Methods and apparatus for monitoring patients and delivering therapeutic stimuli

• US 8,655,441 B2
• Filed: 07/13/2010
• Issued: 02/18/2014
• Est. Priority Date: 04/16/2009
• Status: Active Grant

• Current Assignee: Massachusetts Institute of Technology
• Original Assignee: Massachusetts Institute of Technology
• Inventors: Fletcher, Richard, Picard, Rosalind, Eydgahi, Hoda, Williams, Clayton
• Primary Examiner(s): Dvorak, Linda
• Assistant Examiner(s): ANTISKAY, BRIAN MICHAEL
• Application Number:
  US12/834,903
• Publication Number:
  US 20110004072A1
• Time in Patent Office: 1,316 Days
• Field of Search: 600/372, 600/382, 600/384, 600/386, 600388-393, 600508-509, 600544-547
• US Class Current: 600/545
• 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 5/015 By temperature mapping of body part
• CPC Class Codes: A61B 5/0531 Measuring skin impedance
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/6804 Garments; Clothes
• CPC Class Codes: A61B 5/6806 Gloves
• CPC Class Codes: A61B 5/6807 Footwear
• CPC Class Codes: G16H 40/67 for remote operation

Abstract

In an exemplary implementation of this invention, a user wears comfortable biosensors. These sensors gather physiological data from the user and transmit this data to another radio-enabled device. This other device may be a mobile phone. The data is further transmitted, via this other radio-enabled device, to one or more networks (such as wireless networks or the Internet). A processor analyzes this transmitted data, in real time, to recognize patterns in the data that indicate the need for therapeutic intervention. Upon recognition of such a pattern, the processor outputs instructions for a transducer to deliver therapeutic stimuli. These instructions are transmitted, over one or more wired or wireless networks, to a transducer which delivers the therapeutic stimuli to the user.

Claims

1. A system comprising, in combination:

(a) at least one wearable biosensor configured (i) for measuring physiological data and (ii) for wirelessly transmitting the data to a hub for direct or indirect transmission to a web server, which server is included in a set of one or more servers;

(b) the one or more servers;

wherein

(i) the at least one biosensor includes a wireless module,

(ii) the one or more servers are configured

(A) to use a pattern recognition algorithm to recognize patterns in the data, and

(B) based on a pattern in the data recognized by the algorithm, to output instructions for one or more transducers to produce psychologically therapeutic stimuli,

(iii) the psychologically therapeutic stimuli comprise auditory, visual or audiovisual stimuli, and

(iv) the one or more servers are further configured to perform machine learning to repeatedly update classifiers in a pattern recognition algorithm, which machine learning includes using data from the biosensor for training.

2. The system of claim 1, wherein the hub comprises a mobile computing device.

3. The system of claim 2, wherein the one or more transducers are included in the mobile computing device.

4. The system of claim 1, wherein said pattern is indicative of a drug craving or panic state.

5. The system of claim 1, wherein the psychologically therapeutic stimuli comprise a verbal message.

6. The system of claim 1, wherein the psychologically therapeutic stimuli comprise audio or visual content.

7. The system of claim 1, wherein the psychologically therapeutic stimuli comprise an SMS message or MMS message.

8. The system of claim 1, wherein the one or more transducers are wearable.

9. The system of claim 1, further comprising the one or more transducers.

10. The system of claim 1, wherein the pattern recognition algorithm comprises a Dynamic Bayesian Network.

11. The system of claim 1, wherein the hub is wearable and is configured to store the data, after wireless receiving it and before wirelessly transmitting it.

12. The system of claim 1, wherein said system is adapted to repeatedly:

accept updated physiological data,

use at least some of said updated physiological data as training data for machine learning,

based on said machine learning, revise at least one pattern recognition algorithm, and

use said revised pattern recognition algorithm in a processor housed onboard a wearable or handheld device.

13. The system of claim 1, wherein the one or more servers are further adapted for analyzing said physiological data regarding a user, accessing audio or visual content stored in machine-readable form in memory, and outputting instructions for at least one said transducer to deliver said content to said user in a form perceptible to said user.

14. The system of claim 1, wherein said system is adapted for performing said pattern recognition in real time.

15. The system of claim 1, wherein said system is further adapted for soliciting at least one live human communication and outputting instructions for said communication to be delivered to a person wearing said at least one sensor.

16. A method comprising, in combination:

(a) using a biosensor to measure physiologic data regarding a human;

(b) wirelessly transmitting the data to a mobile computing device; and

(c) using one or more processors;

(i) to recognize a pattern in the data using a pattern recognition algorithm,

(ii) to output instructions for one or more transducers to produce psychologically therapeutic stimuli, and

wherein

(1) the psychologically therapeutic stimuli comprise auditory, visual or audiovisual stimuli, and

(2) the one or more processors are configured to dynamically modify the pattern recognition algorithm with updated classifiers.

17. The method of claim 16, wherein the one or more processors that recognize a pattern (step 16(c)(i)) are onboard the mobile computing device.

18. The method of claim 16, wherein the one or more processors that recognize a pattern (step 16(c)(i)) and output instructions (step 16(c)(ii)) are onboard the mobile computing device.

19. The method of claim 16, wherein all or some of the transducers are onboard the mobile computing device.

20. The method of claim 18, wherein the method includes receiving the updated classifiers directly or indirectly from another computer, but the method does not include computing the updated classifiers.