Passive UHF RFID-based real-time intravenous fluid level sensor.

Ultra high frequency (UHF) passive radio frequency identification (RFID) tag-based sensors are proposed for intravenous (IV) fluid level monitoring in medical Internet of Things (IoT) applications. Two versions of the sensor are proposed: a binary sensor (i.e., full vs. empty state sensing) and a real-time (i.e., continuous level) sensor. The operating principle is demonstrated using full-wave electromagnetic simulation at 910 MHz and validated with experimental results. Generalized Additive Model (GAM) and random forest algorithms are employed for each interrogation dataset. Real-time sensing is accomplished with small deviations across the models. A minimum of 72% and a maximum of 97% of cases are within a 20% error for the GAM model and 62% to 98% for the random forest model. The proposed sensor is battery-free, lightweight, low-cost, and highly reliable. The read range of the proposed sensor is 4.6 m.

Benchmarking Mental Health Status Using Passive Sensor Data: Protocol for a Prospective Observational Study.

BACKGROUND: Computational psychiatry has the potential to advance the diagnosis, mechanistic understanding, and treatment of mental health conditions. Promising results from clinical samples have led to calls to extend these methods to mental health risk assessment in the general public; however, data typically used with clinical samples are neither available nor scalable for research in the general population. Digital phenotyping addresses this by capitalizing on the multimodal and widely available data created by sensors embedded in personal digital devices (eg, smartphones) and is a promising approach to extending computational psychiatry methods to improve mental health risk assessment in the general population. OBJECTIVE: Building on recommendations from existing computational psychiatry and digital phenotyping work, we aim to create the first computational psychiatry data set that is tailored to studying mental health risk in the general population; includes multimodal, sensor-based behavioral features; and is designed to be widely shared across academia, industry, and government using gold standard methods for privacy, confidentiality, and data integrity. METHODS: We are using a stratified, random sampling design with 2 crossed factors (difficulties with emotion regulation and perceived life stress) to recruit a sample of 400 community-dwelling adults balanced across high- and low-risk for episodic mental health conditions. Participants first complete self-report questionnaires assessing current and lifetime psychiatric and medical diagnoses and treatment, and current psychosocial functioning. Participants then complete a 7-day in situ data collection phase that includes providing daily audio recordings, passive sensor data collected from smartphones, self-reports of daily mood and significant events, and a verbal description of the significant daily events during a nightly phone call. Participants complete the same baseline questionnaires 6 and 12 months after this phase. Self-report questionnaires will be scored using standard methods. Raw audio and passive sensor data will be processed to create a suite of daily summary features (eg, time spent at home). RESULTS: Data collection began in June 2022 and is expected to conclude by July 2024. To date, 310 participants have consented to the study; 149 have completed the baseline questionnaire and 7-day intensive data collection phase; and 61 and 31 have completed the 6- and 12-month follow-up questionnaires, respectively. Once completed, the proposed data set will be made available to academic researchers, industry, and the government using a stepped approach to maximize data privacy. CONCLUSIONS: This data set is designed as a complementary approach to current computational psychiatry and digital phenotyping research, with the goal of advancing mental health risk assessment within the general population. This data set aims to support the field's move away from siloed research laboratories collecting proprietary data and toward interdisciplinary collaborations that incorporate clinical, technical, and quantitative expertise at all stages of the research process. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/53857.

Optimal Excitation and Readout of Resonators Used as Wireless Passive Sensors.

Resonators are passive time-invariant components that do not produce a frequency shift. However, they respond to an excitation signal close to resonance with an oscillation at their natural frequencies with exponentially decreasing amplitudes. If resonators are connected to antennas, they form purely passive sensors that can be read remotely. In this work, we model the external excitation of a resonator with different excitation signals and its subsequent decay characteristics analytically as well as numerically. The analytical modeling explains the properties of the resonator during transient response and decay behavior. The analytical modeling clarifies how natural oscillations are generated in a linear time-invariant system, even if their spectrum was not included in the stimulation spectrum. In addition, it enables the readout signals to be optimized in terms of duration and bandwidth.

Application of deep neural networks for inferring pressure in polymeric acoustic transponders/sensors.

Passive sensor-transponders have raised interest for the last few decades, due to their capability of low-cost remote monitoring without the need for energy storage. Their operating principle includes receiving a signal from a source and then reflecting the signal. While well-established transponders operate through electromagnetic antennas, those with a fully acoustic design have advantages such as lower cost and simplicity. Therefore, detection of pressures using the ultrasound signal that is backscattered from an acoustic resonator has been of interest recently. In order to infer the pressure from the backscattered signal, the established approach has been based upon the principle of detection of the shift to the frequency of resonance. Nevertheless, regression of the pressure from the signal with a small error is challenging and has been subject to research. Here in this paper, we explore an approach that employs deep learning for inferring pressure from the ultrasound reflections of polymeric resonators. We assess if neural network regressors can efficiently infer pressure reflected from a fully acoustic transponder. For this purpose, we compare the performance of several regressors such as a convolutional neural network, a network inspired by the ResNet, and a fully connected neural network. We observe that deep neural networks are advantageous in inferring pressure information with a minimal need for analyzing the signal. Our work suggests that a deep learning approach has the potential to be integrated with or replace other traditional approaches for inferring pressure from an ultrasound signal reflected from fully acoustic transponders or passive sensors.

Passive Impedance-Matched Neural Recording Systems for Improved Signal Sensitivity.

Wireless passive neural recording systems integrate sensory electrophysiological interfaces with a backscattering-based telemetry system. Despite the circuit simplicity and miniaturization with this topology, the high electrode-tissue impedance creates a major barrier to achieving high signal sensitivity and low telemetry power. In this paper, buffered impedance is utilized to address this limitation. The resulting passive telemetry-based wireless neural recording is implemented with thin flexible packages. Thus, the paper reports neural recording implants and integrator systems with three improved features: (1) passive high impedance matching with a simple buffer circuit, (2) a bypass capacitor to route the high frequency and improve mixer performance, and (3) system packaging with an integrated, flexible, biocompatible patch to capture the neural signal. The patch consists of a U-slot dual-band patch antenna that receives the transmitted power from the interrogator and backscatters the modulated carrier power at a different frequency. When the incoming power was 5-10 dBm, the neurosensor could communicate with the interrogator at a maximum distance of 5 cm. A biosignal as low as 80 µV peak was detected at the receiver.

Acceptability and feasibility of ecological momentary assessment with augmentation of passive sensor data in young adults at high risk for suicide.

Ecological Momentary Assessment (EMA) and wearable sensor data have the potential to enhance prediction of suicide risk in real-world conditions. However, the feasibility of this methodology with high-risk populations, including over extended periods, warrants closer attention. This study examined the feasibility and acceptability of concurrent EMA and wearable sensor monitoring in young adults after emergency department (ED) care for suicide risk-related concerns. For 2 months after ED discharge, 106 participants (ages 18-25; 81.1% female) took part in EMA surveys (4x per day) and passive sensor (Fitbit) monitoring and completed an end-of-study phone interview. Overall adherence to EMA (62.1%) and wearable sensor (53.6%) was moderate and comparable to briefer protocols. Relative to EMAs (81%), fewer participants completed the full 8 weeks of Fitbit (63%). While lower initial hopelessness was linked to reduced EMA adherence, previous-day suicidal ideation predicted lower Fitbit adherence on the next day. Self-endorsed barriers to EMA and wearable sensor adherence were also examined. Participants tended to report positive experience with the protocol, with majority indicating EMAs were minimally burdensome, reporting that the Fitbit was generally comfortable, and expressing interest in participating in a similar study again. Findings provide support for the feasibility and acceptability of concurrent intensive self-report and wearable sensor data during a high-risk period. Implications and future directions are discussed.

Inductive Coupling of Bipolar Signals with a Conjugate Coil Pair for an Analog Passive ECG Sensor Using a PPy-Coated pvCNT Dry Electrodes.

The wireless capture of analog differential signals from fully passive (battery-less) sensors is technically challenging but it can allow for the seamless capture of differential biosignals such as an electrocardiogram (ECG). This paper presents a novel design for the wireless capture of analog differential signals using a novel conjugate coil pair for a wireless resistive analog passive (WRAP) ECG sensor. Furthermore, we integrate this sensor with a new type of dry electrode, namely conductive polymer polypyrrole (PPy)-coated patterned vertical carbon nanotube (pvCNT) electrodes. The proposed circuit uses dual-gate depletion-mode MOSFETs to convert the differential biopotential signals to correlated drain-source resistance changes and the conjugate coil wirelessly transmits the differences of the two input signals. The circuit rejects (17.24 dB) common mode signals and passing only differential signals. We have integrated this novel design with our previously reported PPy-coated pvCNT dry ECG electrodes, fabricated on a stainless steel substrate with a diameter of 10 mm, which provided a zero-power (battery-less) ECG capture system for long duration monitoring. The scanner transmits an RF carrier signal at 8.37 MHz. The proposed ECG WRAP sensor uses only two complementary biopotential amplifier circuits, each of which has a single-depletion MOSFET. The amplitude-modulated RF signal is envelope-detected, filtered, amplified, and transmitted to a computer for signal processing. ECG signals are collected using this WRAP sensor and compared with a commercial counterpart. Due to the battery-less nature of the ECG WRAP sensor, it has the potential to be a body-worn electronic circuit patch with dry pvCNT electrodes that stably operate for a long period of time.

Flexible Passive Sensor Patch with Contactless Readout for Measurement of Human Body Temperature.

A passive flexible patch for human skin temperature measurement based on contact sensing and contactless interrogation is presented. The patch acts as an RLC resonant circuit embedding an inductive copper coil for magnetic coupling, a ceramic capacitor as the temperature-sensing element and an additional series inductor. The temperature affects the capacitance of the sensor and consequently the resonant frequency of the RLC circuit. Thanks to the additional inductor, the dependency of the resonant frequency from the bending of the patch has been reduced. Considering a curvature radius of the patch of up to 73 mm, the maximum relative variation in the resonant frequency has been reduced from 812 ppm to 7.5 ppm. The sensor has been contactlessly interrogated by a time-gated technique through an external readout coil electromagnetically coupled to the patch coil. The proposed system has been experimentally tested within the range of 32-46 °C, giving a sensitivity of -619.8 Hz/°C and a resolution of 0.06 °C.

Wireless Passive Sensor Technology through Electrically Conductive Media over an Acoustic Channel.

Hydrogen-based technologies provide a potential route to more climate-friendly mobility in the automotive and aviation industries. High-pressure tanks consisting of carbon-fiber-reinforced polymers (CFRPs) are exploited for the storage of compressed hydrogen and have to be monitored for safe and long-term operation. Since neither wired sensors nor wireless radio technology can be used inside these tanks, acoustic communication through the hull of the tank has been the subject of research in recent years. In this paper, we present for the first time a passive wireless sensor technology exploiting an ultrasonic communication channel through an electrically conductive transmission medium with an analog resonant sensor featuring a high quality factor. The instrumentation system comprised a readout unit outside and a passive sensor node inside the tank, coupled with geometrically opposing electromechanical transducers. The readout unit wirelessly excited a resonant sensor, whose temperature-dependent resonance frequency was extracted from the backscattered signal. This paper provides a description of the underlying passive sensor technology and characterizes the electric impedances and acoustic transmission as an electrical 2-Port to design a functional measurement setup. We demonstrated a wireless temperature measurement through a 10 mm CFRP plate in its full operable temperature range from -40 to 110 °C with a resolution of less than 1 mK.

Ultrasoft and Biocompatible Magnetic-Hydrogel-Based Strain Sensors for Wireless Passive Biomechanical Monitoring.

Implantable flexible mechanical sensors have exhibited great potential in health monitoring and disease diagnosis due to continuous and real-time monitoring capability. However, the wires and power supply required in current devices cause inconvenience and potential risks. Magnetic-based devices have demonstrated advantages in wireless and passive sensing, but the mismatched mechanical properties, poor biocompatibility, and insufficient sensitivity have limited their applications in biomechanical monitoring. Here, a wireless and passive flexible magnetic-based strain sensor based on a gelatin methacrylate/Fe3O4 magnetic hydrogel has been fabricated. The sensor exhibits ultrasoft mechanical properties, strong magnetic properties, and long-term stability in saline solution and can monitor strains down to 50 µm. A model of the sensing process is established to identify the optimal detection location and the relation between the relative magnetic permeability and the sensitivity of the sensors. Moreover, an in vitro tissue model is developed to investigate the potential of the sensor in detecting subtle biomechanical signals and avoiding interference with bioactivities. Furthermore, a real-time and high-throughput biomonitoring platform is built and implements passive wireless monitoring of the drug response and cultural status of the cardiomyocytes. This work demonstrates the potential of applying magnetic sensing for biomechanical monitoring and provides ideas for the design of wireless and passive implantable devices.

Signal Source Localization with Long-Term Observations in Distributed Angle-Only Sensors.

Angle-only sensors cannot provide range information of targets and in order to determine accurate position of a signal source, one can connect distributed passive sensors with communication links and implement a fusion algorithm to estimate target position. To measure moving targets with sensors on moving platforms, most of existing algorithms resort to the filtering method. In this paper, we present two fusion algorithms to estimate both the position and velocity of moving target with distributed angle-only sensors in motion. The first algorithm is termed as the gross least square (LS) algorithm, which takes all observations from distributed sensors together to form an estimate of the position and velocity and thus needs a huge communication cost and a huge computation cost. The second algorithm is termed as the linear LS algorithm, which approximates locations of sensors, locations of targets, and angle-only measures for each sensor by linear models and thus does not need each local sensors to transmit raw data of angle-only observations, resulting in a lower communication cost between sensors and then a lower computation cost at the fusion center. Based on the second algorithm, a truncated LS algorithm, which estimates the target velocity through an average operation, is also presented. Numerical results indicate that the gross LS algorithm, without linear approximation operation, often benefits from more observations, whereas the linear LS algorithm and the truncated LS algorithm, both bear lower communication and computation costs, may endure performance loss if the observations are collected in a long period such that the linear approximation model becomes mismatch.

Low-Cost Nonreversible Electronic-Free Wireless pH Sensor for Spoilage Detection in Packaged Meat Products.

Contamination of meat with pathogenic microorganisms can cause severe illnesses and food waste, which has significant negative impacts on both general health and the economy. In many cases, the expiration date is not a good indicator of meat freshness as there is a high risk of contamination during handling throughout the supply chain. Many biomarkers, including color, odor, pH, temperature, and volatile compounds, are used to determine spoilage. Among these, pH presents a simple and effective biomarker directly linked to the overgrowth of bacteria and degradation of the meat tissue. Low-cost methods for wireless pH monitoring are crucial in detecting spoilage on a large commercial scale. Existing technologies are often limited to short-range detection, with the use of batteries and different electronic components that increases both the manufacturing complexity and cost of the final device. To address these shortcomings, we have developed a cost-effective wireless pH sensor, which uses passive resonant frequency (RF) sensing, combined with a pH-responsive polymer that can be placed within packaged meat products and provide a remote assessment of the risk of microbial spoilage throughout the supply chain. The sensor tag consists of a sensing resonator coated with a pH-sensitive material and a passivated reference resonator operating in a differential frequency configuration. Upon exposure to elevated pH levels >6.8, the coating on the sensing resonator dissolves, which in turn results in a distinct change in the resonant frequency with respect to the reference resonator. Systematic theoretical and experimental results at different pH levels demonstrated that a 20% shift in resonant frequency demarcates the point for spoilage detection. As a proof of concept, the performance of the sensor in remotely detecting the risk of food spoilage was validated in packaged poultry over 10 days. The sensor fabrication process takes advantage of recent developments in the scalable manufacturing of flexible, low-cost devices, including selective laser etching of metalized plastic films and doctor-blade coating of stimuli-responsive polymer films. Furthermore, the biocompatibility of all the materials used in the sensor was confirmed with human intestinal cells (HCT-8 cells).

Novel Surface Acoustic Wave Temperature-Strain Sensor Based on LiNbO3 for Structural Health Monitoring.

In this paper, we present the design of an integrated temperature and strain dual-parameter sensor based on surface acoustic waves (SAWs). First, the COMSOL Multiphysics simulation software is used to determine separate frequencies for multiple sensors to avoid interference from their frequency offsets caused by external physical quantity changes. The sensor consists of two parts, a temperature-sensitive unit and strain-sensitive unit, with frequencies of 94.97 MHz and 90.05 MHz, respectively. We use standard photolithography and ion beam etching technology to fabricate the SAW temperature-strain dual-parameter sensor. The sensing performance is tested in the ranges 0-250 °C and 0-700 µÔ‘. The temperature sensor monitors the ambient temperature in real time, and the strain sensor detects both strain and temperature. By testing the response of the strain sensor at different temperatures, the strain and temperature are decoupled through the polynomial fitting of the intercept and slope. The relationship between the strain and the frequency of the strain-sensitive unit is linear, the linear correlation is 0.98842, and the sensitivity is 100 Hz/µÔ‘ at room temperature in the range of 0-700 µÔ‘. The relationship between the temperature and the frequency of the temperature-sensitive unit is linear, the linearity of the fitting curve is 0.99716, and the sensitivity is 7.62 kHz/°C in the range of 25-250 °C. This sensor has potential for use in closed environments such as natural gas or oil pipelines.

Signal Source Positioning Based on Angle-Only Measurements in Passive Sensor Networks.

Some passive sensors can measure only directions of arrival of signals, but the real positions of signal sources are often desirable, which can be estimated by combining distributed passive sensors as a network. However, passive observations should be correctly associated first. This paper studies the multi-target data association and signal localization problem in distributed passive sensor networks. With angle-only measurements from distributed passive sensors, multiple lines in a 3-dimensional (3D) scenario can be built and then those that will intersect in a small volume in 3D are classified into the same source. The center of the small volume is taken as an estimate of the signal source position, whose statistical distributions are formulated. If the minimum distance is less than an association threshold, then two lines are considered to be from the same signal source. In numerical results, the impacts of angle measurement accuracy and platform self-positioning accuracy are analyzed, indicating that this method can achieve a prescribed data association rate and a high positioning performance with a low computation cost.

Sorption of G-agent simulant vapours on human scalp hair.

Human scalp hair is a biological matrix that can trap chemical vapours from explosives (TNT), drugs (THC) and chemical weapons (yperite). The external contamination of human's hair following exposure to organophosphorus (OP) nerve agent was simulated by model compounds: triethyl phosphate (TEP) and diisopropyl fluorophosphate (DFP). In this work were exposed strands of hair to vapours of TEP and DFP (3 and 7 ppmv) to model sorption kinetics. Sorption isotherms were also investigated at several contamination levels (80-3000 mg min.m-3). OP nerve agent simulants were extracted from hair by soaking in DCM. Raw extracts were analysed in GC-MS/MS to quantify each simulant content in hair. Results were fitted by applying isotherm or kinetic equations. The best model was found to be bimodal first-order, suggesting the co-existence of two different mechanisms of sorption. The best equation to describe OP vapours incorporation on hair was Freundlich model. Thus hair can be used as a passive sensor able to trap chemical G-agents and can also offer valuable information regarding both individual contamination and proof of exposure to chemical weapons.

In-Home Passive Sensor Data Collection and Its Implications for Social Media Research: Perspectives of Community Women in Rural South Africa.

There has been a recent increase in debates on the ethics of social media research, passive sensor data collection, and big data analytics. However, little evidence exists to describe how people experience and understand these applications of technology. This study aimed to passively collect data from mobile phone sensors, lapel cameras, and Bluetooth beacons to assess people's understanding and acceptance of these technologies. Seven households were purposefully sampled and data collected for 10 days. The study generated 48 hr of audio data and 30,000 images. After participant review, the data were destroyed and in-depth interviews conducted. Participants found the data collected acceptable and reported willingness to participate in similar studies. Key risks included that the camera could capture nudity and sex acts, but family review of footage before sharing helped reduce concerns. The Emanuel et al. ethics framework was found to accommodate the concerns and perspectives of study participants.

Linear-Time Direct Data Assignment Algorithm for Passive Sensor Measurements.

To solve the problem of passive sensor data association in multi-sensor multi-target tracking, a novel linear-time direct data assignment (DDA) algorithm is proposed in this paper. Different from existing methods which solve the data association problem in the measurement domain, the proposed algorithm solves the problem directly in the target state domain. The number and state of candidate targets are preset in the region of interest, which can avoid the problem of combinational explosion. The time complexity of the proposed algorithm is linear with the number of sensors and targets while that of the existing algorithms are exponential. Computer simulations show that the proposed algorithm can achieve almost the same association accuracy as the existing algorithms, but the time consumption can be significantly reduced.

Sources of Variation in Assessing Canopy Reflectance of Processing Tomato by Means of Multispectral Radiometry.

Canopy reflectance sensors are a viable technology to optimize the fertilization management of crops. In this research, canopy reflectance was measured through a passive sensor to evaluate the effects of either crop features (N fertilization, soil mulching, appearance of red fruits, and cultivars) or sampling methods (sampling size, sensor position, and hour of sampling) on the reliability of vegetation indices (VIs). Sixteen VIs were derived, including seven simple wavelength reflectance ratios (NIR/R460, NIR/R510, NIR/R560, NIR/R610, NIR/R660, NIR/R710, NIR/R760), seven normalized indices (NDVI, G-NDVI, MCARISAVI, OSAVI, TSAVI, TCARI), and two combined indices (TCARI/OSAVI; MCARI/OSAVI). NIR/560 and G-NDVI (Normalized Difference Vegetation Index on Greenness) were the most reliable in discriminating among fertilization rates, with results unaffected by the appearance of maturing fruits, and the most stable in response to different cultivars. Black mulching film did not affect NIR/560 and G-NDVI behavior at the beginning of the growing season, when the crop is more responsive to N management. Due to a moderate variability of NIR/560 and G-NDVI, a small sample size (5-10 observations) is sufficient to obtain reliable measurements. Performing the measurements at 11:00 and 14:00 and maintaining a greater distance (1.8 m) between plants and instrument enhanced measurement consistency. Accordingly, NIR/560 and G-NDVI resulted in the most reliable VIs.

Flexible UHF RFID Tag for Blood Tubes Monitoring.

Low-cost and flexible radio frequency identification (RFID) tag for automatic identification, tracking, and monitoring of blood products is in great demand by the healthcare industry. A robust performance to meet security and traceability requirements in the different blood sample collection and analysis centers is also required. In this paper, a novel low-cost and flexible passive RFID tag is presented for blood sample collection tubes. The tag antenna is based on two compact symmetrical capacitive structures and works at the ultra-high frequency (UHF) European band (865 MHz-868 MHz). The tag antenna is designed considering the whole dielectric parameters such as the blood, substrate and tube. In this way, it operates efficiently in the presence of blood, which has high dielectric permittivity and loss. Measurement results of the proposed tag have confirmed simulation results. The measured performance of the tag shows good matching in the desired frequency band, leading to reading ranges up to 2.2 m, which is 4.4 times higher than typical commercial tags. The potential of this tag as a sensor to monitor the amount of blood contained in clinic tubes is also demonstrated. It is expected that the proposed tag can be useful and effective in future RFID systems to introduce security and traceability in different blood sample collection and analysis centers.

Ultrasonic Power and Data Transfer through Multiple Curved Layers Applied to Pipe Instrumentation.

Ultrasonic power and data transfer through multilayered curved walls was investigated using numerical and experimental analysis. The acoustic channel used in this paper was formed by two concentric pipes filled with water, aiming for applications that involve powering and monitoring sensors installed behind the pipe walls. The analysis was carried out in the frequency and time domains using numerical and experimental models. Power and data were effectively simultaneously transferred through the channel. A remote temperature and pressure sensor was powered and interrogated throughout all the layers, and the power insertion loss was 10.72 dB with a data transmission rate of 1200 bps using an amplitude modulated scheme with Manchester coding. The efficiency of the channel was evaluated through an experimental analysis of the bit error rate (BER) with different values of signal-to-noise ratio (SNR), showing a decrease in the number of errors compared with detection without Manchester coding.