Remote Cardiorespiratory Monitoring (Sensing) with RADAR





Remote or contactless monitoring of heart rate and respiratory rate, termed remote cardiorespiratory monitoring, can be performed with Doppler RADAR or other RADAR techniques. It can be used in patient care in hospitals or at home in order to monitor conditions such as sleep apnea or to calculate energy expenditure. Additionally, using specific algorithms they can be used to infer mood state. Remote cardiorespiratory monitoring consists of the transmission of an electromagnetic signal which is reflected off the thorax wall and is received with the purpose of analysis via demodulation. During respiration, the thoracic wall performs a periodic motion which ranges from 4 mm to 12 mm peak-to-peak in adults, while due to the heartbeat the peak-to-peak motion is approximately 0.5 mm. The reflected and received signal is similar to the transmitted signal with its phase modulated by the position of the thorax. In other words, the reflected signal is modulated by the thoracic motion.




Continuous wave (CW) (unmodulated) microwave Doppler RADAR 


Remote cardiorespiratory monitoring can be performed with Doppler RADAR. Use of continuous wave (CW) unmodulated microwave Doppler RADAR at 2.4 GHz and 1.6 GHz is reported by Droitcour A.D. (Droitcour A.D. 2006 dissertation - Stanford) to detect accurately the heart beat from a distance of 1 m and the respiratory rate from a distance of 1.5 m, as proven by measurements from 22 patients using standard techniques as control (thoracic impedance and inductive plethysmography) (Droitcour A.D. et al 2015). Instead of using bulky equipment, a chip Doppler RADAR transceiver (transmitter and receiver) is created, which functions as a heart rate and respiration rate sensor/monitor (Droitcour A.D. et al 2009).




Figure 1: From Droitcour A.D. 2006 (dissertation) Continuous wave Doppler RADAR for cardiorespiratory monitoring (cf. legend above).



Additionally, a Doppler-RADAR phased array approach with beamforming architecture to generate two simultaneous beams has been used (Nosrati 2019).


An informal analogy to the Doppler RADAR would be an automatic tennis ball machine which throws balls onto a wall with a velocity of one meter per second. If the ball bounces off the wall and return to us in two seconds, we can infer that it travelled for a distance of two meters towards the wall and back in two seconds and therefore that the wall is at a range/position of one meter. If the wall started to move towards us after the return of the first ball, then we would receive the first ball in two seconds, the next in less than two seconds and the next in even shorter than that time. In general, we would receive the balls back more frequently. By analyzing the shift in frequency, we can determine the motion and position of the wall.



Continuous wave (CW) frequency modulated (FM) microwave RADAR (FMCW)


The “Vital-Radio” by the Katabi MIT lab uses continuous wave (CW) frequency modulated (FM) microwave RADAR (FMCW) (5.46 GHz to 7.25 GHz) to measure respiratory rate and heart rate from different subjects from a distance of 8 meters or from another room (Adib et al 2015,, It is noted that this approach does not use the Doppler effect. It was suggested that the system could be incorporated in a Wi-Fi router in order to be used for vital sign monitoring in a residency (“Smart Homes that Monitor Breathing and Heart Rate” by Adib et al 2015). An additional reference for the technique can be found at this weblink.


The same lab for their “EQ-Radio” project processed the FMCW microwave RADAR data from thorax reflection with an algorithm that predicts with accuracy the heartbeat from the relevant small amplitude RADAR signal (heartbeat segmentation algorithm) and used both the respiration and the heartbeat data to compute emotion-dependent features and to infer emotional state (Zhao et al 2016, “EQ-Radio” uses a 2D emotion model whose axes are valence and arousal and classifies between four basic emotional states (in accordance with literature on categorizing human emotions): sadness (negative valence and negative arousal), anger (negative valence and positive arousal), pleasure (positive valence and negative arousal), and joy (positive valence and positive arousal).




Figure 2: The "EQ-radio" by the Katabi MIT lab uses FMCW RADAR (cf. horn antennas) for cardiorespiratory monitoring and detection of emotional state (screen capture from





Figure 3: Facebook post by MIT News presenting the "EQ-radio" (




Researchers at the Georgia Tech Research Institute (GRTI) created several versions of a RADAR vital sign monitor (RVSM) (homodyne/FMCW) among which one was used for Olympic athletes at the 1996 Altlanta Olympics with the purpose of measuring heartbeat at 10 meters and respiration rate at 20 meters (Greneker at al 1997). The same system was studied for biometric identification based on the heartbeat signature and also for stress determination.


It has been mentioned (Droitcour A.D. 2006) that at 100m, the limit was moving background clutter and not the system sensitivity.


A spin-off of these RVSP systems termed “RADAR Flashlight” was used for different applications, including security and law enforcement (ref1),(ref2),(ref3)(ref4).



Figure 4: GTRI Radar Vital Sign Monitor for Olympic Athletes and RADAR Cardiogram taken at 15 Feet (From Greneker 2006).



Ultrawide-band RADAR (UWB) and LIDAR


A large number of studies for remote cardiorespiratory monitoring use Ultrawide-band RADAR (UWB) (Liang et al 2018). It is reported that the use of circularly polarized waves with UWB RADAR provides improved accuracy (Chan et al 2013).


A laser monitor has also been used in a approach that would be similar to LIDAR (Kondo et al 1997).


It is noted that UWB radar has been successfully combined with MRI for medical navigation/guidance purposes (Thiel et al 2010), (ref).



Pulsed RADAR - Wi-Fi

For completeness purposes, it is mentioned that pulsed RADAR represented by Wi-Fi which is a pulsed 2.4 GHz signal, has been used for motion detection (Adib et al 2013), (Pu et al 2013).