PRIORITY CLAIM

This U.S. patent application claims priority under 35 U.S.C. § 119 to: Indian Patent Application No. 201821038470, filed on 10 Oct. 2018. The entire contents of the aforementioned application are incorporated herein by reference.

TECHNICAL FIELD

The disclosure herein generally relates to unobtrusive monitoring of physiological signals, and, more particularly, to real time and cost effective unobtrusive monitoring.

BACKGROUND

Microwave radar technology is being exploited these days for detecting minute vibrations including heart rates and breathing rates. However, microwave interferometry suffers from some issues that may corrupt the measurements, if not handled properly. One of such issues arises from the relation between vibrational amplitudes and the probing wavelength when measurements are done through standard radar baseband processing, sometimes termed as microwave interferometry. If the vibrational amplitude increases in terms of displacement over a certain threshold (compared to the probing wavelength) then the baseband measurement contains harmonics that can often mislead the measurement. This can be managed by maintaining a suitable amplitude vs wavelength ratio. However, the measurement can still suffer from harmonics if measuring around null-points. The situation becomes more problematic in cases where the radar module cannot be fixed at a desirable position (i.e. at optimal point) but requires treading on the move (say hand-held unit for detecting vibrations). Similarly, for use-cases where the subject is also not rigidly bound to a specific point (say non-contact heart-rate or breathing monitoring where people can comfortably stand or sit in front of a fixed radar), the null-point issues may arise and can lead to erroneous measurements. Moreover, measurement around null-point not only creates harmonics but also suffers from sharp decrease in sensitivity. To overcome such issues related to null-points, IQ channel radars are used. However, cost and availability in the market of IQ radars is a major bottleneck in its utilization.

SUMMARY

Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems.

In an aspect, there is provided a processor implemented method for real time unobtrusive monitoring of physiological signals pertaining to a subject confined to a bed, the method comprising the steps of: obtaining periodically, by one or more hardware processors, a motion data signal pertaining to the physiological signals including heartbeat rate and breathing rate from the subject, from a pseudo I (in-phase) channel and a pseudo Q (in quadrature) channel of three pseudo IQ radars, wherein the three pseudo IQ radars are positioned in a predetermined configuration around the subject and wherein each of the three pseudo IQ radars comprise a pair of single channel continuous wave (CW) radars placed at a calibrated distance from each other such that optimum points of one CW radar in the pair spatially overlaps null points of the other CW radar in the pair, a baseband signal from the CW radars constituting the pair serving as the pseudo I channel and the pseudo Q channel respectively; processing, by the one or more hardware processors, the motion data signal from each of the three pseudo IQ radars, to discard the motion data signal having motion artifacts; determining, by the one or more hardware processors, Quality of Information (QOI) based on at least one of Signal to Noise Ratio (SNR) and frequency spectrum associated with the motion data signal from each of the three pseudo IQ radars having no motion artifacts, wherein the QoI is indicative of the pseudo IQ radar from the three pseudo IQ radars to be considered for each lying position of the subject such that readings corresponding to the motion data signal have maximum SNR and minimum harmonic content; recalibrating, by the one or more hardware processors, one or more of the three pseudo IQ radars if the readings corresponding to the motion data signal having no motion artifacts deviate from an empirically determined threshold, wherein the threshold is indicative of number of times anomalous readings are received from the pseudo I channel and the pseudo Q channel; and evaluating, by the one or more hardware processors, the heartbeat rate and breathing rate by applying a Supervised Complex Signal Demodulation (SCSD) method on the motion signal data received from the one or more of the three pseudo IQ radars based on the QoI, wherein the SCSD method is configured to suppress amplitude and DC imbalance by assigning weights to the pseudo I channel and the pseudo Q channel, the weights being determined using a simulated trained weighted K-Nearest Neighbor (KNN) model configured to identify reliability of the pseudo I channel and the pseudo Q channel based on Fast Fourier Transform (FFT) pattern thereof in the range of the heartbeat rate and breathing rate, the reliability being indicative of harmonic content in the motion signal data.

In another aspect, there is provided a system for real time unobtrusive monitoring of physiological signals pertaining to a subject confined to a bed, the system comprising an indented radar system comprising three pseudo IQ radars positioned in a predetermined configuration around the subject, wherein each of the three pseudo IQ radars comprises a pair of single channel continuous wave (CW) radars placed at a calibrated distance from each other such that optimum points of one CW radar in the pair spatially overlaps null points of the other CW radar in the pair, a baseband signal from the CW radars constituting the pair serving as a pseudo I (in-phase) channel and a pseudo Q (in quadrature) channel respectively; and a controller unit in communication with each of the three pseudo IQ radars, wherein the controller unit comprises: one or more data storage devices configured to store instructions; and one or more hardware processors operatively coupled to the one or more data storage devices, wherein the one or more hardware processors are configured by the instructions to: obtaining periodically, a motion data signal pertaining to the physiological signals including heartbeat rate and breathing rate from the subject, from the pseudo I channel and the pseudo Q channel of the three pseudo IQ radars; processing the motion data signal from each of the three pseudo IQ radars, to discard the motion data signal having motion artifacts; determining Quality of Information (QoI) based on at least one of Signal to Noise Ratio (SNR) and frequency spectrum associated with the motion data signal from each of the three pseudo IQ radars having no motion artifacts, wherein the QoI is indicative of the pseudo IQ radar from the three pseudo IQ radars to be considered for each lying position of the subject such that readings corresponding to the motion data signal have maximum SNR and minimum harmonic content; recalibrating one or more of the three pseudo IQ radars if the readings corresponding to the motion data signal having no motion artifacts deviate from an empirically determined threshold, wherein the threshold is indicative of number of times anomalous readings are received from the pseudo I channel and the pseudo Q channel; and evaluating the heartbeat rate and breathing rate by applying a Supervised Complex Signal Demodulation (SCSD) method on the motion signal data received from the one or more of the three pseudo IQ radars based on the QoI, wherein the SCSD method is configured to suppress amplitude and DC imbalance by assigning weights to the pseudo I channel and the pseudo Q channel, the weights being determined using a simulated trained weighted K-Nearest Neighbor (KNN) model configured to identify reliability of the pseudo I channel and the pseudo Q channel based on Fast Fourier Transform (FFT) pattern thereof in the range of the heartbeat rate and breathing rate, the reliability being indicative of harmonic content in the motion signal data.

In an embodiment of the present disclosure, the predetermined configuration provides maximum beam coverage of the bed and comprises two of the three pseudo IQ radars positioned over the subject and one of the three pseudo IQ radars positioned below the bed such that the three pseudo IQ radars form an isosceles triangle for obtaining the motion data signal unobtrusively regardless of the lying position of the subject on the bed.

In an embodiment of the present disclosure, the calibrated distance is based on wavelength of the CW radars comprising the three pseudo IQ radars.

In an embodiment of the present disclosure, the calibrated distance is an integer (n) multiple of one eighth of the wavelength (λ/8) of the CW radars comprising the three pseudo IQ radars.

In an embodiment of the present disclosure, the presence of motion artifacts is detected when dominant frequency in the motion signal data is beyond 2 Hz.

In an embodiment of the present disclosure, the one or more hardware processors are further configured by the instructions to perform recalibrating one or more of the three pseudo IQ radars by suppressing phase imbalance between the pseudo I channel and the pseudo Q channel by fine adjustment of the calibrated distance between the CW radars constituting the pair.

In an embodiment of the present disclosure, the one or more hardware processors are further configured by the instructions to perform training of the simulated trained weighted K-Nearest Neighbor (KNN) model using training data simulated by: dividing the calibrated distance into a plurality of bins including points ranging from the optimum points to the null points; further dividing each of the plurality of bins into five classes including null class, better null class, middle class, better optimum class and optimum class having a distinct frequency spectrum therein; identifying a frequency band of operation corresponding to the physiological signal being monitored; simulating for each of the five classes comprised in each of the plurality of bins, the baseband signal for incremental steps of all frequencies in the frequency band of operation and incremental distances corresponding to each of the five classes; generating features including location of peaks and peak to peak ratio from frequency plots using the simulated baseband signal for each of the five classes comprised in each of the plurality of bins; and training the weighted KNN model using the generated features for each of the five classes comprised in each of the plurality of bins

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

