Predictive respiratory monitor and system

1. A mobile medical device for monitoring a respiratory condition in a subject, the medical device comprising:

a strain sensor comprising a wrinkled metal film that has integrated hierarchal nano- and micro-sized wrinkle structures disposed on a flexible substrate, wherein the strain sensor is configured to be adhered to the skin of a patient, the strain sensor being configured to yield a resistance signal that is modulated by movements of a chest of the subject during respiration, wherein a detectable change in resistance of the sensor occurs upon stretching of the strain sensor; and

an electrical connection that connects the strain sensor to a sensor attachment module (SAM), wherein the SAM comprises:

(a) a measuring circuit configured to receive the resistance signal from the strain sensor, and to measure a change in the resistance signal,

(b) a processor configured to compare one or more characteristics of the change in the resistance signal with a stored threshold criteria and to determine whether the change in the resistance signal is indicative of an adverse respiratory event, and

(c) a transmitter configured to wirelessly transmit data to a respiration monitoring system (RMS),

wherein the processor is configured to control transmission of data by the transmitter to the RMS.

2. The mobile medical device of claim 1 , wherein the strain sensor is mounted on or integrated into an article of clothing.

3. The mobile medical device of claim 2 , wherein the article of clothing is a belt.

4. The mobile medical device of claim 1 , wherein the mobile medical device comprises an electronic device selected from the group consisting of a smartphone, a desktop computer, a laptop computer, a netbook, a tablet computer, a smartwatch, an augmented reality wear, a PDA (personal digital assistants), a server, a digital camera, an e-book reader, a video game platform, a television set-top box (or a television with computing capability), a kiosk, and a combination thereof.

5. The mobile medical device according to claim 1 , wherein the mobile electronic device comprises a crowd sourcing application configured to receive data from the sensor attachment module, to transmit data to a server and to receive data from the server.

6. The mobile medical device according to claim 1 , wherein the crowd sourcing application is configured to permit a user to manually tag an event or to enter descriptive data regarding an event.

7. A respiration monitoring system (RMS) comprising:

(a) a plurality of mobile medical devices according to claim 1 , and

(b) a server for integrating data collected by the plurality of medical devices, the server comprising:

a receiver configured to receive data transmitted from the plurality of mobile medical devices, the received data being collected and processed by the strain sensors and processors of the plurality of mobile electronic devices;

an analytic engine configured to integrate data received from the plurality of mobile medical devices to create or update a map indicating respiratory condition information in connection with a plurality of users; and

a transmitter configured to transmit the map to the plurality of mobile medical devices of the plurality of users, wherein the map indicates a respiratory condition to the plurality of users of the mobile medical devices.

8. The respiration monitoring system (RMS) according to claim 7 , wherein the map is a heat map.

9. The respiration monitoring system (RMS) of claim 7 , wherein the server comprises a machine learning module to detect an adverse regional respiratory event.

10. The respiration monitoring system (RMS) of claim 7 , wherein the machine learning module is configured to detect an asthmatic event.

11. The respiration monitoring system (RMS) of claim 7 , wherein the server is configured to:

train a respiratory distress detection model using machine learning techniques and one or more sets of training data,

automatically detect an instance of suspected respiratory distress by an entity using the respiratory distress detection model, and

updating the respiratory distress detection model using at least a portion of the automatically detected instance of suspected respiratory distress by the entity and machine learning techniques for use in detecting a second instance of suspected respiratory distress.

12. The respiration monitoring system (RMS) according to claim 7 , wherein the server is further configured to analyze the data collected from the mobile electronic devices to determine a further event or a next likely scenario.

13. The respiration monitoring system (RMS) of claim 7 , further comprising a database, the server being in communication with the database to store the indication of the adverse respiratory event.

14. The respiration monitoring system (RMS) of claim 7 , wherein the respiration monitoring system is configured to provide to one or more user display devices a report indicating a geographic location of one or more adverse respiratory events.

15. A method of assessing lung function and/or of monitoring a respiratory therapy in a subject comprising:

placing the mobile medical device according to claim 1 in communication with the chest or abdomen of the subject, and

utilizing data output from the mobile medical device to assess lung function and/or to determine if the respiratory therapy is being carried out effectively.