NON-CONTACT DETECTION OF PHYSIOLOGICAL DATA USING STOCHASTIC RESONANCE

• US 20110196243A1
• Filed: 02/05/2010
• Published: 08/11/2011
• Est. Priority Date: 02/05/2010
• Status: Abandoned Application

• Current Assignee: L&P Property Management Company (Leggett & Platt Incorporated)
• Original Assignee: L&P Property Management Company (Leggett & Platt Incorporated)
• Inventors: Brummett, Travis, Wu, Riheng, Turner, Jason, Browning, Caleb
• Application Number:
  US12/701,402
• Publication Number:
  US 20110196243A1
• Time in Patent Office: Days
• US Class Current: 600/484
• CPC Class Codes: A61B 2503/22 Motor vehicles operators, e.g. drivers, pilots, captains
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/6891 Furniture
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7239 using differentiation including higher order derivatives
• CPC Class Codes: A61B 5/726 using Wavelet transforms

Abstract

Obtaining physiological data from a living being using non-contact sensors is disclosed. The non-contact sensors are disposed within, for example, a vehicle seat or bed. Signals detected by the non-contacting sensors, which include both physiological signals and ambient noise, are transmitted to a processing device which is positioned nearby; such as in the vehicle seat or bed. The processing device employs a stochastic resonance function whereby a signal-to-noise ratio of the physiological signals is improved. A wavelet multi-scale decomposition function and an adaptive threshold function are then used to identify the physiological signals and distinguish them from the ambient noise. The resulting signals are measured and then made available at an output of the processing device for transmission to and use by a human-machine interface.

Claims

1. A system for sensing and measuring physiological signals of a living being, the system comprising:

a plurality of non-contact sensors disposed to sense the physiological signals along with ambient noise; and

a processor connected to the plurality of the sensors for receiving the physiological signals and the ambient noise, wherein the processor includes;

a stochastic resonance module for performing a stochastic resonance function on the physiological signals and the ambient noise to increase a signal-to-noise ratio of the physiological signals;

an identifying module for distinguishing the physiological signals from the ambient noise; and

a measuring module for measuring values associated with the physiological signals.

2. A system as claimed in claim 1, wherein the physiological signals are at least one of a heart rate and respiration.

3. A system as claimed in claim 1, wherein the plurality of sensors are piezoelectric pressure sensors.

4. A system as claimed in claim 1, wherein the plurality of sensors are disposed within a seat.

5. A system as claimed in claim 4, wherein the seating apparatus is at least one of a seat of a vehicle and a medical examination chair.

6. A system as claimed in claim 1, wherein the plurality of sensors are disposed within a bed.

7. A system as claimed in claim 1, further comprising a data acquisition device, wherein the data acquisition device receives the physiological signals and the ambient noise from the plurality of sensors, performs an analog-to-digital conversion of the physiological signals and the ambient noise, and outputs the digital physiological signals and ambient noise to the processor.

8. A system as claimed in claim 1, wherein the apparatus further includes a summation module for adding the physiological signals together prior to being input into the stochastic resonance module.

9. A system as claimed in claim 1, wherein the apparatus further includes a data decimation module for taking the physiological signals and the ambient noise from a high sampling rate down to a lower sampling rate.

10. A system as claimed in claim 1, wherein the identifying module includes a wavelet multi-scale decomposition function.

11. A system as claimed in claim 10, wherein a db5 wavelet is used as a mother wavelet of the wavelet multi-scale decomposition function.

12. A system as claimed in claim 1, wherein the identifying module includes an envelope detector for eliminating negative frequencies of the physiological signals and the ambient noise.

13. A system as claimed in claim 1, wherein the identifying module includes an adaptive threshold function for incorporating an adaptive threshold between the physiological signals and the ambient noise.

14. A method for sensing and measuring physiological signals of a living being, the method comprising:

sensing the physiological signals along with ambient noise using a plurality of sensors disposed in non-contact proximity to the living being;

performing a stochastic resonance function on the physiological signals and the ambient noise to increase a signal-to-noise ratio of the physiological signals;

distinguishing the physiological signals from the ambient noise after the signal-to-noise ratio has been increased; and

measuring values of the physiological signals that have been distinguished from the ambient noise.

15. A method as claimed in claim 14, wherein the physiological signals are at least one of a heart rate and a respiration rate.

16. A method as claimed in claim 14, wherein the plurality of sensors are piezoelectric pressure sensors.

17. A method as claimed in claim 14, wherein the plurality of sensors are disposed within a seat.

18. A method as claimed in claim 17, wherein the seat is at least one of a seat of a vehicle and a medical examination chair.

19. A method as claimed in claim 14, wherein the plurality of sensors are disposed within a bed.

20. A method as claimed in claim 14, further comprising performing an analog-to-digital conversion of the physiological signals and the ambient noise prior to performing the stochastic resonance function.

21. A method as claimed in claim 14, further comprising adding the physiological signals together prior to performing the stochastic resonance function.

22. A method as claimed in claim 14, further comprising performing a data decimation function to take the physiological signals and the ambient noise from a high sampling rate down to a lower sampling rate.

23. A method as claimed in claim 14, wherein distinguishing the physiological signals from the ambient noise includes performing a wavelet multi-scale decomposition function.

24. A method as claimed in claim 23, wherein a db5 wavelet is used as a mother wavelet of the wavelet multi-scale decomposition function.

25. A method as claimed in claim 14, wherein distinguishing the physiological signals from the ambient noise includes performing an envelope detector function for eliminating negative frequencies of the physiological signals and the ambient noise.

26. A method as claimed in claim 14, wherein distinguishing the physiological signals from the ambient noise includes performing an adaptive threshold function for incorporating an adaptive threshold between the physiological signals and the ambient noise.

27. An apparatus for processing physiological signals of a living being, the apparatus comprising:

an input for receiving the physiological signals along with ambient noise that have been sensed by a plurality of sensors disposed in non-contact proximity to the living being;

a stochastic resonance module for performing a stochastic resonance function on the physiological signals and the ambient noise to increase a signal-to-noise ratio of the physiological signals;

an identifying module for distinguishing the physiological signals from the ambient noise; and

a measuring module for measuring values associated with the physiological signals.