The integration of wireless sensors into the automotive industry is making vehicles increasingly safe and autonomous. At present, vehicles are equipped with external sensors that facilitate maneuverability and braking and internal sensors that detect presence, seat belt state, and occupant vital signs. Early detection of dysfunctional breathing patterns can reduce the risk of accidents caused by drowsiness or fatigue. In this regard, this work presents a MIMO FMCW Radar System able to distinguish dysfunctional breathing patterns from human torso motion. The torso has been divided into six regions, and the amplitude of the elongation and the frequency of motion of each region have been measured during breathing. An elongation map is then constructed in order to graphically show the dysfunctional breathing patterns. The signal processing includes a peak search algorithm to detect elongation amplitude and a band pass frequency filter to minimize DC-component, random driver motion, and vehicle vibration. A torso elongation emulator phantom has been assembled with materials of skin-like relative permittivity and DC motors for calibration and validation of basic breathing patterns. Finally, the signal of the system is compared with that of a commercial respiration belt, and then the system is tested by performing measurements on people. The MIMO radar system is able to measure, differentiate, and classify patterns associated with dysfunctional breathing such as hyperventilation syndrome and thoracic dominant breathing.

The combination of microwave and microfluidic technologies has the potential to enable wireless monitoring and interaction with bioparticles, facilitating, in this way, the exploration of a still largely uncharted territory at the intersection of biology, communication engineering, and microscale physics. Opportunely, the scientific and technical requirements of microfluidics and microwave techniques converge to the need for system miniaturization to achieve the required sensitivity levels. This work, therefore, presents the design and optimization of a measurement system for the detection of bioparticles over the frequency range of 0.01–10 GHz with different coplanar electrodes’ configurations on a microfluidic platform. The design of the measurement signal-chain setup is optimized for a novel real-time superheterodyne microwave detection system. In particular, signal integrity is achieved by means of a microwave-shielded chamber, which is protected from external electromagnetic interference that may potentially impact the coplanar electrodes mounted on the microfluidic device. In addition, analytical expressions and experimental validation of the system-level performance are provided and discussed for the different designs of the coplanar electrodes. This technique is applied to measure the electrical field perturbation produced by 10- $\mu \text{m}$ polystyrene beads with a concentration of $10^{5}$ beads/mL and flowing at a rate of 10 $\mu \text{L}$ /min. The achieved SNR is in the order of 40 dB for the three coplanar electrodes considered.

The combination of microwave and microfluidic technologies enables the creation of efficient platforms to rapidly quantify live/dead bioparticles in industrial processes, biomedical research and environmental monitoring applications. In this regard, this work demonstrates the differentiation of single live/dead bioparticles with a microwave-microfluidic platform. The system is composed of a contactless current-sensing superheterodyne architecture with broadband frequency-operation bowtie electrodes. Furthermore, the 20mm × 20mm bowtie electrode has been designed and manufactured with a –10 dB frequency range, from 4GHz to 8GHz, and capable of confining the electric field in a micrometric volume. The gap of the bowtie geometry of the electrodes is adjusted to the dimensions of the 50 μm × 50 μm cross-section and 40mm long microfluidic channel. In particular, 10mL sample with 10 ⁹ bacteria/mL flows through the microchannel at 19.8 μL/min, focused with elasto-inertial effect enhanced with 500 ppm of polyethylene oxide. Accordingly, from the signal captured by the electrodes, the energy variation of the applied electric field produced by the bioparticles is sensed by a current-based transimpedance amplifier. Here, the incorporation of the transimpedance amplifier in the measurement system improves the SNR by 4 dB. In addition, the superheterodyne transceiver has been optimized in terms of local oscillator power and bandwidth. Furthermore, the microfluidic system has been adjusted so that the balance of hydrodynamic forces confines bioparticles at the centerline of the microchannel within less than 5 μm. As a result, the system measures the cytoplasmic content of live and dead flowing bacteria with a difference of 6 dB and relative deviation of 3.2%.

Microwaves have proven their imaging capabilities to visualize the body composition for medical applications thanks to their penetration inside biological structures. In this context, this paper presents a novel methodology that aims to extract not just the internal morphology but also the brain’s functional activity using the UWB Pulse Amplitude Modulation (PAM) technique to have simultaneously functional monitoring and imaging capability and apply it to monitor the Parkinson’s disease. The radiofrequency system is composed of two orthogonal sets of double UWB probes operating in the frequency range of 0.5 GHz - 1.5 GHz. An experimental set-up has been devised that avoids complex in-vivo testing, albeit allows a system proof-of-concept validation. A bio-tag consisting of an optically modulated photodiode is used to emulate local medium changes associated to cell activity. The proposed system is used to first extract the modeled brain Action Potential (AP) to validate the performance of the bio-tag, and then to monitor the Parkinson’s disease (PD) based on the beta frequency band character within basal ganglia–thalamocortical (BGTC) which is a key marker for the PD. The results show a good capability of locating and differentiating the signals generated within the phantom by the bio-tag, alternatively emulating the healthy and PD’s state, based on the frequency. The obtained results of the functional monitoring technique on distinguishing the healthy from non-healthy brain model activity, as well as in the phantom mimicking the average proprieties of a human head, will serve as a basis for detecting functional diseases in the future.

Factors such as road traffic, challenging ambient temperature conditions, and extended periods of driving have detrimental effects on the physical and mental well-being of a driver. These factors can alter the stress levels, thereby diminishing his or her capacity to make effective decisions when faced with hazardous situations on the road. In this regard, this study presents a novel approach utilizing a MIMO radar system to accurately assess driver stress levels by measuring both physiological signals and driving behavior. The proposed method, assisted by a machine learning technique, provides comprehensive insights to classify the stress level of a driver into three states: drowsiness, awakeness, and anxiety. The MIMO radar system captures the elongation distance and velocity of six specific regions of the frontal torso of the driver in an advanced driving simulator based on virtual reality. This allows for the extraction of vital physiological parameters such as heart rate, respiratory rhythm, and breathing patterns over time, as well as the identification of changes in driving style determined by variations in their relative position in the seat and control of the steering wheel. Then, a fully-connected neural network model is trained with the acquired data, and its performance is evaluated with volunteers submitted to different driving situations that induce stress in the driver. The findings show an accuracy in drowsiness detection of 90%, awakeness of 96% and anxiety of 85%.

This study proposes a new method for the detection of facial expressions of pain using a 3D profiler that combines a multiple-input-multiple-output (MIMO) radar system with a machine learning (ML) model (ML-MIMO radar profiler). It offers a promising solution for the pain detection of facial expressions in a non-invasive, non-intrusive, and cost-effective manner. The ML-MIMO radar profiler employs six radars behind a lens to monitor changes in six facial regions and to build a 3D facial profile with real-time facial activity information. A dielectric lens was used to ensure an optimal beam size to effectively illuminate each face region. Signal processing is performed using dynamic time deformation to determine the longitudinal distance and a discrete stationarywavelet transform to filter the signal and improve accuracy. The information from the 3D profiler was compared with the facial action coding system (FACS) to determine actual facial expressions. A machine learning algorithm was trained to learn action units from the FACS and compare them with the information provided by the ML-MIMO radar profiler, thus performing facial expression classification. In this study, we analyzed four facial expressions: joy, sadness, anger, and pain. Identification and classification were performed using a machine-learning model based on multilayer perceptrons. The results revealed 92% accuracy of the system for pain expression, whereas expressions of happiness, sadness, and anger were detected with 88, 86, and 87% accuracy, respectively.