Unobtrusive Sensors for Synchronous Monitoring of Different Breathing Parameters in Care Environments.

Respiratory problems are common amongst older people. The rapid increase in the ageing population has led to a need for developing technologies that can monitor such conditions unobtrusively. This paper presents a novel study that investigates Wi-Fi and ultra-wideband (UWB) antenna sensors to simultaneously monitor two different breathing parameters: respiratory rate, and exhaled breath. Experiments were carried out with two subjects undergoing three breathing cases in breaths per minute (BPM): (1) slow breathing (12 BPM), (2) moderate breathing (20 BPM), and (3) fast breathing (28 BPM). Respiratory rates were captured by Wi-Fi sensors, and the data were processed to extract the respiration rates and compared with a metronome that controlled the subjects' breathing. On the other hand, exhaled breath data were captured by a UWB antenna using a vector network analyser (VNA). Corresponding reflection coefficient data (S11) were obtained from the subjects at the time of exhalation and compared with S11 in free space. The exhaled breath data from the UWB antenna were compared with relative humidity, which was measured with a digital psychrometer during the breathing exercises to determine whether a correlation existed between the exhaled breath's water vapour content and recorded S11 data. Finally, captured respiratory rate and exhaled breath data from the antenna sensors were compared to determine whether a correlation existed between the two parameters. The results showed that the antenna sensors were capable of capturing both parameters simultaneously. However, it was found that the two parameters were uncorrelated and independent of one another.

Dual-Planar Monopole Antenna-Based Remote Sensing System for Microwave Medical Applications.

Neurodegenerative diseases (NDs) can be life threatening and have chronic impacts on patients and society. Timely diagnosis and treatment are imperative to prevent deterioration. Conventional imaging modalities, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET), are expensive and not readily accessible to patients. Microwave sensing and imaging (MSI) systems are promising tools for monitoring pathological changes, namely the lateral ventricle enlargement associated with ND, in a non-invasive and convenient way. This paper presents a dual-planar monopole antenna-based remote sensing system for ND monitoring. First, planar monopole antennas were designed using the simulation software CST Studio Suite. The antenna analysis was carried out regarding the reflection coefficient, gain, radiation pattern, time domain characterization, E-field distribution, and Specific Absorption Rate (SAR). The designed antennas were then integrated with a controlling circuit as a remote sensing system. The system was experimentally validated on brain phantoms using a vector network analyzer and a laptop. The collected reflection coefficient data were processed using a radar-based imaging algorithm to reconstruct images indicating brain abnormality in ND. The results suggest that the system could serve as a low-cost and efficient tool for long-term monitoring of ND, particularly in clinics and care home scenarios.

Microwave sensing dataset for noninvasive monitoring of ventricle enlargement due to Alzheimer's disease.

This paper presents a dataset generated from a comprehensive study on the potential of microwave imaging for early detection or monitoring of different stages of Alzheimer's disease. The study includes collecting and analyzing frequency-domain data using a radar-based head imaging system. The data was obtained from lamb brain phantoms designed to mimic lateral ventricle enlargement, a common symptom of Alzheimer's disease. The article provides detailed descriptions of the data collection method, experimental setup, and different phantoms used. Additionally, the article highlights the importance and potential of the dataset to be used for evaluating and validating new signal processing and imaging techniques. The dataset includes magnitude and phase information for both reflected and transmitted signals making it useful to evaluate radar-based signal processing and imaging techniques. The dataset is open-source and available to the scientific community, providing a valuable resource for researchers to advance their understanding of the potential use of microwave imaging techniques for detecting or monitoring Alzheimer's disease.

Evaluation of Unobtrusive Microwave Sensors in Healthcare 4.0-Toward the Creation of Digital-Twin Model.

The prevalence of chronic diseases and the rapid rise in the aging population are some of the major challenges in our society. The utilization of the latest and unique technologies to provide fast, accurate, and economical ways to collect and process data is inevitable. Industry 4.0 (I4.0) is a trend toward automation and data exchange. The utilization of the same concept of I4.0 in healthcare is termed Healthcare 4.0 (H4.0). Digital Twin (DT) technology is an exciting and open research field in healthcare. DT can provide better healthcare in terms of improved patient monitoring, better disease diagnosis, the detection of falls in stroke patients, and the analysis of abnormalities in breathing patterns, and it is suitable for pre- and post-surgery routines to reduce surgery complications and improve recovery. Accurate data collection is not only important in medical diagnoses and procedures but also in the creation of healthcare DT models. Health-related data acquisition by unobtrusive microwave sensing is considered a cornerstone of health informatics. This paper presents the 3D modeling and analysis of unobtrusive microwave sensors in a digital care-home model. The sensor is studied for its performance and data-collection capability with regards to patients in care-home environments.

Experimental radar data for monitoring brain atrophy progression.

This data set contains complex frequency domain signals obtained from unidirectional antennas mainly fabricated for radar-based head imaging. Data were obtained as part of a project investigating radar-based microwave imaging for monitoring neurodegenerative diseases, especially Alzheimer's disease. The wearable device, measurement setup, and phantoms used are described. Multiple experiments were performed to get the data from three lamb brain phantoms that realistically mimic the whole-brain atrophy due to Alzheimer's disease. Microwave imaging has shown great potential for breast and brain screening due to its low cost, non-ionizing, portable, and wearable nature. Most of the studies are based on simulations with good results, but further evaluation on experimental data is required before its clinical viability. This work provides an open-source experimental dataset that can be used to evaluate novel signal processing and imaging techniques and validate simulation results. The data provide both the magnitude and phase value at each discrete frequency, making this data set useful for both time-delay and phase-shift based imaging algorithms.

Non-Invasive RF Technique for Detecting Different Stages of Alzheimer's Disease and Imaging Beta-Amyloid Plaques and Tau Tangles in the Brain.

This paper describes a novel approach of detecting different stages of Alzheimer's disease (AD) and imaging beta-amyloid plaques and tau tangles in the brain using RF sensors. Dielectric measurements were obtained from grey matter and white matter regions of brain tissues with severe AD pathology at a frequency range of 200 MHz to 3 GHz using a vector network analyzer and dielectric probe. Computational models were created on CST Microwave Suite using a realistic head model and the measured dielectric properties to represent affected brain regions at different stages of AD. Simulations were carried out to test the performance of the RF sensors. Experiments were performed using textile-based RF sensors on fabricated phantoms, representing a human brain with different volumes of AD-affected brain tissues. Experimental data was collected from the sensors and processed in an imaging algorithm to reconstruct images of the affected areas in the brain. Measured dielectric properties in brain tissues with AD pathology were found to be different from healthy human brain tissues. Simulation and experimental results indicated a correlated shift in the captured reflection coefficient data from RF sensors as the amount of affected brain regions increased. Finally, images reconstructed from the imaging algorithm successfully highlighted areas of the brain affected by plaques and tangles as a result of AD. The results from this study show that RF sensing can be used to identify areas of the brain affected by AD pathology. This provides a promising new non-invasive technique for monitoring the progression of AD.

Integrated Flexible Hybrid Silicone-Textile Dual-Resonant Sensors and Switching Circuit for Wearable Neurodegeneration Monitoring Systems.

This paper describes the design, development, and testing of flexible hybrid silicone-textile sensors and a flexible switching circuit that were integrated into a wearable system for monitoring neurodegenerative diseases. A total of 6 planar monopole antenna sensors were fabricated that propagates at two separate resonant frequencies: 800 MHz and 2.1 GHz respectively. In addition, 2 switching circuits, each having 3 switches and 4 SMA breakout boards, were assembled and placed on the wearable neurodegeneration monitoring system. Each switching circuit connects 3 sensors to a single port on a vector network analyzer (VNA) that is used to generate and receive microwave signals. Experiments were performed using the wearable device with the developed sensors and switching circuit on phantoms mimicking two common physiological changes in the brain caused by neurodegenerative diseases: 1) brain atrophy and 2) lateral ventricle enlargement. The dual nature of the sensors' resonance allows it to detect both brain atrophy and lateral ventricle enlargement separately at different operating frequency. This provides the advantage of minimizing the number of sensor elements needed to monitor neurodegenerative disease. The use of a switching circuit also allows for quick and convenient measurements by choosing which sensors are active for ports 1 and 2 on the VNA respectively. In addition to being low-cost, the flexibility of the materials used in fabrication allows the sensors and switching circuit to be conformal to the patient's head. Results from the experiments indicates that the sensors and switching circuit were working successfully when integrated into the wearable device.