-based OOK-UWB transmitter

This paper presents the design of a tunable memristor-based On-off keying (OOK) impulse-radio ultrawideband (IR-UWB) transmitter (TX), operating within the 3–5 GHz band. The proposed TX contains a CMOS-Memristor-based Ring Oscillator, an OOK modulator, a control signals circuit, and a pulse shape filter. The linear programming of the memristor device offers a smooth linear frequency variation of the ring oscillator. Accordingly, in the proposed circuit, two external control signals Vctrl and Vtune are used to tune the memristance value and the output pulse width, respectively, allowing adjusting the power spectral density (PSD) bandwidth and its central frequency. The IR-UWB TX is suitable for OOK modulation schema. It is designed and simulated using TSMC CMOS 0.18 µm technology with a 1.8 V supply voltage. The simulation results show that the proposed TX can cover both narrowband and wideband UWB PSD, depending on the number of oscillations per emitted pulse. The TX output swing is 483mVpp, and the pulse duration is 0.8 ns for narrowband and 1.74 ns for wideband. For a pulse repetition frequency of 10 MHz, the pulse generator consumes 94.8 µW and accomplishes an energy efficiency of 23.4%, while the total energy consumption is 9.48 pJ/pulse.

A 13.56-MHz -25-dBm-Sensitivity Inductive Power Receiver System-on-a-Chip With a Self-Adaptive Successive Approximation Resonance Compensation Front-End for Ultra-Low-Power Medical Implants.

Battery-less and ultra-low-power implantable medical devices (IMDs) with minimal invasiveness are the latest therapeutic paradigm. This work presents a 13.56-MHz inductive power receiver system-on-a-chip with an input sensitivity of -25.4dBm (2.88μW) and an efficiency of 46.4% while driving a light load of 30μW. In particular, a real-time resonance compensation scheme is proposed to mitigate resonance variations commonly seen in IMDs due to different dielectric environments, loading conditions, and fabrication mismatches, etc. The power-receiving front-end incorporates a 6-bit capacitor bank that is periodically adjusted according to a successive-approximation-resonance-tuning (SART) algorithm. The compensation range is as much as 24pF and it converges within 12 clock cycles and causes negligible power consumption overhead. The harvested voltage from 1.7 V to 3.3V is digitized on-chip and transmitted via an ultra-wideband impulse radio (IR-UWB) back-telemetry for closed-loop regulation. The IC is fabricated in 180-nm CMOS process with an overall current dissipation of 750 nA. At a separation distance of 2 cm, the end-to-end power transfer efficiency reaches 16.1% while driving the 30-μW load, which is immune to artificially induced resonance capacitor offsets. The proposed system can be applied to various battery-less IMDs with the potential improvement of the power transfer efficiency on orders of magnitude.

A Conformal UWB Dual-Polarized Antenna for Wireless Capsule Endoscope Systems

A conformal ultrawideband (UWB) dual-polarized capsule antenna is designed for ingestible wireless capsule endoscope (WCE) systems. To the best of our knowledge, this is the first time to study the UWB dual-polarized antenna for a capsule endoscope system. Dual polarization is realized by a step-shaped monopole and an irregular rectangular loop antenna, which are printed on the top and bottom side of the dielectric substrate, separately. The simulated antenna has very wide -10 dB impedance bandwidth of 2.15-14.75 GHz, which can cover 2.4-2.48 GHz, 5.725-5.875 GHz Industrial, Scientific, and Medical (ISM) band, 3.5-4.5 GHz impulse radio UWB (IR-UWB) and 3.1-10.6 GHz UWB. Moreover, because of the orthogonally polarized waves generated by two types of antennas, the simulated isolation between them is better than 40 dB in the whole band. Considering the application of the antenna in the wireless body area network system, this design focuses on 1-7 GHz frequency band, especially 4 GHz IR-UWB. An antenna prototype is fabricated and measured, and the measured S-parameters and radiation pattern demonstrate a reasonable agreement with the simulation results. Furthermore, specific absorption rate and link budget of the antenna are analyzed.

Low data regimes in extreme climates: Foliage penetration personnel detection using a wireless network-based device-free sensing approach

As far as low-cost deployment is concerned, wireless network-based device-free sensing (DFS) is of great interest and has successfully demonstrated the feasibility in Foliage penetration (FOPEN) target recognition. The classification accuracy of this technology is known to dramatically decrease in extreme climates where the received signals tend to be severely attenuated; while deep learning approaches have boosted performance, they only perform effectively when trained with large amounts of labeled data. Consequently, it is still unknown how to ensure reasonable detection accuracy in extreme climates where sufficient samples are difficult to obtain. To address this concern, we adopt two special measures for performance enhancement in this paper. One measure is to employ higher-order spectral (HOS) analysis to transform the time-domain signals into the bispectrum image representations, so that the shift to an image classification task could provide the advantage of using the existing Convolutional Neural Network (CNN) models. More importantly, the immunity of the approach against the unwanted clutters in foliage environments can be improved. The other one is to present an end-to-end Deep Learning Data Augmentation and Classification (DLDAC) model comprised of a Deep Convolutional Generative Adversarial Network (for data augmentation) and a SqueezeNet CNN backbone (for target classification), which can improve the classifier performance by using the augmented data on-the-fly. Thus, the negative impacts of low data regimes in extreme climates can be considerably accommodated. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a real FOPEN dataset collected by impulse-radio ultra-wideband (IR-UWB) transceivers under three severe weather conditions. The experimental results demonstrate that even when only 300 training samples are taken for each type of target under every weather condition, the average classification accuracy of the proposed approach is still better than 92% in terms of distinguishing between human and other targets.

The Overview of Human Localization and Vital Sign Signal Measurement Using Handheld IR-UWB Through-Wall Radar.

Obtaining information (e.g., position, respiration, and heartbeat rates) on humans located behind opaque and non-metallic obstacles (e.g., walls and wood) has prompted the development of non-invasive remote sensing technologies. Due to its excellent features like high penetration ability, short blind area, fine-range resolution, high environment adoption capabilities, low cost and power consumption, and simple hardware design, impulse radio ultra-wideband (IR-UWB) through-wall radar has become the mainstream primary application radar used for the non-invasive remote sensing. IR-UWB through-wall radar has been developed for nearly 40 years, and various hardware compositions, deployment methods, and signal processing algorithms have been introduced by many scholars. The purpose of these proposed approaches is to obtain human information more accurately and quickly. In this paper, we focus on IR-UWB through-wall radar and introduce the key advances in system design and deployment, human detection theory, and signal processing algorithms, such as human vital sign signal measurement methods and moving human localization. Meanwhile, we discuss the engineering pre-processing methods of IR-UWB through-wall radar. The lasts research progress in the field is also presented. Based on this progress, the conclusions and the development directions of the IR-UWB through-wall radar in the future are also preliminarily forecasted.

An Optimized IR-UWB Communication System with Interference Reduction on a Narrowband System Using Genetic Algorithm

This paper proposed a new impulse radio ultra wideband (IR-UWB) signal to reduce interference on narrowband systems at high usage 5.2 GHz frequency. The combination of 4th and 5th order derivatives of Gaussian pulses were employed to generate the signal so that satisfy the US Federal Communications Commission (FCC) mask. The genetic algorithm technique was used to find the pulse optimized parameters. In this paper, spectrum was optimized by changing the shape factor (sigma) and amplitude of Gaussian pulses so that the transmitted power spectrum density was minimized at a frequency of 5.2 GHz and maximized at the other frequencies from 3.1 to 10.6 GHz according to the FCC mask. The signal was applied in an IR-UWB transceiver system with binary-phase shift keying modulation. The IR-UWB waveform is generated by a programmable digital to analog based circuit. The proposed system was compared with conventional IR-UWB systems at the same condition. The simulation results in multipath channel showed that the proposed system not only reduced interference with the narrowband system, but also this was robust against narrowband interference at 5.2 GHz frequency.

IR-UWB Chipless RFID Reader Based on Frequency Translation Technique for Decoding Frequency-Coded Tags

The impulse radio ultra-wideband (IR-UWB) reader is the most promising technique to interrogate a frequency-coded chipless radio frequency identification (RFID) tag. However, challenges of receiving an ultra-wideband (UWB) pulse, such as complexity, high-noise bandwidth, low reading speed, and high cost, are yet to be addressed in IR-UWB readers. A combined solution based on an IR-UWB transmitter and a frequency translation (FT) method in the receiver is presented in this article. The tag is interrogated by a broadband pulse (3.1-10.6 GHz). In the receiver, instead of processing the entire wideband backscattered pulse in one run, the new architecture transposes the spectrum of the backscattered pulse to a 150-MHz intermediate frequency (IF) band. Hence, the noise bandwidth, cost, and complexity of the receiver are reduced significantly. Since the reading range plays a decisive role in reducing the complexity and cost, a detailed study of the design parameters of the reader, affecting the detection range, is performed. Then, a low-cost and straightforward reader is implemented, based on the outcomes of the parameters' study. It is shown that the reading range of the reader is competitive with that of the state-of-the-art readers and vector network analyzer (VNA), while the complexity, reading time, and cost are dropped.

3-D Gait Abnormality Detection Employing Contactless IR-UWB Sensing Phenomenon

Gait disorder diagnosis and rehabilitation is one area where human perception and observation are highly integrated. Predominantly, gait evaluation comprises technological devices for gait analysis, such as dedicated force sensors, cameras, and wearable sensor-based solutions; however, they are limited by insufficient gait parameter recognition, postprocessing, installation costs, mobility, and skin irritation issues. Thus, the proposed study concentrates on the creation of a widely deployable, noncontact and nonintrusive gait recognition method from impulse radio ultrawideband (IR-UWB) sensing phenomenon, where a standalone IR-UWB system can detect gait problems with less human intervention. A 3-D human motion model for gait identification from IR-UWB has been proposed with embracing spherical trigonometry and vector algebra to determine knee angles. Subsequently, normal and abnormal walking subjects were involved in this study. Abnormal gait subjects belong to the spastic gait category only. The prototype has been tested in both anechoic and multipath environments. The outcomes have been corroborated with a simultaneously deployed Kinect Xbox sensor and supported by a statistical graphical approach Bland and Altman (B&A) analysis.