Separation of Doppler radar-based respiratory signatures

Abstract

Respiration detection using microwave Doppler radar has attracted significant interest primarily due to its unobtrusive form of measurement. With less preparation in comparison with attaching physical sensors on the body or wearing special clothing, Doppler radar for respiration detection and monitoring is particularly useful for long-term monitoring applications such as sleep studies (i.e. sleep apnoea, SIDS). However, motion artefacts and interference from multiple sources limit the widespread use and the scope of potential applications of this technique. Utilising the recent advances in independent component analysis (ICA) and multiple antenna configuration schemes, this work investigates the feasibility of decomposing respiratory signatures into each subject from the Doppler-based measurements. Experimental results demonstrated that FastICA is capable of separating two distinct respiratory signatures from two subjects adjacent to each other even in the presence of apnoea. In each test scenario, the separated respiratory patterns correlate closely to the reference respiration strap readings. The effectiveness of FastICA in dealing with the mixed Doppler radar respiration signals confirms its applicability in healthcare applications, especially in long-term home-based monitoring as it usually involves at least two people in the same environment (i.e. two people sleeping next to each other). Further, the use of FastICA to separate involuntary movements such as the arm swing from the respiratory signatures of a single subject was explored in a multiple antenna environment. The separated respiratory signal indeed demonstrated a high correlation with the measurements made by a respiratory strap used currently in clinical settings.

A System for Monitoring Breathing Activity Using an Ultrasonic Radar Detection with Low Power Consumption

“…For showing the advantages of the current study over the existing literature, we have compared the correlation coefficient of the proposed system with the most relevant literature [27,35,36] when the subjects were fully-clothed. At a distance of 0.5-1 m, the proposed system had a correlation coefficient of 0.96 in comparison with 0.93 [35], 0.93 [36], and 0.9063 [27]. Although the results obtained in this study have verified the effectiveness and accuracy of the ultrasonic radar detection to extract the abnormal breathing syndromes at different distances up to 3 m, it also has some limitations.…”

“…There have been several attempts to devise direct and indirect techniques to monitor breathing activity while minimizing discomfort. These techniques include direct contact such as magnetic induction [14][15][16][17], microphone [18,19], and capacitive [20][21][22][23], and indirect contact (contactless) such as electromagnetic radar detection [24][25][26][27][28][29], laser radar detection [30][31][32][33], ultrasonic radar detection [10,[34][35][36][37], thermographic imaging [38][39][40][41][42][43], and video camera imaging [44][45][46][47][48][49][50]. Each of these techniques, however, requires different process monitoring and has benefits and drawbacks that may make it more or less appealing to use under different circumstances as discussed in reference [51].…”

Phase Correlation Single Channel Continuous Wave Doppler Radar Recognition of Multiple Sources

“…Earlier research pursued to separate the signal mixture using complex transceiver architectures or advanced signal processing algorithms. In [8,9], Borić-Lubecke et al, and Lee et al, demonstrated the feasibility to separate multiple spectral diverse or spatially diverse cardiovascular-related motions with single-antenna and multiple-antenna CWDRs. In [10], Rivera et al, presented a multi-target detection method for heart and respiration rates, which applies clustering and multiple signal classification (MUSIC) algorithms to ultra-wideband (UWB) radar output.…”

Concurrent Respiration Monitoring of Multiple Subjects by Phase-Comparison Monopulse Radar Using Independent Component Analysis (ICA) With JADE Algorithm and Direction of Arrival (DOA)

“…This unobtrusive form of respiratory monitoring has prompted interest in sleep apnea studies because existing wearable sleep study sensors affect the quality of sleep and thus hamper accurate measurement [13]. In home-based sleep monitoring studies, it is common to have multiple subjects in view of the radar sensors when partners share a bed, and separating signals becomes a challenge [14]- [17]. However, most reported results in Doppler radar sensor literature simply focus on measurement of a single subject to avoid separation issues [15]- [17].…”

“…An early solution approach studied for single-antenna systems, involves frequency domain analysis [18], [19]. However, the Fast Fourier Transform (FFT) approach essentially proved to be unsuitable when a mixture of different breathing patterns occurs [14]- [20]. Previous research has also focused on utilizing DOA to estimate the angular location of subjects, but this has not proven very effective for closely spaced subjects due to the angular resolution limit [20]- [24].…”