Method and system for self-monitoring of environment-related respiratory ailments

• US 20090112114A1
• Filed: 12/06/2007
• Published: 04/30/2009
• Est. Priority Date: 10/26/2007
• Status: Abandoned Application

• Current Assignee: Sharp Laboratories of America Incorporated (Hon Hai Precision Industry Co., Ltd.)
• Original Assignee: Sharp Laboratories of America Incorporated (Hon Hai Precision Industry Co., Ltd.)
• Inventors: Chan, Wai-Chung, Ayyagari, Deepak V.
• Application Number:
  US11/999,569
• Publication Number:
  US 20090112114A1
• Time in Patent Office: Days
• US Class Current: 600/529
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 7/003 Detecting lung or respiration noise

Abstract

Methods and systems for continual self-monitoring of respiratory health and components for use therewith. The present methods and systems and their related components improve the standard of core in respiratory health self-monitoring by providing continual and unobtrusive monitoring that accounts for environmental, physiological and patient background information, and is capable of yielding a complex array of respiratory health-preserving responses. In some embodiments, the present methods and systems leverage ubiquitous handheld electronic devices [e.g. cell phones and personal data assistants (PDA)] for respiratory health self-monitoring.

Claims

1. A method for respiratory health self-monitoring, comprising the steps of:

receiving physiological data collected from a patient;

receiving environmental data; and

generating respiratory health data for the patient based at least in part on the physiological data and the environmental data.

2. The method of claim 1, wherein the physiological data and the environmental data comprise data received on a mobile electronic device at regular intervals.

3. The method of claim 2, wherein the physiological data further comprise data received on a mobile electronic device episodically.

4. The method of claim 1, wherein the respiratory health data are further generated based at least in part on statically configured patient background data.

5. The method of claim 4, wherein patient background data comprise at least one of the behavior pattern data, co-morbidity data, medication data, age data, height data, weight data, gender data, race data or genetic background data.

6. The method of claim 1, wherein the respiratory health data comprise present health data generated using current physiological data and environmental data.

7. The method of claim 1, wherein the respiratory health data comprise health trend data generated using historical physiological data and environmental data.

8. The method of claim 1, wherein the respiratory health data comprise health cross-correlation data generated using historical physiological data and environmental data.

9. The method of claim 1, further comprising the step of outputting a respiratory health alert in response to the respiratory health data.

10. The method of claim 1, further comprising the step of controlling an environment control system in response to the respiratory health data.

11. The method of claim 1, further comprising the step of generating a predictive model for the patient in response to the respiratory health data.

12. The method of claim 1 wherein the physiological data comprise at least one of lung sound data, blood oxygen saturation (SpO2) data or pulse rate data.

13. The method of claim 1, wherein the environmental data comprise at least one of airborne particulate data, temperature data or relative humidity data.

14. The method of claim 1, wherein the environmental data comprise at least one of airborne particulate presence, type or density data.

15. A handset, comprising:

at least one network interface; and

a processor communicatively coupled with the network interface, wherein the network interface is adapted to receive at regular intervals via a wireless link physiological data from at least one physiological monitor and environmental data from at least one environmental monitor and the processor is adapted to generate respiratory health data for a patient operatively coupled to the at least one physiological monitor based at least in part on the physiological data and the environmental data.

16. A body area network (BAN), comprising:

at least one physiological monitor operatively coupled to a patient;

at least one environmental monitor; and

a handset communicatively coupled with the physiological monitor and the environmental monitor, wherein the handset generates respiratory health data for the patient based at least in part on physiological data acquired by the handset at regular intervals from the physiological monitor and the environmental monitor.

17. The BAN of claim 16, wherein the respiratory health data are further generated based at least in part on patient background data statically configured on the handset.

18. The BAN of claim 16, wherein the handset outputs the respiratory health data on a user interface of the handset.

19. The BAN of claim 16, wherein the handset outputs a respiratory health alert in response to the respiratory health data.

20. The BAN of claim 16, wherein the handset transmits an environment control message from the handset in response to the respiratory health data.