Granted Patent B2

US 10,495,725 · App. 16/200,616 · Granted Dec 3, 2019

Inventors: Feng Zhang (Greenbelt, MD); Chen Chen (Burlingame, CA); Qinyi Xu (College Park, MD); Beibei Wang (Clarksville, MD); Chenshu Wu (Greenbelt, MD); Hangfang Zhang (Greenbelt, MD); Chau-Wai Wong (Raleigh, NC); David N. Claffey (Somerville, MA); Chun-I Chen (Brookeville, MD); Hung-Quoc Duc Lai (Parkville, MD); Zhung-Han Wu (College Park, MD); Min Wu (Clarksville, MD); Yi Han (Ellicott City, MD); Oscar Chi-Lim Au (San Jose, CA); K. J. Ray Liu (Potomac, MD)

Assignee: ORIGIN WIRELESS, INC.

G01S5/06G01S5/0226G01S5/0252G01S5/0273

Abstract

Claims (159)

1. A system for monitoring a repeating motion in a venue, comprising:

a transmitter located at a first position in the venue and configured for transmitting a wireless signal through a wireless multipath channel impacted by the repeating motion of an object in the venue;

a receiver located at a second position in the venue and configured for:

receiving the wireless signal through the wireless multipath channel impacted by the repeating motion of the object in the venue, and

obtaining a time series of channel information (CI) of the wireless multipath channel based on the wireless signal; and

a repeating motion monitor configured for monitoring a periodic characteristics of the repeating motion of the object based on the time series of CI,

wherein:

each CI comprises N 1 components, wherein N 1 is an integer greater than 1; and

the repeating motion monitor is further configured for:

decomposing the time series of CI into N 1 component time series, each respective component time series comprising a respective one of the N 1 components of each CI,

computing N 1 component-feature time series based on the N 1 component time series, each respective component-feature time series comprises a feature of the respective component of each CI,

associating a current periodic characteristic of the repeating motion of the object with a current sliding time window,

computing N 1 component sliding functions, each respective component sliding function being a respective transform of a respective component-feature time series in the current sliding time window, and

monitoring the current periodic characteristics of the repeating motion of the object based on at least one of: the N 1 component sliding functions, and a combined sliding function computed based on the N 1 component sliding functions.

2. The system of claim 1 , wherein:

the object is at least one of: a life, a device, and a land;

the repeating motion represents at least one of the following of the object:

breathing, heartbeat, periodic hand gesture, periodic gait, rotation, vibration, and earthquake; and

the periodic characteristics represents a rate of the repeating motion.

3. The system of claim 1 , wherein the repeating motion monitor is coupled to at least one of: the transmitter, the receiver, an additional transmitter, an additional receiver, a cloud server, a fog server, a local server, and an edge server.

4. The system of claim 1 , wherein:

each CI comprises at least one of:

a channel frequency response (CFR) comprising N 1 complex frequency components each of which is associated with a frequency,

a channel impulse response (CIR) comprising N 1 complex time components each of which is associated with a time, and

a decomposition with N 1 components each of which is associated with an index;

the feature comprises at least one of: phase, magnitude, real component, purely imaginary component, mean, mode, median, expected value, variance, square, cube, power, polynomial, exponential, derivative, and integration;

the respective transform comprises at least one of: Fourier transform, Sine transform, Cosine transform, Laplace transform, wavelet transform, Hadamard transform, Hilbert transform, slant transform, sparse transform, graph-based transform, lowpass filtering, highpass filtering, bandpass filtering, finite-impulse-response (FIR) filtering, infinite-impulse-response (IIR) filtering, linear filtering, convolution, nonlinear function, statistical function, mean function, variance function, autocorrelation function (ACF), moment generating function, nonlinear filtering, signal processing, graph-based processing, particle filtering, integration, differentiation, first order derivative, second order derivative, high order derivative, neural network, learning network, feature extraction, denoising, smoothing, signal enhancement, coding, encryption, mapping, remapping, vector quantization, autoregressive (AR) filtering, moving average (MA) filtering, autoregressive moving average (ARMA) filtering, median filtering, mode filtering, ordered statistics filtering, percentile filtering, eigen-decomposition, singular-value decomposition, orthogonal decomposition, sparse approximation, principle component analysis (PCA), and independent component analysis (ICA), projection, decomposition, sampling, re-sampling, random sampling, down-sampling, up-sampling, interpolation, and extrapolation; and

the combined sliding function comprises at least one of: arithmetic mean, geometric mean, harmonic mean, power mean, f-mean, linear combination, weighted mean, weighted arithmetic mean, weighted geometric mean, weighted harmonic mean, weighted truncated mean, interquartile mean, and generalized mean, of the N 1 component functions.

5. The system of claim 1 , wherein the repeating motion monitor further comprises:

a repetition feature extractor configured for computing at least one feature of at least one of: the combined sliding function, and each of the N 1 component sliding functions, wherein the at least one feature comprises at least one of: an extremum, a local maximum, a local minimum, a zero-crossing, a local maximum derivative, a local maximum high-order derivative, a local minimum derivative, a local minimum high-order derivative, a local zero derivative, a positive quantity, a negative quantity, a significant quantity, a positive maximum, a positive minimum, a negative maximum, a negative minimum, a first maximum, a first minimum, a second maximum, a second minimum, an N-th maximum, an N-th minimum, a significant maximum, a significant minimum, a significant derivative, and a significant high-order derivative; and

a repetition rate estimator configured for monitoring the current periodic characteristics of the repeating motion of the object based on at least one of:

the at least one feature of at least one of: the combined sliding function and each of the N 1 component sliding functions, and

at least one domain value associated with the at least one feature of at least one of: the combined sliding function and each of the N 1 component sliding functions.

6. The system of claim 1 , wherein the repeating motion monitor further comprises:

a repetition feature extractor configured for computing at least one dominant periodic feature of each of the N 1 component-feature time series in the current sliding time window, wherein the at least one dominant periodic feature comprises at least one of: a spectral extremum, a local spectral maximum, a local spectral minimum, a spectral zero-crossing, a local maximum spectral derivative, a local maximum spectral high-order derivative, a local minimum spectral derivative, a local minimum spectral high-order derivative, a local zero spectral derivative, a positive spectral quantity, a negative spectral quantity, a significant spectral quantity, a positive spectral maximum, a positive spectral minimum, a negative spectral maximum, a negative spectral minimum, a first spectral maximum, a first spectral minimum, a second spectral maximum, a second spectral minimum, an N-th spectral maximum, an N-th spectral minimum, a significant spectral maximum, a significant spectral minimum, a significant spectral derivative, and a significant spectral high-order derivative; and

a repetition rate estimator configured for monitoring the current periodic characteristics of the repeating motion of the object based on at least one of:

the dominant periodic features of the N 1 component-feature time series in the current sliding time window, and

at least one combined dominant periodic feature based on the dominant periodic features of the N 1 component-feature time series in the current sliding time window.

7. The system of claim 1 , wherein the repeating motion monitor further comprises:

a repetition feature extractor configured for computing at least one dominant periodic feature of each of the N 1 component time series in the current sliding time window, wherein the at least one dominant periodic feature comprises at least one of: a spectral extremum, a local spectral maximum, a local spectral minimum, a spectral zero-crossing, a local maximum spectral derivative, a local maximum spectral high-order derivative, a local minimum spectral derivative, a local minimum spectral high-order derivative, a local zero spectral derivative, a positive spectral quantity, a negative spectral quantity, a significant spectral quantity, a positive spectral maximum, a positive spectral minimum, a negative spectral maximum, a negative spectral minimum, a first spectral maximum, a first spectral minimum, a second spectral maximum, a second spectral minimum, an N-th spectral maximum, an N-th spectral minimum, a significant spectral maximum, a significant spectral minimum, a significant spectral derivative, a significant spectral high-order derivative, and another periodic feature; and

a repetition rate estimator configured for monitoring the periodic characteristics of the repeating motion of the object based on at least one of:

the dominant periodic features of the N 1 component time series in the current sliding time window, and

at least one combined dominant periodic feature based on the dominant periodic features of the N 1 component time series in the current sliding time window.

8. The system of claim 1 , wherein the repeating motion monitor further comprises:

a repeating signal enhancer configured for computing at least one of:

N 1 component frequency functions based on a frequency transform of each of the N 1 respective component sliding functions, and

a combined frequency function based on the frequency transform of the combined sliding function;

a repetition feature extractor configured for computing at least one feature of at least one of:

the N 1 component frequency functions, and

the combined frequency function; and

a repetition rate estimator configured for monitoring at least one of: the current periodic characteristics of the repeating motion of the object, and a current periodic characteristics of an additional repeating motion of an additional object in the venue, based on the at least one feature.

9. The system 8 , wherein the repeating motion monitor further comprises:

a channel information processor configured for identifying and removing any unallowed feature from the at least one feature, wherein:

a feature outside an allowable range is identified as an unallowed feature,

a feature in the allowable range is identified as an unallowed feature when it is greater than an adaptive threshold that is computed adaptively based on an adaptive background noise floor, and

the adaptive background noise floor is computed based on at least one feature outside the allowable range.

10. The system of claim 8 , wherein:

the wireless multipath channel is further impacted by the additional repeating motion of the additional object in the venue;

the at least one feature comprises: a first feature associated with the periodic characteristics of the repeating motion of the object, and a second feature associated with the periodic characteristics of the additional repeating motion of the additional object;

the periodic characteristics of the repeating motion of the object and the periodic characteristics of the additional repeating motion of the additional object are monitored contemporaneously based on the first feature and the second feature.

11. The system of claim 1 , wherein the repeating motion monitor is further configured for:

computing at least one analytics based on the periodic characteristics of the repeating motion of the object.

12. The system of claim 1 , wherein the repeating motion monitor is further configured for:

transmitting information of the periodic characteristics of the repeating motion of the object to a user interface.

13. The system of claim 12 , further comprising:

the user interface configured for presenting a summary of the periodic characteristics of the repeating motion of the object based on the transmitted information.

14. The system of claim 1 , wherein:

the repeating motion of the object is locally repeating such that, in each of a series of overlapping time windows, the locally repeating motion resembles a periodic motion; and

the periodic characteristics of the locally repeating motion of the object is monitored in each of the series of overlapping time windows.

15. The system of claim 1 , wherein the repeating motion monitor comprises a repetition rate estimator configured for:

processing the periodic characteristics of the repeating motion of the object;

computing a time function based on the periodic characteristics of the repeating motion of the object;

analyzing at least one of: the periodic characteristics of the repeating motion of the object, and the time function; and

computing at least one analytics based on at least one of: the periodic characteristics of the repeating motion of the object, and the time function.

16. The system of claim 1 , wherein the repeating motion monitor is further configured for:

performing a joint analysis on the periodic characteristics of the repeating motion of the object and at least one of: a different characteristics computed based on the time series of CI, and another different characteristics computed based on a different time series of CI of the wireless multipath channel;

computing at least one joint analytics based on the joint analysis.

17. The system of claim 1 , wherein the repeating motion monitor comprises a repetition rate estimator configured for:

estimating a missing periodic characteristics of the repeating motion of the object based on at least one of: available periodic characteristics of the repeating motion of the object computed based on the time series of CI, a different characteristics computed based on the time series of CI, and another different characteristics computed based on a different time series of CI of the wireless multipath channel.

18. The system of claim 1 , wherein the repeating motion monitor is further configured for:

monitoring an additional periodic characteristics of an additional repeating motion of an additional object contemporaneously based on the time series of CI, wherein the wireless multipath channel is further impacted by the additional repeating motion of the additional object in the venue.

19. The system of claim 1 , wherein the repeating motion monitor comprises a repetition rate estimator configured for:

computing a trend function by lowpass filtering a time function of the periodic characteristics of the repeated motion of the object;

analyzing at least one of: the time function, the trend function, and a detrended function computed by subtracting the trend function from the time function of the periodic characteristics; and

monitoring the repeating motion of the object based on at least one of: the time function, the trend function, and the detrended function.

20. A method for monitoring a repeating motion in a venue, comprising:

receiving a wireless signal through a wireless multipath channel impacted by the repeating motion of an object in the venue;

obtaining a time series of channel information (CI) of the wireless multipath channel based on the wireless signal; and

monitoring a periodic characteristics of the repeating motion of the object based on the time series of CI,

wherein:

each CI comprises N 1 components, wherein N 1 is an integer greater than 1; and

the method further comprises:

decomposing the time series of CI into N 1 component time series each respective component time series comprising a respective one of the N 1 components of each CI,

computing N 1 component-feature time series based on the N 1 component time series, each respective component-feature time series comprises a feature of the respective component of each CI,

associating a current periodic characteristic of the repeating motion of the object with a current sliding time window,

computing N 1 component sliding functions, each respective component sliding function being a respective transform of a respective component-feature time series in the current sliding time window, and

monitoring the current periodic characteristics of the repeating motion of the object based on at least one of: the N 1 component sliding functions, and a combined sliding function computed based on the N 1 component sliding functions.

21. The method of claim 20 , wherein:

the object is at least one of: a life, a device, and a land;

the repeating motion represents at least one of the following of the object:

breathing, heartbeat, periodic hand gesture, periodic gait, rotation, vibration, and earthquake; and

the periodic characteristics represents a rate of the repeating motion.

22. The method of claim 20 , wherein:

each CI comprises at least one of:

a channel frequency response (CFR) comprising N 1 complex frequency components each of which is associated with a frequency,

a channel impulse response (CIR) comprising N 1 complex time components each of which is associated with a time, and

a decomposition with N 1 components each of which is associated with an index;

the feature comprises at least one of: phase, magnitude, real component, purely imaginary component, mean, mode, median, expected value, variance, square, cube, power, polynomial, exponential, derivative, and integration;

the respective transform comprises at least one of: Fourier transform, Sine transform, Cosine transform, Laplace transform, wavelet transform, Hadamard transform, Hilbert transform, slant transform, sparse transform, graph-based transform, lowpass filtering, highpass filtering, bandpass filtering, finite-impulse-response (FIR) filtering, infinite-impulse-response (IIR) filtering, linear filtering, convolution, nonlinear function, statistical function, mean function, variance function, autocorrelation function (ACF), moment generating function, nonlinear filtering, signal processing, graph-based processing, particle filtering, integration, differentiation, first order derivative, second order derivative, high order derivative, neural network, learning network, feature extraction, denoising, smoothing, signal enhancement, coding, encryption, mapping, remapping, vector quantization, autoregressive (AR) filtering, moving average (MA) filtering, autoregressive moving average (ARMA) filtering, median filtering, mode filtering, ordered statistics filtering, percentile filtering, eigen-decomposition, singular-value decomposition, orthogonal decomposition, sparse approximation, principle component analysis (PCA), and independent component analysis (ICA), projection, decomposition, sampling, re-sampling, random sampling, down-sampling, up-sampling, interpolation, and extrapolation; and

the combined sliding function comprises at least one of: arithmetic mean, geometric mean, harmonic mean, power mean, f-mean, linear combination, weighted mean, weighted arithmetic mean, weighted geometric mean, weighted harmonic mean, weighted truncated mean, interquartile mean, and generalized mean, of the N 1 component functions.

23. The method of claim 20 , further comprising:

computing at least one feature of at least one of: the combined sliding function, and each of the N 1 component sliding functions, wherein the at least one feature comprises at least one of: an extremum, a local maximum, a local minimum, a zero-crossing, a local maximum derivative, a local maximum high-order derivative, a local minimum derivative, a local minimum high-order derivative, a local zero derivative, a positive quantity, a negative quantity, a significant quantity, a positive maximum, a positive minimum, a negative maximum, a negative minimum, a first maximum, a first minimum, a second maximum, a second minimum, an N-th maximum, an N-th minimum, a significant maximum, a significant minimum, a significant derivative, and a significant high-order derivative; and

monitoring the current periodic motion of the object based on at least one of:

the at least one feature of at least one of: the combined sliding function and each of the N 1 component sliding functions, and

at least one domain value associated with the at least one feature of at least one of: the combined sliding function and each of the N 1 component sliding functions.

24. The method of claim 20 , further comprising:

computing at least one dominant periodic feature of each of the N 1 component-feature time series in the current sliding time window, wherein the at least one dominant periodic feature comprises at least one of: a spectral extremum, a local spectral maximum, a local spectral minimum, a spectral zero-crossing, a local maximum spectral derivative, a local maximum spectral high-order derivative, a local minimum spectral derivative, a local minimum spectral high-order derivative, a local zero spectral derivative, a positive spectral quantity, a negative spectral quantity, a significant spectral quantity, a positive spectral maximum, a positive spectral minimum, a negative spectral maximum, a negative spectral minimum, a first spectral maximum, a first spectral minimum, a second spectral maximum, a second spectral minimum, an N-th spectral maximum, an N-th spectral minimum, a significant spectral maximum, a significant spectral minimum, a significant spectral derivative, and a significant spectral high-order derivative; and

monitoring the current periodic characteristics of the repeating motion of the object based on at least one of:

the dominant periodic features of the N 1 component-feature time series in the current sliding time window, and

at least one combined dominant periodic feature based on the dominant periodic features of the N 1 component-feature time series in the current sliding time window.

25. The method of claim 20 , further comprising:

computing at least one dominant periodic feature of each of the N 1 component time series in the current sliding time window, wherein the at least one dominant periodic feature comprises at least one of: a spectral extremum, a local spectral maximum, a local spectral minimum, a spectral zero-crossing, a local maximum spectral derivative, a local maximum spectral high-order derivative, a local minimum spectral derivative, a local minimum spectral high-order derivative, a local zero spectral derivative, a positive spectral quantity, a negative spectral quantity, a significant spectral quantity, a positive spectral maximum, a positive spectral minimum, a negative spectral maximum, a negative spectral minimum, a first spectral maximum, a first spectral minimum, a second spectral maximum, a second spectral minimum, an N-th spectral maximum, an N-th spectral minimum, a significant spectral maximum, a significant spectral minimum, a significant spectral derivative, a significant spectral high-order derivative, and another periodic feature; and

monitoring the periodic characteristics of the repeating motion of the object based on at least one of:

the dominant periodic features of the N 1 component time series in the current sliding time window, and

at least one combined dominant periodic feature based on the dominant periodic features of the N 1 component time series in the current sliding time window.

26. A receiver of a motion monitoring system, comprising:

a wireless circuitry configured to receive a wireless signal through a wireless multipath channel impacted by a repeating motion of an object in a venue, wherein the wireless signal is transmitted by a transmitter of the motion monitoring system;

a processor communicatively coupled with the wireless circuitry;

a memory communicatively coupled with the processor; and

a set of instructions stored in the memory which, when executed, causes the processor to obtain a time series of channel information (CI) of the wireless multipath channel based on the wireless signal, wherein:

the time series of CI is to be used by a repeating motion monitor of the motion monitoring system to monitor a periodic characteristics of the repeating motion,

wherein:

each CI comprises N 1 components. wherein N 1 is an integer greater than 1; and

the repeating motion monitor is configured for:

decomposing the time series of CI into N 1 component time series each respective component time series comprising a respective one of the N 1 components of each CI,

computing N 1 component-feature time series based on the N 1 component time series, each respective component-feature time series comprises a feature of the respective component of each CI,

associating a current periodic characteristic of the repeating motion of the object with a current sliding time window,

computing N 1 component sliding functions, each respective component sliding function being a respective transform of a respective component-feature time series in the current sliding time window, and

monitoring the current periodic characteristics of the repeating motion of the object based on at least one of: the N 1 component sliding functions, and a combined sliding function computed based on the N 1 component sliding functions.

27. The receiver of claim 26 , wherein:

the object is at least one of: a life, a device, and a land;

the repeating motion represents at least one of the following of the object:

breathing, heartbeat, periodic hand gesture, periodic gait, rotation, vibration, and earthquake;

the periodic characteristics represents a rate of the repeating motion; and

the repeating motion monitor is coupled to at least one of: the transmitter, the receiver, an additional transmitter, an additional receiver, a cloud server, a fog server, a local server, and an edge server.

28. monitor of a motion monitoring system, comprising:

a processor;

a memory communicatively coupled with the processor; and

a set of instructions stored in the memory which, when executed, causes the processor to perform:

obtaining a time series of channel information (CI) of a wireless multipath channel from a receiver of the motion monitoring system, wherein the receiver extracts the time series of CI from a wireless signal received from a transmitter of the motion monitoring system through the wireless multipath channel impacted by a repeating motion of an object in a venue, and

monitoring a periodic characteristics of the repeating motion of the object based on the time series of CI,

wherein:

each CI comprises N 1 components, wherein N 1 is an integer a than 1; and

monitoring the periodic characteristics of the repeating motion of the object based on the time series of CI comprises:

decomposing the time series of CI into Ni component time series, each respective component time series comprising a respective one of the NI components of each CI,

computing N 1 component-feature time series based on the N 1 component time series, each respective component-feature time series comprises a feature of the respective component of each CI,

associating a current periodic characteristic of the repeating motion of the object with a current sliding time window,

computing N 1 component sliding functions, each respective component sliding function being a respective transform of a respective component-feature time series in the current sliding time window, and

monitoring the current periodic characteristics of the repeating motion of the object based on at least one of: the N 1 component sliding functions, and a combined sliding function computed based on the N 1 component sliding functions.

Assignments (2)

Continuity (70)

Cited By (2)

US 12,436,253 US 12,566,244