NON-CONTACT PHYSIOLOGIC MOTION SENSORS AND METHODS FOR USE

• US 20100130873A1
• Filed: 04/03/2009
• Published: 05/27/2010
• Est. Priority Date: 04/03/2008
• Status: Active Grant

• Current Assignee: ResMed Sensor Technologies Limited (ResMed Incorporated)
• Original Assignee: Kai Sensors, Inc.
• Inventors: Madsen, Anders Host, Yuen, Andrea, Shing, Tommy, Hourani, Charles El, Park, Byung Kwon, Droitcour, Amy
• Granted Patent: US 8,454,528 B2
• Time in Patent Office: Days
• US Class Current: 600/484
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/0507 using microwaves or terahertz waves
• CPC Class Codes: A61B 5/1126 using a particular sensing technique
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A61B 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/721 using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: G01S 13/534 based upon amplitude or phase shift resulting from movement of objects, with reference to the surrounding clutter echo signal, e.g. non coherent MTi, clutter referenced MTi, externally coherent MTi
• CPC Class Codes: G01S 13/583 using transmission of continuous unmodulated waves, amplitude-, frequency-, or phase-modulated waves and based upon the Doppler effect resulting from movement of targets
• CPC Class Codes: G01S 13/88 Radar or analogous systems specially adapted for specific applications electromagnetic prospecting or detecting of objects, e.g. near-field detection, G01V3/00
• CPC Class Codes: G01S 7/2886 using I/Q processing
• CPC Class Codes: G01S 7/358 using I/Q processing
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

A radar-based physiological motion sensor is disclosed. Doppler-shifted signals can be extracted from the signals received by the sensor. The Doppler-shifted signals can be digitized and processed subsequently to extract information related to the cardiopulmonary motion in one or more subjects. The information can include respiratory rates, heart rates, waveforms due to respiratory and cardiac activity, direction of arrival, abnormal or paradoxical breathing, etc. In various embodiments, the extracted information can be displayed on a display.

Claims

1. A method of sensing motion using a motion sensor, the method comprising:

generating electromagnetic radiation from a source of radiation, wherein the frequency of the electromagnetic radiation is in the radio frequency range;

transmitting the electromagnetic radiation towards a subject using one or more transmitters;

receiving a radiation scattered at least by the subject using one or more receivers;

extracting a Doppler shifted signal from the scattered radiation;

transforming the Doppler shifted signal to a digitized motion signal, said digitized motion signal comprising one or more frames, wherein the one or more frames comprise time sampled quadrature values of the digitized motion signal;

demodulating said one or more frames using a demodulation algorithm executed by a processor to isolate a signal corresponding to a physiological movement of the subject or a part of the subject;

analyzing the signal to obtain information corresponding to a non-cardiopulmonary motion or other signal interference;

processing the signal to obtain information corresponding to the physiological movement of the subject or a part of the subject, substantially separate from said non-cardiopulmonary motion or other signal interference; and

communicating the information to an output system that is configured to perform an output action.

2. The method of claim 1, wherein the output system comprises a display unit configured to display the information.

3. The method of claim 1, wherein the output system comprises an audible system that is configured to report information or alerts audibly based on the information.

4. The method of claim 1, wherein the output system comprises an external medical system that is configured to perform an action based on the information.

5. The method of claim 1, wherein the demodulating algorithm comprises a linear demodulation algorithm, an arc-based demodulation algorithm or a non-linear demodulation algorithm.

6. The method of claim 1, wherein the information is displayed at least alphanumerically, graphically and as a waveform.

7. The method of claim 1, wherein the subject is a human being or an animal and the physiological movement comprises at least one of a motion due to respiratory activity of the subject, motion due to a cardiopulmonary activity of the subject, motion due to a cardiac activity of the subject, motion due to a cardiovascular activity of the subject, and motion due to a physical activity of the subject.

8. The method of claim 1, wherein the demodulating algorithm comprises projecting the signal in a complex plane on a best-fit line, projecting the signal in a complex plane on a principal eigenvector, or aligning a signal arc to a best-fit circle and using the best-fit circle parameters to extract the angular information from the signal arc.

9. The method of claim 1, wherein demodulating said one or more frames comprises:

computing in the processor a first set of covariance matrices of a first subset of frames selected from said one or more frames;

determining a first A-matrix, wherein the first A-matrix comprises a weighted sum of the first set of covariance matrices;

determining a first parameter vector corresponding to a first primary value of the first A matrix; and

storing the first parameter vector in a memory device which is in communication with the processor.

10. The method of claim 9, further comprising computing in the processor a second set of covariance matrices of a second subset of frames selected from said one or more frames;

determining a second A-matrix, wherein the second A-matrix comprises a weighted sum of the second set of covariance matrices;

determining a second parameter vector corresponding to a second primary value of the second A-matrix;

calculating an inner product of the first parameter vector and the second parameter vector;

multiplying the second parameter vector by the sign of the inner product; and

projecting the values of the second frame on the second parameter vector to obtain the demodulated signal.

11. The method of claim 10, wherein the first primary value comprises the largest eigenvalue of the first A-matrix and the first primary vector comprises an eigenvector corresponding to said eigenvalue.

12. The method of claim 10, wherein the second primary value comprises the largest eigenvalue of the second A-matrix and the second primary vector comprises an eigenvector corresponding to said eigenvalue.

13. The method of claim 1, wherein the source of radiation comprises an oscillator.

14. The method of claim 1, wherein said one or more transmitters comprise one or more antennae.

15. The method of claim 1, wherein said one or more receivers comprise one or more antennae or arrays of antennae.

16. The method of claim 1, wherein said transmitting and receiving antennae are the same antennae.

17. The method of claim 1, wherein the receiver comprises a homodyne receiver.

18. The method of claim 1, wherein the receiver comprises a heterodyne receiver.

19. The method of claim 1, wherein the receiver comprises a low-IF receiver configured to transform the Doppler-shifted signal to a Doppler-shifted signal comprising frequencies in a low intermediate frequency range, which is digitized and digitally transformed to a digitized motion signal.

20. The method of claim 1, wherein the processor comprises at least one of a digital signal processor, a microprocessor and a computer.

21. The method of claim 20, further comprising a controller configured to control the processor.

22. The method of claim 1, wherein the output system comprises a display unit configured to display information regarding the physiological movement of a user at a remote location.

23. The method of claim 1, wherein analyzing the signal comprises executing a non-cardiopulmonary motion detection algorithm configured to detect the absence of non-cardiopulmonary motion is detected if the signal comprises a single stable source or the presence of non-cardiopulmonary signal if at least the signal is unstable or at least the signal has multiple sources.

24. The method of claim 1, wherein analyzing the signal comprises executing a non-cardiopulmonary motion detection algorithm configured to detect the presence of non-cardiopulmonary motion if the signal indicates an excursion larger than the subject'"'"'s maximum chest excursion from cardiopulmonary activity.

25. The method of claim 1, wherein analyzing the signal comprises executing a non-cardiopulmonary motion detection algorithm configured to detect the presence of non-cardiopulmonary motion if a best-fit vector related to linear demodulation changes significantly.

26. The method of claim 1, wherein analyzing the signal comprises executing a non-cardiopulmonary motion detection algorithm configured to detect the presence of non-cardiopulmonary motion if a RMS difference between a complex constellation of the signal and a best fit vector related to linear demodulation changes significantly.

27. The method of claim 1, wherein analyzing the signal comprises executing a non-cardiopulmonary motion detection algorithm configured to detect the presence of non-cardiopulmonary motion if an origin or radius of a best-fit circle related to arc-based demodulation changes significantly.

28. The method of claim 1, wherein analyzing the signal comprises executing a non-cardiopulmonary motion detection algorithm configured to detect the presence of non-cardiopulmonary motion if a RMS difference between a complex constellation of the signal and a best-fit circle related to arc-based demodulation changes significantly.

29. The method of claim 1, wherein analyzing the signal comprises:

executing a non-cardiopulmonary motion detection algorithm by a processor to detect the presence or absence of non-cardiopulmonary motion or other signal interference from the digitized motion signal, wherein the non-cardiopulmonary motion detection algorithm comprises a first mode which detects a presence of non-cardiopulmonary motion or other signal interference and a second mode which detects a cessation of non-cardiopulmonary motion or other signal interference.

30. The method of claim 1, further comprising communicating information related to a signal quality of a cardiopulmonary motion signal, based on at least one of:

a presence of non-cardiopulmonary motion or other signal interference, an absence of non-cardiopulmonary motion or other signal interference, a degree of non-cardiopulmonary motion or other signal interference, an assessment of the signal-to-noise ratio, a detection of low signal power, or a detection of signal clipping or other signal interference, to an output system configured to output the information.

31. The method of claim 28, wherein the first mode comprises:

selecting a first subset of frames from said one or more frames and computing in the processor a first set of covariance matrices of the first subset of frames filtered by a low-pass filter;

determining a first A-matrix wherein the A-matrix comprises a weighted sum of the first set of covariance matrices;

determining a first parameter vector corresponding to a first primary value of the first A matrix; and

storing the first parameter vector in a memory device which is in communication with the processor.

32. The method of claim 31, further comprising

computing in the processor a second set of covariance matrices of a second subset of frames filtered by the low-pass filter;

determining a second A-matrix, wherein the A-matrix comprises a weighted sum value of the second set of covariance matrices;

determining a first and a second primary value of the second A-matrix;

determining a second parameter vector corresponding to the first primary value of the second A-matrix;

calculating an inner product of the first parameter vector and the second parameter vector;

calculating a ratio of the first primary value of the second A matrix to the second primary value of the second A matrix;

calculating a first energy corresponding to the average energy of a third subset of frames filtered by a high-pass filter and a second energy corresponding to the average energy of a fourth subset of frames filtered by a high-pass filter; and

calculating a ratio of the second energy to the first energy.

33. The method of claim 31, wherein the first primary value of the first A-matrix comprises the largest eigenvalue of the first A-matrix and the first primary vector comprises an eigenvector corresponding to said eigenvalue.

34. The method of claim 32, wherein the first primary value of the second A-matrix comprises the largest eigenvalue of the second A-matrix, the second primary value of the second A-matrix comprises the second largest eigenvalue of the second A-matrix and the second primary vector of the second A-matrix comprises an eigenvector corresponding to said first primary value of the second A-matrix.

35. The method of claim 28, wherein the method further comprises:

computing in the processor a first condition, said first condition being the inner product is less than a first threshold value or the ratio of the first primary value of the second A-matrix to the second primary value of the second A-matrix is less than a second threshold value or the ratio of the second energy to the first energy is greater than a third threshold value,

wherein the presence of non-cardiopulmonary motion or other signal interference is detected if the first condition is true and the ratio of the second energy to the first energy is greater than a fourth threshold value.

36. The method of claim 35, wherein the first threshold value is approximately between 0.6 and 1.

37. The method of claim 35, wherein the second threshold value is approximately between 4 and 12.

38. The method of claim 35, wherein the third threshold value is approximately between 4 and 20.

39. The method of claim 35, wherein the fourth threshold value is approximately between 0.1 and 0.8.

40. The method of claim 28, wherein the second mode comprises:

selecting in the processor each and every consecutive subset of frames within a fifth subset of frames;

computing in the processor covariance matrices for every subset of frames;

computing in the processor an A′

-matrix for each subset of frames, wherein the A′

-matrix is the average of the covariance matrices in the subset;

computing in the processor a p-matrix, wherein each element of the p-matrix corresponds to a first primary vector of the corresponding A′

-matrix;

computing the inner product of each pair of primary vectors in the p-matrix and selecting a minimum absolute value of the inner products;

calculating an A matrix which is the sum of the covariance matrices in a sixth subset of frames;

determining a first and a second primary value of the A-matrix; and

calculating the ratio of the first primary value of the A matrix to the second primary value of the A matrix.

41. The method of claim 40, wherein the method further comprises:

computing in the processor a second condition, said second condition being the minimum absolute value of the inner products is greater than a first threshold value and the ratio of the first primary value to the second primary value is greater than a second threshold value,

wherein the cessation of non-cardiopulmonary motion or other signal interference is detected if the second condition is true.

42. The method of claim 41, wherein the fifth threshold value is approximately between 0.6 and 1.

43. The method of claim 41, wherein the sixth threshold value is approximately between 4 and 12.

44. The method of claim 40, wherein the first primary vector comprises an eigenvector corresponding to the largest eigenvalue of the corresponding A′

-matrix

45. The method of claim 40, wherein the first primary value comprises the largest eigenvalue of the A-matrix and the second primary value comprises the second largest eigenvalue of the A-matrix.

46. The method of claim 40, further comprising a retrospect step configured to determine a frame from said one or more frames when the non-cardiopulmonary motion substantially ceased.

47. The method of claim 46, wherein one or more frames preceding said frame are discarded.

69. The method of claim 31, wherein the weighted sum is an arithmetic mean.

48. A method of estimating the rate of a physiological motion using a motion sensor, the method comprising:

generating an electromagnetic radiation from a source of radiation, wherein the frequency of the electromagnetic radiation is in the radio frequency range;

transmitting the electromagnetic radiation towards a subject using one or more transmitters;

receiving a radiation scattered at least by the subject using one or more receivers;

extracting a Doppler shifted signal from the scattered radiation;

transforming and digitizing the Doppler shifted signal to a digitized motion signal, said digitized motion signal comprising one or more frames, wherein the one or more frames comprise time sampled quadrature values of the digitized motion signal;

demodulating said one or more frames using a demodulation algorithm executed by a processor to isolate a signal corresponding to a physiological movement of the subject or a part of the subject;

executing a non-cardiopulmonary motion detection algorithm by the processor to identify from the digitized motion signal one or more non-cardiopulmonary motion detection events or other signal interference events corresponding to the presence or absence of a non-cardiopulmonary motion or other signal interference;

executing by a processor a rate estimation algorithm to estimate a rate of the physiological movement; and

providing information related to at least the rate of the physiological movement of the subject or a part of the subject to an output unit that is configured to output the information.

49. The method of claim 48, wherein the rate estimation algorithm comprises:

collecting a plurality of samples from the demodulated frames;

identifying one or more samples from said plurality of samples corresponding to non-cardiopulmonary motion detection events and setting to zero said one or more samples from said plurality of samples to obtain at least a first subset of said plurality of samples; and

subtracting in the processor a mean of the first subset from said first subset.

50. The method of claim 49, further comprising calculating in the processor a Fourier transform of the samples included in the first subset to obtain a magnitude spectrum of said samples in the first subset.

51. The method of claim 50, wherein the estimated frequency domain rate of the physiological movement corresponds to the largest magnitude component in the spectrum of said samples in the first subset.

52. The method of claim 49, further comprising:

identifying either at least three positive zero crossings or at least three negative zero crossings in said first subset;

identifying at least a first value for the samples within a first and a second zero crossing, said first value being the largest magnitude positive value or largest magnitude negative value;

identifying at least a second value for the samples within a second and a third zero crossing, said second value being the largest magnitude positive value or largest magnitude negative value;

comparing said first and second values against a threshold value;

identifying at least a first breathing event if said first value is greater than a threshold value;

identifying at least a second breathing event if said second value is greater than a threshold value; and

estimating a time domain respiration rate based on at least said first and second breathing events and the time interval between said first, second and third zero crossings.

53. The method of claim 52, further comprising:

calculating in the processor a Fourier transform of the samples included in the first subset to obtain a magnitude spectrum of said samples in the first subset;

estimating a frequency domain respiration rate of the physiological movement that corresponds to the largest magnitude spectrum of said samples in the first subset; and

comparing the time domain rate and the frequency domain rate to verify an accuracy of said time domain rate and said frequency domain rate.

54. The method of claim 48, wherein the rate estimation algorithm comprises:

identifying at least three consecutive peaks from said plurality of samples, such that a valley is included between two consecutive peaks; and

determining a respiration rate based on a number of consecutive peaks detected and the time interval between a first and a last peak.

55. The method of claim 48, wherein the rate estimation algorithm comprises:

identifying at least three consecutive valleys from said plurality of samples, such that a peak is included between two consecutive valleys; and

determining a respiration rate based on a number of consecutive valleys detected and the time interval between a first and a last valley.

56. The method of claim 54 or claim 55, wherein the rate algorithm selects whether to identify peaks or valleys depending on which occurs first.

57. The method of claim 54 or claim 55, wherein the rate estimation algorithm averages the respiration rate based on a number of consecutive peaks and the respiration rate based on a number of consecutive valleys to improve the robustness of the rate estimate.

58. A system for sensing a physiological motion, said system comprising:

one or more antennas configured to transmit electromagnetic radiation;

one or more antennas configured to receive electromagnetic radiation;

at least one processor configured to extract information related to cardiopulmonary motion by executing at least one of a demodulation algorithm, a non-cardiopulmonary motion detection algorithm, a rate estimation algorithm, a paradoxical breathing algorithm and a direction of arrival algorithm; and

a communications system configured to communicate with an output device, said output device configured to output information related to the cardiopulmonary motion.

59. A vital signs monitor comprising the system of claim 58, said vital signs monitor configured to monitor at least one of a respiration rate, a heart rate, a depth of breath, respiratory waveform, heart waveform, tidal volume activity, and degree of asynchronous breathing in one or more subjects.

60. A vital signs measurement system comprising the system of claim 58, said system configured to measure at least one of respiration rate, heart rate, ratio of inhale time to exhale time, tidal volume, and depth of breath in one or more subjects.

61. A vital signs measurement system, comprising the system of claim 60, said system configured to perform a measurement at a point in time or at intermittent points in time.

62. An apnea detection system comprising the system of claim 58, said apnea detection system configured to monitor at least one of a respiration rate, respiratory effort, a heart rate, a depth of breath, tidal volume and paradoxical breathing,activity, position, and configured to detect the presence or absence of breathing in one or more subjects.

63. A sleep monitor comprising the system of claim 58, said sleep monitor configured to monitor at least one of a respiration rate, a heart rate, a depth of breath, tidal volume, paradoxical breathing and physical movement in one or more subjects.

64. A psycho-physiological state monitor comprising the system of claim 58, said psycho-physiological state monitor configured to monitor at least one of a respiration rate, respiratory waveform, heart waveform, activity, a heart rate, a depth of breath, tidal volume, inhale time, exhale time, and inhale time to exhale time ratio, in one or more subjects in response to one or more external stimuli.

65. The system of claim 58, configured to send information to an imaging system, said imaging system configured to image a subject, said information configured to synchronize the imaging system to a physiological motion in the subject.

66. The system of claim 58, said monitor configured to assess at least one of the presence or absence of respiratory motion and the presence or absence of heart motion.

67. The system of claim 58, configured to send information to a medical device, said information configured to operate the medical device.

68. A physical activity monitor comprising the system of claim 58, said physical activity monitor configured to monitor at least one of a respiration rate, a heart rate, a depth of breath, tidal volume, frequency of non-cardiopulmonary motion, and duration of non-cardiopulmonary motion in one or more subjects.

70. A method of estimating the presence or absence of paradoxical breathing using a motion sensor, the method comprising:

generating an electromagnetic radiation from a source of radiation, wherein the frequency of the electromagnetic radiation is in the radio frequency range;

transmitting the electromagnetic radiation towards a subject using one or more transmitters;

receiving a radiation scattered at least by the subject using one or more receivers;

extracting a Doppler shifted signal from the scattered radiation;

transforming the Doppler shifted signal to a digitized quadrature motion signal, said digitized quadrature motion signal comprising one or more frames, wherein the one or more frames comprise time sampled quadrature values of the digitized motion signal;

executing a non-cardiopulmonary motion detection algorithm by the processor to identify from the digitized motion signal one or more non-cardiopulmonary motion detection events or other signal interference events corresponding to the presence or absence of a non-cardiopulmonary motion or other signal interference;

executing by a processor a paradoxical breathing indication algorithm to estimate the presence or absence of paradoxical breathing; and

providing information related to at least the presence, absence, or degree of paradoxical breathing.

71. The method of claim 70, wherein the paradoxical breathing indication algorithm comprises:

selecting a subset of the frames;

filtering the frames using a low-pass filter; and

obtaining a complex constellation plot of said filtered frames.

72. The method of claim 71, wherein an absence of paradoxical breathing is detected if the complex constellation plot is approximately linear, such that the magnitude of a first dimension of the complex constellation plot is greater than a second dimension of said complex constellation plot.

73. The method of claim 71, wherein a presence of paradoxical breathing is detected if the complex constellation plot has a first and a second dimension, such that said first and second dimensions have comparable magnitude.

74. The method of claim 71, wherein a paradoxical factor is calculated to estimate a degree of paradoxical breathing

75. The method of claim 74, wherein the paradoxical factor can be estimated by:

calculating in the processor a covariance matrix of the subset;

calculating a first primary value and a second primary value of said covariance matrix;

calculating a first primary vector corresponding to said first primary value and a second primary vector corresponding to said second primary value;

projecting the signal on said first primary vector and determining a first amplitude corresponding to the largest peak-to-peak value of the projected signal on the first primary vector;

projecting the signal on said second primary vector and determining a second amplitude corresponding to the largest peak-to-peak value of the projected signal on the second primary vector;

calculating a first ratio of the first amplitude to the second amplitude;

calculating a second ratio of the first primary value to the second primary value; and

calculating a product of said first ratio to said second ratio.

76. The method of claim 75, wherein the first and second primary value comprise eigenvalues of said covariance matrix and the first and second primary vectors comprise eigenvectors corresponding to said first and second primary value.

77. The method of claim 74, wherein the paradoxical indicator may be calculated with a cost function performed on the paradoxical factor.

78. The method of claim 77, wherein the presence or absence of paradoxical breathing is determined by comparing the output of the cost function to a threshold.

79. The method of claim 74, wherein the paradoxical indicator is analyzed to provide a first indication for absence of paradoxical breathing, a second indication for uncertain results and a third indication for the presence of paradoxical breathing.

80. A method of estimating the direction of arrival using a motion sensor, the method comprising:

generating an electromagnetic radiation from a source of radiation, wherein the frequency of the electromagnetic radiation is in the radio frequency range;

transmitting the electromagnetic radiation towards a subject using one or more transmitters;

receiving a radiation scattered at least by the subject using one or more receivers;

extracting a Doppler shifted signal from the scattered radiation;

transforming the Doppler shifted signal to a digitized quadrature motion signal, said digitized quadrature motion signal comprising one or more frames, wherein the one or more frames comprise time sampled quadrature values of the digitized motion signal from each receiver;

executing by a processor a direction of arrival algorithm to estimate the number of targets and corresponding angles; and

providing information corresponding to at least one of the cardiopulmonary movement of one or more subjects or a part of one or more subjects, the number of subjects, and the direction of one or more subjects to an output unit that is configured to output the information.

81. The method of claim 80, wherein the direction of arrival algorithm comprises:

filtering a subset of frames selected from said one or more frames using a low pass filter, each frame consisting of signals from a plurality of receive channels in said multiple receive antenna array;

calculating the power spectrum density of all the channels for the low pass filtered subset of frames;

using the power of the frequency components in said calculated power spectrum density to determine the frequency components that are most likely to contain a cardiopulmonary signals from one or more subjects;

identifying the angular direction of each frequency component;

identifying at least a first and a second angular direction such that each angular direction is separated from the other angular direction by an angular distance greater than or equal to an angular resolution of said one or more receivers;

eliminating one or more angles that are separated by an angular distance less than the angular resolution of said one or more receivers; and

generating one or more DOA vector with unity magnitude for each target in the said angular direction; and

smoothing the DOA vectors with a weighted average of a current DOA vector and a previous DOA vectors in a buffer

82. The method of claim 81, further comprising:

separating the signal from each angular direction by steering spatial nulls towards the other angular directions;

executing by the processor a non-cardiopulmonary motion detection algorithm to detect a presence or absence of non-cardiopulmonary motion or other signal interference in each separated signal; and

executing by the processor a demodulation algorithm to demodulate each of the separated signals, and process each demodulated signal to obtain information corresponding to the cardiopulmonary motion if absence of non-cardiopulmonary motion is detected.

83. The method of claim 81, further comprising:

isolating the signal from the desired subject by steering spatial nulls toward the other angular directions;

executing by the processor a non-cardiopulmonary motion detection algorithm to detect a presence or absence of non-cardiopulmonary motion or other signal interference in the isolated signal; and

executing by the processor a demodulation algorithm to demodulate the isolated signal, and process the demodulated signal to obtain information corresponding to the subject'"'"'s cardiopulmonary motion if absence of non-cardiopulmonary motion is detected.

84. The method of claim 80, wherein the direction of arrival algorithm comprises:

filtering a subset of frames selected from said one or more frames using a low pass filter, each frame consisting of signals from a plurality of receive channels included in said multiple receiver antenna array;

calculating the power spectrum density of all the channels for the low pass filtered subset of frames;

using the power of the frequency components in said calculated power spectrum density to determine the frequency components that are most likely to contain the cardiopulmonary signals from one or more subjects;

identifying an angular direction of each frequency component;

identifying at least a first and a second angular direction such that each angular direction is separated from the other angular direction by an angular distance greater than or equal to an angular resolution of said multiple receiver antenna array;

eliminating one or more angles that are separated by an angular distance less than the angular resolution of said multiple receiver antenna array;

generating a DOA vector with unity magnitude for each target in the said angular direction;

smoothing the DOA vectors with a weighted average of the current DOA vectors and previous DOA vectors in a buffer;

repeating the DOA algorithm periodically and updating the DOA vectors; and

communicating angles corresponding to the DOA vectors to the output unit.