Time Domain Measurements of Signals Backscattered by Wideband RFID Tags

Passive wideband RFID is increasing interest for its capability of providing high-accuracy tag localization in addition to identification and tag-reader communication. The measurement of backscattering capabilities of wideband antennas is usually conducted in the frequency domain by using network analyzers, which does not allow for the extraction of the antenna mode component of the backscattered signal when the antenna load is time variant. To overcome this issue, in this paper we present a novel setup for time domain measurements of signals backscattered by wideband RFID tags. Experimental evaluations are presented for comparing different wideband antennas and show the effects of the setup characteristics and of the processing schemes on the achievable measurement results

Human Breathing Rate Estimation from Radar Returns Using Harmonically Related Filters

Radar-based noncontact sensing of life sign signals is often used in safety and rescue missions during disasters such as earthquakes and avalanches and for home care applications. The radar returns obtained from a human target contain the breathing frequency along with its strong higher harmonics depending on the target’s posture. As a consequence, well understood, computationally efficient, and the most popular traditional FFT-based estimators that rely only on the strongest peak for estimates of breathing rates may be inaccurate. The paper proposes a solution for correcting the estimation errors of such single peak-based algorithms. The proposed method is based on using harmonically related comb filters over a set of all possible breathing frequencies. The method is tested on three subjects for different postures, for different distances between the radar and the subject, and for two different radar platforms: PN-UWB and phase modulated-CW (PM-CW) radars. Simplified algorithms more suitable for real-time implementation have also been proposed and compared using accuracy and computational complexity. The proposed breathing rate estimation algorithms provide a reduction of about 81% and 80% in the mean absolute error of breathing rates in comparison to the traditional FFT-based methods using strongest peak detection, for PN-UWB and PM-CW radars, respectively

Enhancing system performance through objective feature scoring of multiple persons' breathing using non-contact RF approach

Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates privacy concerns and the need for users to carry a device. In addition, such methods can reduce stress on healthcare facilities by providing intelligent digital health technologies. These intelligent digital technologies utilize a machine learning (ML)-based system for classifying breathing abnormalities. Despite advances in ML-based systems, the increasing dimensionality of data poses a significant challenge, as unrelated features can significantly impact the developed system’s performance. Optimal feature scoring may appear to be a viable solution to this problem, as it has the potential to improve system performance significantly. Initially, in this study, software-defined radio (SDR) and RF sensing techniques were used to develop a breathing monitoring system. Minute variations in wireless channel state information (CSI) due to breathing movement were used to detect breathing abnormalities in breathing patterns. Furthermore, ML algorithms intelligently classified breathing abnormalities in single and multiple-person scenarios. The results were validated by referencing a wearable sensor. Finally, optimal feature scoring was used to improve the developed system’s performance in terms of accuracy, training time, and prediction speed. The results showed that optimal feature scoring can help achieve maximum accuracy of up to 93.8% and 91.7% for single-person and multi-person scenarios, respectively

IoT Platform for COVID-19 Prevention and Control: A Survey

As a result of the worldwide transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has evolved into an unprecedented pandemic. Currently, with unavailable pharmaceutical treatments and vaccines, this novel coronavirus results in a great impact on public health, human society, and global economy, which is likely to last for many years. One of the lessons learned from the COVID-19 pandemic is that a long-term system with non-pharmaceutical interventions for preventing and controlling new infectious diseases is desirable to be implemented. Internet of things (IoT) platform is preferred to be utilized to achieve this goal, due to its ubiquitous sensing ability and seamless connectivity. IoT technology is changing our lives through smart healthcare, smart home, and smart city, which aims to build a more convenient and intelligent community. This paper presents how the IoT could be incorporated into the epidemic prevention and control system. Specifically, we demonstrate a potential fog-cloud combined IoT platform that can be used in the systematic and intelligent COVID-19 prevention and control, which involves five interventions including COVID-19 Symptom Diagnosis, Quarantine Monitoring, Contact Tracing & Social Distancing, COVID-19 Outbreak Forecasting, and SARS-CoV-2 Mutation Tracking. We investigate and review the state-of-the-art literatures of these five interventions to present the capabilities of IoT in countering against the current COVID-19 pandemic or future infectious disease epidemics.Comment: 12 pages; Submitted to IEEE Internet of Things Journa

FarSense: pushing the range limit of WiFi-based respiration sensing with CSI ratio of two antennas

International audienceThe past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring. However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. On the other hand, most home environments only have one WiFi access point and it may not be located in the same room as the target. This sensing range constraint greatly limits the application of the proposed approaches in real life. This paper presents FarSense-the first real-time system that can reliably monitor human respiration when the target is far away from the WiFi transceiver pair. FarSense works well even when one of the transceivers is located in another room, moving a big step towards real-life deployment. We propose two novel schemes to achieve this goal: (1) Instead of applying the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range; (2) The division operation further enables us to utilize the phase information which is not usable with one single antenna for sensing. The orthogonal amplitude and phase are elaborately combined to address the "blind spots" issue and further increase the sensing range. Extensive experiments show that FarSense is able to accurately monitor human respiration even when the target is 8 meters away from the transceiver pair, increasing the sensing range by more than 100%. 1 We believe this is the first system to enable through-wall respiration sensing with commodity WiFi devices and the proposed method could also benefit other sensing applications

Design and Implementation of a Stepped Frequency Continuous Wave Radar System for Biomedical Applications

There is a need to detect vital signs of human (e.g., the respiration and heart-beat rate) with noncontact method in a number of applications such as search and rescue operation (e.g. earthquakes, fire), health monitoring of the elderly, performance monitoring of athletes Ultra-wideband radar system can be utilized for noncontact vital signs monitoring and tracking of various human activities of more than one subject. Therefore, a stepped-frequency continuous wave radar (SFCW) system with wideband performance is designed and implemented for Vital signs detection and fall events monitoring. The design of the SFCW radar system is firstly developed using off-the-shelf discrete components. Later, the system is implemented using surface mount components to make it portable with low cost. The measurement result is proved to be accurate for both heart rate and respiration rate detection within ±5% when compared with contact measurements. Furthermore, an electromagnetic model has been developed using a multi-layer dielectric model of the human subject to validate the experimental results. The agreement between measured and simulated results is good for distances up to 2 m and at various subjects’ orientations with respect to the radar, even in the presence of more than one subject. The compressive sensing (CS) technique is utilized to reduce the size of the acquired data to levels significantly below the Nyquist threshold. In our demonstration, we use phase information contained in the obtained complex high-resolution range profile (HRRP) to derive the motion characteristics of the human. The obtained data has been successfully utilized for non-contact walk, fall and limping detection and healthcare monitoring. The effectiveness of the proposed method is validated using measured results

UWB for medical applications

The aim of this project is to be familiarized with UWB radar technology for medical applications. The extremely high-resolution UWB signals together with the low transmit power are good candidates for non-invasive patient monitoring. For instance, breathe rate monitoring. The project will investigate the UWB radar signal detection for breathe monitoring, supported with real experiments. HW equipment consist of TIME DOMAIN® PulsON® 400 Series for Ranging and Communications Application System model and companion Matlab software will support the analysis. ProgrThe respiratory frequency monitoring is an important indicator to the medical field. Also, the need of sensor system solutions for home monitoring is growing as the life expectancy of the world population is increasing. For those reasons, this thesis considers the use of an impulse-radio (IR) UWB radar system to track respiratory frequency and respiratory patterns, as apnoea episodes, in a non-invasive and real-time way. We start our analysis with well-known spectral estimators, like the Periodogram or Bartlett estimator to obtain the first results and insights over the estimation of steady frequencies in an offline regime. Later, we consider the use of adaptive algorithms like the LMS together with AR modelling to monitor the breathing rate transitions and variations. Simulations have been performed to validate and adjust the parameters of the algorithms, balancing between its trade-offs to suit our solution to the problem. Finally, the results of the experiments in different environments are presented meeting the expected requirements and performance of the system.La monitorización de la frecuencia respiratoria es un importante indicador en el campo de la medicina. De la misma manera, la necesidad de soluciones basadas en sistemas de sensores para monitorizar pacientes no hospitalizados en sus hogares crece al mismo ritmo que la esperanza de vida de la población mundial crece. Por esas razones, esta tesis considera el uso de un sistema de radar basado en impulse-radio (IR) UWB para controlar la frecuencia respiratoria, y a la vez, patrones respiratorios, como episodios de apnea, de una manera no invasiva y a tiempo real. Empezamos nuestro análisis con estimadores espectrales como el Periodograma o Estimador Bartlett para obtener los primeros resultados en la estimación de frecuencias estables en una configuración no en tiempo real. Más tarde, consideramos el uso de algoritmos adaptativos como LMS junto a modelado AR para monitorizar las transiciones y variaciones en la frecuencia respiratoria. Se han llevado a cabo simulaciones para validar y ajustar los parámetros de los algoritmos, intentando compensar sus diferentes características para ajustarlos a nuestra problemática. Finalmente, los resultados de experimentos en diferentes escenarios son presentados cumpliendo con los requerimientos y rendimientos esperados del sistema. La monitorització de la freqüència respiratòria es un important indicador en el camp de la medicina. De la mateixa manera, la necessitat de solucions basades en sistemes de sensors per a monitoritzar pacients no hospitalitzats a les seves llars creix a mesura que la esperança de vida de la població mundial creix. Per aquestes raons, aquesta tesi considera l’ús d’un sistema de radar basat en impulse-radio (IR) UWB per a controlar la freqüència respiratòria, i al mateix temps, patrons de respiració, com episodis d’apnea, d’una manera no invasiva i a temps real. Comencem el nostre anàlisi amb estimadors espectrals com el Periodograma o l’Estimador Bartlett per a obtenir els primers resultats en l’estimació de freqüències estables en una configuració no en temps real , per continuar amb, l’ús d’algoritmes adaptatius com LMS junt a modelat AR per a monitoritzar les transicions y variacions en la freqüència respiratòria. Hem dut a terme simulacions per a validar i ajustar els paràmetres dels algoritmes, intentant compensar les seves diferents característiques per a ajustar-los a la nostra problemàtica. Finalment, els resultats de experiments en diferent escenaris son presentats acomplint amb els requisits i rendiments esperats del sistema