Respiratory function is a critical indicator of health yet it is often underutilized because of shortcomings in conventional monitoring methods. Even basic respiratory rate measurement can provide insight into a subject’s health and physiological stability and is one of the strongest predictors of mortality in hospitalized patients (Droitcour et al., 2009). Respiratory signatures for sedentary patients have been used to assess Sleep apnea using the apnea-hypopnea index (AHI) (Young et al., 1993). Results suggest a high prevalence of undiagnosed sleep disorders for subjects within the age range of 30–60. Sleep Disordered Breathing (SDB) has also been associated with hypertension and cancer mortality in community-based samples (Pedrosa et al., 2011; Nieto et al., 2012). Conventional methods used to assess respiratory function include measurement of SpO ₂ levels through pulse oximetry, respiratory expansion, and contraction through ECG-measured thoracic impedance, airflow through spirometry, thorax expansion/contraction through respiration belts, or respiratory rate through clinical observation and counting. While SpO ₂ levels can provide a strong indication of respiratory distress, such indications do not show up until the problem is advanced and do not provide further diagnostic indications. Measurements of the diaphragm and related muscle activity require the attachment of ECG electrodes and are subject to interference from other electrical activity in the body. Direct measurement of airflow through spirometry or chest motion through elastic belts also requires that restrictive measurement apparatus be applied to the subject’s body, and measurement of respiratory rate through observation is labor intensive and generally limited to a rough estimate. Thus, more accurate and less obtrusive means for respiration monitoring can save lives, improve quality of life, reduce hospital stays, and lower medical costs (Droitcour et al., 2009).

Measurement of cardiopulmonary function through Doppler radar measurement of thoracic displacement has been known since the 1970s (Franks et al., 1976). Infants as well as burn victims can especially benefit from contactless respiratory measurement systems. The efficacy of radar-based systems has been shown compared to other contactless approaches. It has been demonstrated that radar systems can be designed in compact monolithic packages similar to wireless telecommunications devices and can function without affecting the monitored subject (Droitcour et al., 2001; Droitcour et al., 2004; Savage et al., 2016). The range of operation for low-cost, low-power Doppler radar transceivers also makes them suitable for applications beyond healthcare including occupancy detection, search and rescue, and security applications (Park et al., 2007a; Lubecke et al., 2007; Islam et al., 2023; Song et al., 2023). Off-the-shelf radar kit designs have also been used for radar sleep monitoring and automated sleep apnea detection (Zaffaroni et al., 2009; Savage et al., 2016). The use of appropriate signal processing can isolate respiratory and heart signals accurately even when the radar system is meters or even tens of meters away from the subject (Droitcour et al., 2001; Baboli et al., 2012a). Longer-range search and rescue triage applications of Doppler radar have also emerged. The respiratory rate has been successfully monitored for a seated subject at up to 69 m, while heart rate was successfully monitored at up to 21 m, using a compact system generating tens of milliwatts of power at 2.4 GHz, similar to a cordless phone. At closer ranges associated with home and hospital patient monitoring, more detailed cardiopulmonary signatures can be analyzed using similar or lower power levels (Yamada et al., 2006). Heart rate measurements have been successfully measured at 1 m using sub-micro-watt power levels.

Both heart and respiratory activity present as displacement changes at the body surface, whether the subject is standing, seated, or recumbent. Doppler radar measurement of this displacement provides a real-time signature of respiratory function which can be analyzed not only to assess the respiratory rate, but also for the assessment of respiratory volume, recognition of diagnostic patterns, and even for support in the verification of the identity of an individual (Singh et al., 2013). A signature of this type is illustrated in Figure 1, where details associated with inhalation, exhalation, and transitions provide valuable information on pulmonary function and physiological characteristics of an individual subject.

The periodic motion patterns measured by Doppler radar can be used to differentiate between normal and abnormal breathing and identify irregular episodes such as sleep apnea events (Lee et al., 2014). A sleep study was conducted for Doppler radar assessment of Obstructive Sleep Apnea (OSA) and hypopnea events. The results were compared to the gold standard polysomnography test, with accurate detection of events identifiable by a simple indicator included in the standard sleep study monitoring data. To verify the accuracy of the Doppler radar system on infants, an infant breathing simulator has been developed (Yan et al., 2009).

Alternate non-invasive biosensors, such as lasers, pressure sensors, and magnetic induction coils have been used with the same goal (Aubert et al., 1984; Reinvuo et al., 2006; Steffen and Leonhardt, 2008). Recording cardiopulmonary signals have been attempted with accelerometers directly attached to the body (Aubert et al., 1984). Using lasers requires a direct line of sight with the target. Body-attached accelerometers may have similar noise and encumbrance issues as sensors used for ECG. Infrared sensors have also been used for respiratory monitoring applications but suffer from line-of-sight requirements (Yavari et al., 2014). In comparison, Doppler radar can be used through clothing and other obstacles (Rissacher and Galy, 2015; Islam et al., 2020a).

Most non-invasive radar monitoring of respiration is focused on sedentary breathing, as it provides a consistent mechanism for comparing breathing between individuals and for tracking breathing for an individual over time. Breathing can change dramatically or even stop during certain activities. Sedentary breathing does not require knowledge of a subject’s activity but does require a mechanism for determining that sedentary conditions are met, which can be achieved by analyzing the spatial-temporal variations of the radar signal (Massagram et al., 2011). In addition to obstructive sleep apnea assessment, radar respiratory measurements of sleeping subjects have also been used to detect sleep/wake states in subjects as well as the sleep posture of a subject (Chen, 2008; de Chazal et al., 2008; de Chazal et al., 2010).

Radar-based torso displacement measurements have been used to accurately assess respiratory tidal volume which corresponds distinctly with an individual’s respiratory mechanics (Benchetrit, 2000). Accurate reconstruction of the chest wall motion implementing non-contact sensors presents the possibility of remote identification of individuals based on features of unique airflow profiles. By analyzing the unique patterns of inhalation and exhalation along with tidal volume, feature sets have been engineered to uniquely identify subjects (Rahman et al., 2016; Rahman et al., 2018; Islam et al., 2019; Islam et al., 2020b). Furthermore, unique respiratory signatures have been used to classify measurements of adults or children, and to study other mammals, as well as fish and reptiles (Hafner et al., 2012; Singh et al., 2012; Wang et al., 2019).

Notably, the standard frequencies used for radar sensing are the internationally reserved 2.4 GHz, 5.8 GHz, and 24 GHz Industrial, Scientific, and Medical (ISM) bands. Using 2.4 GHz and 5.8 GHz radars offers the use of off-the-shelf components as these are frequencies commonly used for telecommunication applications. Radar transceivers operating at 24 GHz or higher frequencies offer higher-resolution data but require more complex signal processing (Singh et al., 2013).

The Respiratory Mechanics section of this paper describes the relationship between respiratory function and torso displacement which can be measured with a radar system. The Physiological Radar section describes the principles of operation for Doppler radar motion sensing and the types of signals produced by such sensors. The Signal Analysis section describes the application of non-data-driven expert models to provide radar-based assessments of various respiratory characteristics or conditions for a subject, and also data-driven machine learning approaches to such assessments which can provide more robust analysis and can be tailored to individuals.

Physiological Doppler radar detects the minute movement of the chest surface due to cardiorespiratory activities including heartbeat, arterial pulsation, and breathing (Benchetrit, 2000). This physiological motion is mostly concentrated about the thorax, where the heart and lungs lie, and the abdomen (Benchetrit, 2000; Vander et al., 2020; H Opie, 2005; Lin et al., 2017). Respiratory functions for these two regions are known as thoracic and diaphragmatic breathing, respectively (Aubert et al., 1984; Lin et al., 2017). Torso deformation is a complex phenomenon based on a three-dimensional pattern that varies with the subject and activity (Vander et al., 2020; H Opie, 2005).

Biomedical Doppler radar operates at a distance and in the frequency range where most of the signal is reflected at the skin surface and thus motion sensing is primarily restricted to this region. Generally, lung contraction takes place in two ways. One is by downward and upward movement of the diaphragm to lengthen or shorten the chest cavity, and the other is by elevation and depression of the ribs to increase and decrease the diameter of the chest cavity. It has been demonstrated in various clinical investigations that a sedentary adult human subject shows diversity in respiratory patterns not only in terms of the tidal volume during inspiratory and expiratory duration but also in terms of airflow profile (Benchetrit, 2000; Vander et al., 2020; H Opie, 2005). Additionally, different people have different lung sizes, shapes, and volumes due to physiological diversity (Benchetrit, 2000; Vander et al., 2020; H Opie, 2005).

Chest wall movement during the airflow in the lungs is related to the contraction and relaxation of the diaphragm, as well as the external intercostal, neck, and chest muscles (Droitcour et al., 2004), as illustrated in Figure 2A. When the diaphragm pushes downward, the volume inside the lung increases and air flows into the lungs. Air continues to flow until the pressure inside the lungs equals the pressure outside the lungs, and at that point, the diaphragm relaxes and moves back. Therefore, the lung and diaphragm movement coordination are the drivers for external chest displacement, which is also controlled by the mechanics of the sternum and ribcage. Volumetric changes in the thorax correspond to sternum movement which occurs similarly to the motion of a “pump handle”, as illustrated in Figure 2B. Additionally, rib movement occurs in the lateral dimension of the thorax when the shafts are elevated, similar to the motion of a “bucket handle”, also shown in Figure 2B. The profiles for thoracic volume change during inhalation and exhalation are not completely symmetric, nor are they identical for deep and shallow breathing. Figure 2C illustrates lung compliance hysteresis for healthy breathing (Menon and Agrawal, 2003).

Thus, the displacement across the body measured by a radar system can be ultimately traced to the mechanical function of the lungs, and are also subject to the sternum, rib, and muscular dynamics of the individual. This facilitates not only the assessment of lung function through radar measurement but also the unique characterization of individual subjects.

A Doppler radar motion-sensing transceiver typically transmits a continuous wave signal, receives the reflection of this signal from a moving surface, and compares the phase of the signals to demodulate the motion information (Boric-Lubecke et al., 2015). While most physiological radar measurements use a fixed-beam antenna to limit the radar field of view, beam-steering with phased array antennas has also been demonstrated to track and isolate subjects of interest.

While stationary reflecting surfaces in the radar signal path result in a constant value for the demodulated phase-difference signal, surfaces with periodic motion result in a phase demodulation output signal which varies in proportion to displacement during the displacement cycle. However, the resolution of the received signal depends on the standing wave pattern established by the nominal distance to the moving subject. Depending on the frequency of the transmitted wave, at a distance of even multiple of λ/4, where λ is the wavelength of the radar signal, destructive interference of transmitted and received signal occurs and resolution is compromised (null point). On the other hand, at a distance of every odd multiple of λ/4, the optimum signal level is attained (Park et al., 2007b).

Radar-measured displacement patterns can be used to characterize various cardiopulmonary functions including respiratory rate, tidal volume, and apnea/hypopnea events, based on the development of a suitable mathematical model. There have been several successful demonstrations of such measures based on models developed using domain expertise, which set generalized criteria to correlate the measurement signal with expectations for the characteristic of interest. Recently, some of these have been used as a basis for more advanced machine learning (ML) methods which use data sets to train the system to recognize and categorize measurements in subsequent data.

Doppler radar has been successfully used to accurately assess various human respiratory characteristics. These include general measurements such as respiratory rate and tidal volume, as well as interpretive analysis including sleep apnea event detection and unique subject identification. Radar systems can produce multi-dimensional representations of body surface motion which can be analyzed through intuitive mathematical models associating mechanical motion with respiratory function, or through machine learning approaches that use data sets for known conditions of interest to create models that can be used to accurately recognize those conditions in subsequent data. Through further research, correlations may be drawn between additional respiratory health and function and measurable radar signatures to advance this non-invasive approach to broader analytics including assessment of lung ventilation heterogeneity and other diagnostically significant conditions.