Remote sensing of human breathing at a distance

• US 10,401,479 B2
• Filed: 05/15/2015
• Issued: 09/03/2019
• Est. Priority Date: 05/16/2014
• Status: Active Grant

• Current Assignee: University of Ottawa
• Original Assignee: University of Ottawa
• Inventors: Mabrouk, Mohamed, Batkin, Izmail, Rajan, Sreeraman, Bolic, Miodrag, Groza, Voicu, Dajani, Hilmi
• Primary Examiner(s): McGue, Frank J
• Application Number:
  US14/713,936
• Publication Number:
  US 20150369911A1
• Time in Patent Office: 1,572 Days
• Field of Search: 342 28
• CPC Class Codes: G01S 13/52 Discriminating between fixed and moving objects or between objects moving at different speeds
• CPC Class Codes: G01S 13/888 through wall detection
• CPC Class Codes: G01S 7/415 Identification of targets based on measurements of movement associated with the target

Abstract

A method and system is described for using radar to provide non-contact detection of human breathing at a distance. Radar data is processed to remove clutter and enable detection of a breathing signal. The breathing signal can be used to determine breathing rate or posture of the target. Static obstructions can be removed from the radar data to identify target breathing signal. The system and method can be applicable to emergency response, home or institutional monitoring applications and for detection of medical conditions.

Claims

1. A method for detecting a target using a radar transmitter/receiver for detecting a target, the method comprising:

receiving radar data from the radar transmitter/receiver;

performing clutter suppression on the received radar data;

detecting one or more targets in specific range bins in the clutter suppressed radar data;

extracting a breathing signal for the detected one or more targets;

extracting parameters of the extracted breathing signal and estimation of a breathing rate numerical value for the one or more targets;

verifying that each of the one or more targets are in associated range bins based on the extracted parameters;

wherein extracting parameters further comprises detecting a closest target range bin by monitoring changes in moments of a probability distribution of the received radar data wherein the moments are one of a skewness, a variance or a mean of the probability distribution;

wherein a condition for determining if the target is in the range bin is based upon comparing skewness changes relative to a defined threshold based upon characteristics of the breathing signal; and

wherein a condition for determining if the target is in the range bin based on the skewness is defined by;

2. The method of claim 1 wherein a distribution of a given range over time will have a skewness value from zero to C, where C is a constant number, if it is clutter and if the target is present it will drop to less than β

where β

is a threshold value and a transition from C to any reasonably small value will constitute detection of a human target.

3. The method of claim 1 wherein the method is executed by a computer processor in addition to the radar transmitter/receiver

wherein the radar transmitter/receiver is stationary;

wherein the computer processor receives the radar data from the stationary radar transmitter/receiver coupled to an obstructing surface opposite a target space;

wherein the detection of one or more targets in specific range bins in the clutter suppressed radar data is in the target space;

wherein extracting a breathing signal for the detected one or more targets is in the specific range bins;

wherein verifying that each of the one or more targets are in associated range bins based on the extracted parameters is done by verifying that the breathing signal of each of the one or more targets are in associated range bins in the target space;

wherein the verifying is to confirm that each of the one or more targets are associated with a respective person in the target space; and

wherein identifying the verified one or more targets is done through an input/output interface coupled to the processor.

4. The method of claim 3 wherein extracting the breathing signal is performed by processing in time-domain to detect individual pulses of the breathing signal.

5. The method of claim 3 further comprising applying a band-pass filter for modifying a frequency band of interest for extracting the breathing signal by adjusting an upper and a lower cut-off frequencies of the band-pass filter based on the extracted parameters.

6. The method of claim 3 further comprising detecting stop-breathing events based on an estimated breathing rate numerical value.

7. The method of claim 3 wherein the radar transmitter/receiver is an ultra-wide band (UWB) radar transmitter/receiver, a Continuous wave radar transmitter/receiver, a Frequency Modulated Continuous Wave radar transmitter/receiver, pseudo-random noise coded continuous-wave radar transmitter/receiver, or a pseudo-random noise Ultra wide band (PN-UWB) radar transmitter/receiver.

8. The method of claim 3 wherein performing clutter suppression on the received radar data and detecting the one or more targets from the radar data is performed iteratively by applying singular value decomposition (SVD) to the received radar data.

9. The method of claim 3 wherein the skewness of a probability distribution of amplitude squared of the received radar data is computed and its first significant minimum is estimated, whereby the first minimum corresponds to a location of the closest target.

10. The method of claim 3 wherein detecting the closest target range bin is performed by combining SVD with changes in moments of the probability distribution.

11. The method of claim 10 wherein a zero crossing method is applied to detect individual pulses of the breathing signal.

12. The method of claim 10 wherein the extracted parameters from the breathing signal include one or more of the parameters of:

a power of a fundamental harmonic, a peak-to-peak interval, and characteristics of a breathing signal pulse.

13. The method of claim 12 wherein the extracted parameters of each pulse are evaluated against predefined thresholds in order to reject certain pulses and estimate the breathing rate numerical value based on accepted pulses.

14. A method executed using a computer processor and a stationary radar transmitter/receiver for analysing a breathing signal, comprising:

cleaning received stationary radar data, from the stationary radar transmitter/receiver coupled to an obstructing surface opposite a target space;

performing clutter suppression on received stationary radar data;

extracting a frequency of a fundamental harmonic from received stationary radar data in the target space;

extracting amplitudes of the fundamental harmonic and one or more higher harmonics from the received stationary radar data;

determining relationship between the amplitudes of the harmonics to identify a breathing signal of one or more targets, including estimation of a numerical value of breathing frequency;

verifying that the breathing signal of each of the one or more targets are in associated range bins in the target space based on the extracted amplitudes to confirm that each of the one or more targets are associated with a respective person in the target space; and

identifying the verified one or more targets through an input/output interface coupled to the processor.

15. The method of claim 14 wherein determining relationship between the amplitudes of the harmonics is based on ratios between an amplitude of first harmonics and amplitudes of the higher harmonics.

16. The method of claim 14 further comprising detecting posture changes of a single person based on values of the amplitudes of a fundamental frequency and the harmonics.

17. The method of claim 14 further comprising determining posture based on values of the amplitudes of the harmonics.

18. The method of claim 14 further comprising determining if a person is lying on a floor based on the determined relationship.

19. The method of claim 14 further comprising discriminating between several targets breathing at different rates based on existence of several sets of harmonic components of the breathing signal.

20. The method of claim 14 wherein the radar transmitter/receiver is from the group consisting of an ultra-wide band (UWB) radar transmitter/receiver, a Continuous wave radar transmitter/receiver, a Frequency Modulated Continuous Wave radar transmitter/receiver, pseudo-random noise coded continuous-wave radar transmitter/receiver, and a pseudo-random noise Ultra wide band (PN-UWB) radar transmitter/receiver.

21. The method of claim 14 wherein determining harmonics of the breathing signal are utilized to find inter-modulation products between breathing and heartbeat harmonics in order to facilitate detection of a heart beat signal.

22. A device comprising:

a processor coupled to a stationary radar antenna of a radar transmitter/receiver; and

a non-transitory memory containing instructions which when executed by the processor and using the radar transmitter/receiver perform steps of;

receiving radar data from the stationary radar antenna coupled to an obstructing surface opposite to a target space;

performing clutter suppression on the received radar data;

detecting one or more targets in specific range bins in the clutter suppressed radar data in the target space;

extracting a breathing signal for the detected one or more targets in the specific range bins;

extracting parameters of the extracted breathing signal and estimation of a breathing rate numerical value for the one or more targets;

verifying that the breathing signal of each of the one or more targets are in associated range bins in the target space based on the extracted parameters to confirm that each of the one or more targets are associated with a respective person in the target space;

wherein extracting parameters further comprises detecting a closest target range bin by monitoring changes in moments of a probability distribution of the received radar data wherein the moments are one of a skewness, a variance or a mean of the probability distribution;

wherein a condition for determining if the target is in the range bin is based upon comparing skewness changes relative to a defined threshold based upon characteristics of the breathing signal;

wherein a condition for determining if the target is in the range bin based on the skewness is defined by;