Real-time Respiration Measurement during Sleep Using a Microwave Sensor

A Voxel Vascular Structure-based Mannequin-like Arm Electromagnetic Model for Radio Frequency Biomedical Sensors

Radio Frequency (RF) sensor is widely used to monitor physiological signals. Generally, RF sensor simulation is mostly done using a layered model, which sometimes cannot model the accurate properties in the real world. A voxel vascular structure-based mannequin-like arm electromagnetic model (VVS-MaM) is proposed to evaluate the RF sensor, which mainly gathers the real physiological signal. This model is built with high-precision Magnetic Resonance Imaging (MRI), and it can finish fast simulation while there is also a voxel-like part in it which means it has the advantages of both the layered model and the real human model. After modelling, both simulation and in-vivo experiments are designed to test this sensor. In the simulation, the simulated standard resonant frequency of the equivalent model is 1.8137 GHz, and the relative error of the VVS-MaM is 0.012 GHz, which is closer to the standard value than the layer model result of 0.049 GHz. Meanwhile, in the in-vivo experiments, an RF sensor based on a composite right/left-handed transmission line (CRLH-TL) and complementary split resonator rings (CSRRs) are fabricated, and the measurements from the real experiments are gathered and stored to compare with that of the simulation. The comparison shows that the relative error of the VVS-MaM (0.08804 GHz)is closer to the in-vivo measurements than that of the layer model (0.09891 GHz), which validates the performance of VVS-MaM.Clinical Relevance-Radio Frequency, magnetic resonance imaging, scattering parameter, composite right/left-handed, complementary split resonator ring.

Trends in Passive IoT Biomarker Monitoring and Machine Learning for Cardiovascular Disease Management in the U.S. Elderly Population

It is predicted that the growth in the U.S. elderly population alongside continued growth in chronic disease prevalence will further strain an already overburdened healthcare system and could compromise the delivery of equitable care. Current trends in technology are demonstrating successful application of artificial intelligence (AI) and machine learning (ML) to biomarkers of cardiovascular disease (CVD) using longitudinal data collected passively from internet-of-things (IoT) platforms deployed among the elderly population. These systems are growing in sophistication and deployed across evermore use-cases, presenting new opportunities and challenges for innovators and caregivers alike. IoT sensor development that incorporates greater levels of passivity will increase the likelihood of continued growth in device adoption among the geriatric population for longitudinal health data collection which will benefit a variety of CVD applications. This growth in IoT sensor development and longitudinal data acquisition is paralleled by the growth in ML approaches that continue to provide promising avenues for better geriatric care through higher personalization, more real-time feedback, and prognostic insights that may help prevent downstream complications and relieve strain on the healthcare system overall. However, findings that identify differences in longitudinal biomarker interpretations between elderly populations and relatively younger populations highlights the necessity that ML approaches that use data from newly developed passive IoT systems should collect more data on this target population and more clinical trials will help elucidate the extent of benefits and risks from these data driven approaches to remote care.

Sensing Systems for Respiration Monitoring: A Technical Systematic Review

Respiratory monitoring is essential in sleep studies, sport training, patient monitoring, or health at work, among other applications. This paper presents a comprehensive systematic review of respiration sensing systems. After several systematic searches in scientific repositories, the 198 most relevant papers in this field were analyzed in detail. Different items were examined: sensing technique and sensor, respiration parameter, sensor location and size, general system setup, communication protocol, processing station, energy autonomy and power consumption, sensor validation, processing algorithm, performance evaluation, and analysis software. As a result, several trends and the remaining research challenges of respiration sensors were identified. Long-term evaluations and usability tests should be performed. Researchers designed custom experiments to validate the sensing systems, making it difficult to compare results. Therefore, another challenge is to have a common validation framework to fairly compare sensor performance. The implementation of energy-saving strategies, the incorporation of energy harvesting techniques, the calculation of volume parameters of breathing, or the effective integration of respiration sensors into clothing are other remaining research efforts. Addressing these and other challenges outlined in the paper is a required step to obtain a feasible, robust, affordable, and unobtrusive respiration sensing system.