Electromagnetic radars have been shown potentially to be used for remote sensing of biosignals in a more comfortable and easier way than wearable and contact devices. While there is an increasing interest in using radars for health monitoring, their performance has not been tested and reported either in practical scenarios or with acceptable low errors. Therefore, we use a frequency modulated continuous wave (FMCW) radar operating at 77 GHz in a bedroom environment to extract the respiration and heart rates of a patient who is used to lying down on the bed. Indeed, the proposed signal processing contains advanced phase unwrapping manipulation which is unique. In addition, the results are compared with a reliable reference sensor. Our results show that the correlations between the reference sensor and the radar estimates are in 94% and 80% for breathing and heart rates, respectively.

Planar microwave sensors are considered an attractive choice to non-invasively probe the dielectric attributes of biological tissues due to their low cost, simple fabrication, miniature scale, and minimum risk to human health. This paper develops and measures a novel microwave biosensor for non-invasive real-time monitoring of glucose level. The design comprises a rectangular plexiglass channel integrated on a triple-pole complementary split ring resonator (TP-CSRR). The proposed sensor operates in the centimeter-wave range 1 — 6 GHz and is manufactured using PCB on top of an FR4 dielectric substrate. The sensor elements are excited via a coupled microstrip transmission-line etched on the bottom side of the substrate. The integrated CSRR-based sensor is used as a near-field probe to non-invasively monitor the glucose level changes in the blood mimicking solutions of clinically relevant concentrations to Type-2 normal diabetes (70 — 120 mg/dL), by recording the frequency response of the harmonic reflection and transmission resonances. This indicates the sensor's capability of detecting small variations in the dielectric properties of the blood samples that are responsive to the electromagnetic fields. The proposed sensor is verified through practical measurements of the fabricated design. Experimental results obtained using a Vector Network Analyzer (VNA) demonstrate a sensitivity performance of about 6.2 dB/(mg/ml) for the developed triple-pole sensor that significantly outperforms the conventional single-pole and other proposed sensors in the literature in terms of the resonance amplitude resolution.

This paper details the development of a gesture recognition technique using a mm-wave radar sensor for in-car infotainment control. Gesture recognition is becoming a more prominent form of human-computer interaction, and can be used in the automotive industry to provide a safe and intuitive control interface that will limit driver distraction. We use a 60 GHz mm-wave radar sensor to detect precise features of fine motion. Specific gesture features are extracted and used to build a machine learning engine that can perform real-time gesture recognition. This paper discusses the user requirements and in-car environmental constraints that influenced design decisions. Accuracy results of the technique are presented, and recommendations for further research and improvements are made.

This paper presents a high-sensitive planar microwave sensor based on the metamaterial technology, integrated with a microfluidic channel to be used for liquid sensing in a variety of biomedical applications. The sensor design comprises three cells of circular complementary split ring resonators (CSRRs) engraved on the ground plane of a dielectric substrate in a cascaded configuration. The sensing cells are excited with a time-varying electric field coupled from a planar microstrip-line (MTL) etched on the bottom side of the substrate. The proposed design of multiple coupled resonators would enhance the electric field intensity over a larger sensing region through the inter-resonator coupling, thus improving the overall sensitivity for detecting liquid samples of high permittivity and dielectric losses. The sensor exhibits a reject (band stop) filter behaviour with multiple resonances in the centimeter-wave band 1 – 6 GHz when loaded symmetrically with liquid samples. The sensor is fabricated on an FR4 dielectric substrate with a biocompatible microfluidic channel aligned appropriately over the sensing area to enable consistent and intact sensing of the liquid samples. The CSSR-sensor is numerically modeled and compared in performance to the single-cell CSRR for detecting small variations in the dielectric properties. The sensitivity, reliability, and repeatability of the proposed sensor are practically demonstrated by the in-lab measurements for different liquid samples using a Vector Network Analyzer (VNA) setup where distinctive resonant features (amplitude and frequency) are extracted from multiple resonance modes in the reflection and transmission responses. A high sensitivity is also demonstrated for monitoring glucose level variations in synthetic blood samples of concentrations (70 – 150 mg/dL) which are clinically-relevant to diabetes conditions. Beside its impressive capability in detecting small dielectric variations, the developed sensor features other favorable attributes including compact size, simple fabrication, affordable cost, non-ionizing nature, and minimal health risk or impact. Such key advantages could potentially promote the proposed sensor for integration with other microwave components in an embedded device for non-invasive monitoring of blood glucose for diabetes.

In this work, we present a cloud-based system for noncontact, real-time recognition, and monitoring of physical activities and walking periods within a domestic environment. The proposed system employs standalone Internet of Things (IoT)-based millimeter wave radar devices and deep learning models to enable autonomous, free-living activity recognition, and gait analysis. To train deep learning models, we utilize range-Doppler maps generated from a data set of real-life in-home activities. The performance of several deep learning models is evaluated based on accuracy and prediction time, with the gated recurrent network [gated recurrent unit (GRU)] model selected for real-time deployment due to its balance of speed and accuracy compared to 2-D convolutional neural network long short-term memory (2D-CNNLSTM) and long short-term memory (LSTM) models. The overall accuracy of the GRU model for classifying in-home physical activities of trained subjects is 93%, with 86% accuracy for a new subject. In addition to recognizing and differentiating various activities and walking periods, the system also records the subject’s activity level over time, washroom use frequency, sleep/sedentary/active/out-of-home durations, current state, and gait parameters. Importantly, the system maintains privacy by not requiring the subject to wear or carry any additional devices.

In this article, a novel sensing approach is presented for glucose level monitoring where a robust low-power millimeter(mm)-wave radar system is used to differentiate between blood samples of disparate glucose concentrations in the range 0.5 to 3.5 mg/mL. The proposed radar sensing mechanism shows greater capabilities for remote detection of blood glucose inside test tubes through detecting minute changes in their dielectric properties. In particular, the reflected mm-waves that represent unique signatures for the internal synthesis and composition of the tested blood samples, are collected from the multi-channels of the radar and analyzed using signal processing techniques to identify different glucose concentrations and correlate them to the reflected mm-wave readings. The mm-wave spectrum is chosen for glucose sensing in this study after a set of preliminary experiments that investigated the dielectric permittivity behavior of glucose-loaded solutions across different frequency bands. In this regard, a newly-developed commercial coaxial probe kit (DAK-TL) is used to characterize the electromagnetic properties of glucose-loaded samples in a broad range of frequencies from 300 MHz to 67 GHz using two different 50 Ω open-coaxial probes. This would help to determine the portion of the frequency spectrum that is more sensitive to slight variations in glucose concentrations as indicated by the amount of change in the dielectric constant and loss tangent parameters due to the different concentrations under test. The mm-wave frequency range 50 to 67 GHz has shown to be promising for acquiring both high sensitivity and sufficient penetration depth for the most interaction between the glucose molecules and electromagnetic waves. The processed results have indicated the reliability of using mm-wave radars in identifying changes in blood glucose levels while monitoring trends among those variations. Particularly, blood samples of higher glucose concentrations are correlated with reflected mm-wave signals of greater energy. The proposed system could likely be adapted in the future as a portable non-invasive continuous blood glucose level monitoring for daily use by diabetics.