COVID-19 outbreak has tremendously changed the world in many ways, but it learns us many lessons to adapt and maintain sustainability in pandemics. The protection of healthcare providers must be the first priority. However, due to the exponential increment of the number of patients, plus the shortage of personal protection equipment (PPE) create a huge challenge to control the pandemic. In addition, body temperature, blood oxygen density, heartbeats, and respiration are the primary vital signs that they are monitored constantly in the hospitals for COVID-19 patients, but the conventional medical devices require attachments of a part to the patient’s body. Thus, instead of reducing risk to the hospital workers, they increase the risk of infection to the healthcare providers. So, we need to employ new technologies for long-distance monitoring of multiple patients. This reduces the exposure and cost, and it overcomes the shortage in PPEs by reducing the number of nurses.
Sleep apnea, bedsore, sleep disorders, and, in general, bedbound patients require various wearable or contact medical tools to record vital signs. Sleep monitoring with the polysomnography (PSG) as a gold standard is very costly and requires the continuous supervision of a physician. Mostly, these are contact devices intervening with the patients’ freedom. Although in some cases, the devices are impossible to be connected to the patient; for instance –a patient with burned skin. Indeed, health monitoring is interrupted when the attachments become loose, or at least, the measurement error increases. However, more convenience and reliability can be offered by contactless devices. For instance, it has been shown that radars can detect vital signs without any contact with the body by capturing tiny chest motions due to cardiorespiratory activity1–4. Although they can facilitate non-obstructive, touchless, and long-term vital signs detection, practically using radars for heart rate and breathing rate (BR) detection lacks enough accuracy in the presence of body motions1 or the radar platform motion4. In particular, they can be used for bedbound patients where they are supposed to be stationary most of the time with occasional movements.
Remote vital signs monitoring with radars covers a number of areas ranging from electric and magnetic characterizations of human body5–9 to high resolution spectral and temporal signal acquisition and detection10–13. For the signal acquisition, linear demodulation1, 2, 14, and arctangent demodulation have been used in many research since they retrieve the body-modulated phase of the radar indicating the chest motion. In fact, in the radar principle, the signal phase can reveal the target displacement in the range of even less than one wavelength; however, it requires to apply a nonlinear function to the signal for the demodulation, which creates strong harmonics if the received signal is contaminated with an interference. More specifically, a practical challenge in phase acquisition of the continuous wave (CW) or frequency-modulated CW (FMCW) radars is the existence of a DC term in the received signal due to multiple reasons such as antenna coupling or RF cross-talk, a stationary object in the desired range, and an intrinsic DC generated by the phase modulation itself3, 15. Although all DC components should be eliminated, the DC part of the phase modulation, we call it vital DC, must be in the phase demodulation for a distortionless phase extraction3. Otherwise, the vital DC cancellation incurs many harmonics and intermodulations of the breathing and heartbeats in the phase domain15. In addition, the phase analysis requires the phase unwrapping, which adds a constraint on how the body moves depending on the sampling frequency15.
Apart from the signal analysis with implicit ideal assumptions, the radar response, in reality, not only include the target chest motions but also random body activities and the self-movement of radar4. For instance, authors in1, 3 tried to suppress the extra body motions by placing the subject in the middle of two radars or identifying the sudden short-duration movements with the continuous wavelet transform (CWT), respectively. In fact, in3, the summation of the signals received from two sides of the body effectively cancels the body motions while the respiration and heartbeats remained in the signal. In contrast, in1, the application of CWT allowed authors to detect interim high-frequency occurring moments corresponding to moderate body motions such as “limb movement" and “crossing the legs". Then, a moving average smooths out the signal in the occurrence of extraneous motions reducing the impact on the detection of vital signs. Furthermore, if the radar platform moves, the authors in4 proposed a dual-band CW radar such that one band is used to detect the radar motion with respect to a reference reflector.
Essentially, the radar is an integrator, which collects backscattered fields from all reflectors in the environment. For this, the radar observation for a large object, like the human body, is the surface integration of reflected fields on the entire area of the object. Each point on the large target moves with a velocity depending on its relative angles toward the radar. Therefore, even if the mass centre of a large target moves with a constant speed, the radar output will be a signal with many velocity components depending on the relative motions of different points of the target with respect to the radar, the target size, the operating frequency, and a beamwidth of the radar radiation. In fact, the higher the radar resolution is, the lower the integration area is for each radar cube cell16. Therefore, a radar with high angular and range resolution can select an appropriate part of space in the in 2D (xy) or 3D (xyz) with a reduction in the distortion due to the smaller integration area. The range resolution depends on the sweeping bandwidth of FMCW chirps, so the higher the range resolution is, the higher RF bandwidth is. Indeed, wideband RF components are expensive, so, there is a tradeoff between the hardware cost and the range resolution. Besides, MIMO radars can obtain a high angular resolution with a few numbers of Tx and Rx antenna elements. In fact, the higher angular resolution is achieved by increasing the effective aperture size of the receiver array, which results in a more compact and cheaper hardware16. So, in this work, we use a MIMO FMCW radar to obtain angular information not only for better signal acquisition but also for detecting a greater number of individuals. In fact, the radar could function as multiple sensors at the same time preserving power, cost, and promoting the safe distancing to COVID-19 patients.
The problem of finding respiration frequency is identical to the classical fundamental frequency estimation where primarily they have been extensively investigated in speech processing17, 18. The problem involves a noisy observation of a signal containing harmonics of a natural process therein the fundamental frequency, magnitude, and the phase of the process are unknown. In fact, depending on the practical considerations, the harmonic mixture is corrupted mainly by an additive Gaussian noise, which in turn a maximum likelihood (ML) estimator17 gives an optimum solution. However, ML requires high computational power and the full knowledge of the noise making it inappropriate to be used in practice. Therefore, there are many traditional sub-optimal harmonic estimators such as linear prediction19, harmonic fitting20, and subspace methods21, 22. Subspace methods use a clever geometrical interpretation of the signal and noise spaces to separate the signal from the noise such as MUltiple SIgnal Classification (MUSIC) analysis12, 23. Furthermore, with the matched filter concept, one could find optimum filter coefficients resulting in an output, which is the closest to an ideal noiseless harmonic. In fact, this is implemented by Christensen et al.in24, which does not only estimate of the fundamental frequency but also a noiseless waveform. We will apply the high-resolution optimum filter when the radar complex signal for each subject is fed into the filter giving the respiration rate and the noiseless waveform. Also, we note that the complex signal detection is more robust than the phase domain analysis since it does not need nonlinear phase computation, phase unwrapping, and also the vital DC cancellation does not create distortions.
For sleep position detection, we collect features from each detected target points as the inputs to support vector machine (SVM) classifier. The detection is novel and does not have any similar prior arts. Knowing the sleep position is much important for patients who could not move from the bed that they usually require to change their sleep position more often as a relief to the bed ulcers. Also, sleep apnea patients need to change their position preceding the obstruction moments. So, the radar advises the patient’s supervisor about the sleep position history, and the supervisor helps to change the position of the patient has staid on a position for a long time.
In this work, we use a commercial millimeter-wave FMCW radar by Texas Instruments, AWR124325, operating at centre frequency of 79 GHz with 4 GHz sweeping bandwidth. The choice of mm-wave frequency is for high sensitivity in millimeter- scale since the mechanics of the respiration moves the body within a few millimeters15. The radar has 3 transmitters and 4 receivers, but it makes an 8-element virtual receiver array by the MIMO radar technique. This enables about 14 degrees of angular resolution, which is enough to distinguish two the same subjects at 3 meters with 70 cm lateral separation. Furthermore, we use a smart garment, Hexoksin vest, by Carre Technologies inc. as a reference sensor26. It can monitor BR, tidal volume (vt), minute ventilation, and hip motion intensity (HMI) among other vital signs. The authors in27 reported that the device measures BR for different body postures with 98% accuracy in comparison to the standard laboratory measurement tools. In addition to vital signs monitoring, we propose a novel algorithm for the sleep position detection that determines the position for each individual independently.