Method and apparatus for wirelessly monitoring repetitive bodily movements

1. A method for determining a rate of repetitive bodily motion of an individual without requiring contact with the individual, wherein the method is executable by one or more computing devices and comprises:

transmitting a signal for reflection off of the individual;

receiving a reflected signal;

applying a frequency estimation algorithm to the reflected signal to produce an estimated spectral density, wherein the estimated spectral density is in frequency domain and includes at least one frequency component corresponding to the repetitive bodily motion, and wherein the applying the frequency estimation algorithm includes:

estimating a covariance matrix of the reflected signal;

estimating a clutter covariance matrix of the reflected signal;

generating a resultant matrix from the clutter covariance matrix and the covariance matrix; and

applying the frequency estimation algorithm to the resultant matrix to produce the estimated spectral density; and

applying a repetitive bodily motion pattern search function to the estimated spectral density to estimate the rate of the repetitive bodily motion of the individual based on the at least one frequency component.

2. The method of claim 1 further comprises:

the transmitting the signal for reflection off of the individual is performed by a first computing device of the one or more computing devices;

the receiving the reflected signal is performed by the first computing device of the one or more computing devices;

the applying the frequency estimation algorithm to the reflected signal to produce the estimated spectral density is performed by a second computing device of the one or more computing devices; and

the applying the repetitive bodily motion pattern search function to the estimated spectral density is performed by the second computing device of the one or more computing devices.

3. The method of claim 1 , wherein the transmitting the signal comprises:

generating a continuous wave reference signal;

up-converting the continuous wave reference signal to a radio frequency signal; and

transmitting the radio frequency signal as the signal.

4. The method of claim 1 , wherein the reflected signal comprises one or more of:

the transmitted signal;

a clutter signal;

a multipath signal;

a noise component; and

a Doppler shifted version of the repetitive bodily motion.

5. The method of claim 1 , wherein the repetitive bodily motion comprises at least one of:

heartbeat;

respiration;

eye movement;

spasms; and

ticks.

6. The method of claim 1 , wherein the frequency estimation algorithm is a multiple signal classification (MUSIC) algorithm.

7. The method of claim 1 , wherein the applying the repetitive bodily motion pattern search function comprises:

applying a heartbeat and respiration search function to the estimated spectral density to determine principal frequency components of heartbeat and respiration, wherein the heartbeat and the respiration correspond to the repetitive bodily motion; and

estimating a heart rate and a respiration rate from the determined principal frequency components.

8. The method of claim 7 further comprises:

determining characteristics of the individual; and

selecting the heartbeat and respiration search function from a plurality of heartbeat and respiration search functions based on the characteristics of the individual.

9. The method of claim 1 , wherein the applying the repetitive bodily motion pattern search function comprises:

applying an eye movement search function to the estimated spectral density to determine principal frequency components of eye movement, wherein the eye movement corresponds to the repetitive bodily motion; and

estimating an eye movement rate from the determined principal frequency components.

10. The method of claim 1 further comprises:

outputting the rate of the repetitive bodily motion.

11. The method of claim 1 further comprises:

applying the repetitive bodily motion pattern search function to the estimated spectral density to generate an audible representation of the repetitive bodily motion of the individual.

12. The method of claim 1 further comprises:

down-converting the reflected signal to a baseband signal; and

applying the frequency estimation algorithm to the baseband signal to produce the estimated spectral density.

13. A computing device comprises:

a transceiver operable to:

transmit a signal for reflection off of an individual;

receive a reflected signal; and

a memory; and

a processing module operably coupled to the transceiver and the memory, wherein the processing module is operable to:

apply a frequency estimation algorithm to the reflected signal to produce an estimated spectral density, wherein the estimated spectral density is in frequency domain includes at least one frequency component corresponding to the repetitive bodily motion, and wherein the processing module is operable to apply the frequency estimation algorithm by:

estimating a covariance matrix of the reflected signal;

estimating a clutter covariance matrix of the reflected signal;

generating a resultant matrix from the clutter covariance matrix and the covariance matrix; and

applying the frequency estimation algorithm to the resultant matrix to produce the estimated spectral density; and

apply a repetitive bodily motion pattern search function to the estimated spectral density to estimate the rate of the repetitive bodily motion of the individual based on the at least one frequency component.

14. The computing device of claim 13 , wherein the transceiver further functions to transmit the signal by:

generating a continuous wave reference signal;

up-converting the continuous wave reference signal to a radio frequency signal; and

transmitting the radio frequency signal as the signal.

15. The computing device of claim 13 , wherein the reflected signal comprises one or more of:

the transmitted signal;

a clutter signal;

a multipath signal;

a noise component; and

a Doppler shifted version of the repetitive bodily motion.

16. The computing device of claim 13 , wherein the repetitive bodily motion comprises at least one of:

heartbeat;

respiration;

eye movement;

spasms; and

ticks.

17. The computing device of claim 13 , wherein the frequency estimation algorithm is a multiple signal classification (MUSIC) algorithm.

18. The computing device of claim 13 , wherein the processing module further functions to apply the repetitive bodily motion pattern search function by:

applying a heartbeat and respiration search function to the estimated spectral density to determine principal frequency components of heartbeat and respiration, wherein the heartbeat and the respiration correspond to the repetitive bodily motion; and

estimating a heart rate and a respiration rate from the determined principal frequency components.

19. The computing device of claim 18 , wherein the processing module is further operable to:

determine characteristics of the individual; and

select the heartbeat and respiration search function from a plurality of heartbeat and respiration search functions based on the characteristics of the individual.

20. The computing device of claim 13 , wherein the processing module further functions to apply the repetitive bodily motion pattern search function by:

applying an eye movement search function to the estimated spectral density to determine principal frequency components of eye movement, wherein the eye movement corresponds to the repetitive bodily motion; and

estimating an eye movement rate from the determined principal frequency components.

21. The computing device of claim 13 , wherein the processing module is further operable to:

output the rate of the repetitive bodily motion.

22. The computing device of claim 13 , wherein the processing module is further operable to:

apply the repetitive bodily motion pattern search function to the estimated spectral density to generate an audible representation of the repetitive bodily motion of the individual.

23. The computing device of claim 13 , further comprises:

the transceiver is further operable to down-convert the reflected signal to a baseband signal; and

the processing module is further operable to apply the frequency estimation algorithm to the baseband signal to produce the estimated spectral density.