Using continuous wave, 94-GHz millimeter-wave interferometry, a signal representing chest wall motion can be obtained that contains both the heart rate and respiration patterns of a human subject. These components have to be separated from each other in the received signal. Our method was to use the quadrature and in-phase components of the signal, after removing the mean of each, to find the phase, unwrap it, and convert it to a displacement measurement. Using this, the power spectrum was examined for peaks, which corresponded to the heart rate and respiration rate. The displacement waveform of the chest was also analyzed for discrete heartbeats using a novel wavelet decomposition technique.

A compact millimeter-wave (MMW) sensor has been developed for remote monitoring of human vital signs (heart and respiration rate). The low-power homodyne transceiver operating at 94 GHz was assembled by using solid-state active and passive block-type components and can be battery operated. A description of the MMW system front end and the back-end acquisition hardware and software is presented. Representative test case results on the application of various signal processing and data analysis algorithms developed to extract faint physiological signals of interest in presence of strong background interference are provided. Although the laboratory experiments so far have been limited to standoff distances of up to 15 m, the upper limit of the detection range is expected to be higher. In comparison with its microwave counterparts, the MMW system described here provides higher directivity, increased sensitivity, and longer detection range for measuring subtle mechanical displacements associated with heart and respiration functions. The system may be adapted for use in a wide range of standoff sensing applications including for patient health care, structural health monitoring, nondestructive testing, biometric sensing, and remote vibrometry in general.

This study uses the hidden Markov model (HMM) to identify different market regimes in the US stock market and proposes an investment strategy that switches factor investment models depending on the current detected regime. We first backtested an array of different factor models over a roughly 10.5 year period from January 2007 to September 2017, then we trained the HMM on S&P 500 ETF historical data to identify market regimes of that period. By analyzing the relationship between factor model returns and different market regimes, we are able to establish the basis of our regime-switching investing model. We then back-tested our model on out-of-sample historical data from September 2017 to April 2020 and found that it both delivers higher absolute returns and performs better than each of the individual factor models according to traditional portfolio benchmarking metrics.

A continuous wave (CW) 94-GHz millimeter wave (mmW) standoff biosensor has been developed for remote biometric sensing applications. The sensor measures the demodulated in-phase (I) and quadrature-phase (Q) components of the received reflected mmW signal from a subject. Both amplitude and phase of the reflected signal are obtained from downconverted I and Q channels from the quadrature mixer. The mmW sensor can faithfully monitor human vital signs (heartbeat and respiration) at relatively long standoff distances. Principle Component Analysis (PCA) is used to extract the heartbeat, the respiration and the body motion signals. The approach allows one to deduce information about amplitude and beat-to-beat rate of the respiration and the heartbeat. Experimental results collected from a subject were analyzed and compared to the signal obtained with a three-electrode ECG monitoring instrument.