Investigation of mmWave Radar Technology For Non-contact Vital Sign Monitoring

Non-contact vital sign monitoring has many advantages over conventional methods in being comfortable, unobtrusive and without any risk of spreading infection. The use of millimeter-wave (mmWave) radars is one of the most promising approaches that enable contact-less monitoring of vital signs. Novel low-power implementations of this technology promise to enable vital sign sensing in embedded, battery-operated devices. The nature of these new low-power sensors exacerbates the challenges of accurate and robust vital sign monitoring and especially the problem of heart-rate tracking. This work focuses on the investigation and characterization of three Frequency Modulated Continuous Wave (FMCW) low-power radars with different carrier frequencies of 24 GHz, 60 GHz and 120 GHz. The evaluation platforms were first tested on phantom models that emulated human bodies to accurately evaluate the baseline noise, error in range estimation, and error in displacement estimation. Additionally, the systems were also used to collect data from three human subjects to gauge the feasibility of identifying heartbeat peaks and breathing peaks with simple and lightweight algorithms that could potentially run in low-power embedded processors. The investigation revealed that the 24 GHz radar has the highest baseline noise level, 0.04mm at 0{\deg} angle of incidence, and an error in range estimation of 3.45 +- 1.88 cm at a distance of 60 cm. At the same distance, the 60 GHz and the 120 GHz radar system shows the least noise level, 0.0lmm at 0{\deg} angle of incidence, and error in range estimation 0.64 +- 0.01 cm and 0.04 +- 0.0 cm respectively. Additionally, tests on humans showed that all three radar systems were able to identify heart and breathing activity but the 120 GHz radar system outperformed the other two.

Non-invasive urinary bladder volume estimation with artefact-suppressed bio-impedance measurements

Urine output is a vital parameter to gauge kidney health. Current monitoring methods include manually written records, invasive urinary catheterization or ultrasound measurements performed by highly skilled personnel. Catheterization bears high risks of infection while intermittent ultrasound measures and manual recording are time consuming and might miss early signs of kidney malfunction. Bioimpedance (BI) measurements may serve as a non-invasive alternative for measuring urine volume in vivo. However, limited robustness have prevented its clinical translation. Here, a deep learning-based algorithm is presented that processes the local BI of the lower abdomen and suppresses artefacts to measure the bladder volume quantitatively, non-invasively and without the continuous need for additional personnel. A tetrapolar BI wearable system called ANUVIS was used to collect continuous bladder volume data from three healthy subjects to demonstrate feasibility of operation, while clinical gold standards of urodynamic (n=6) and uroflowmetry tests (n=8) provided the ground truth. Optimized location for electrode placement and a model for the change in BI with changing bladder volume is deduced. The average error for full bladder volume estimation and for residual volume estimation was -29 +/-87.6 ml, thus, comparable to commercial portable ultrasound devices (Bland Altman analysis showed a bias of -5.2 ml with LoA between 119.7 ml to -130.1 ml), while providing the additional benefit of hands-free, non-invasive, and continuous bladder volume estimation. The combination of the wearable BI sensor node and the presented algorithm provides an attractive alternative to current standard of care with potential benefits in providing insights into kidney function.