Method and system for monitoring intoxication

• US 8,878,669 B2
• Filed: 01/30/2014
• Issued: 11/04/2014
• Est. Priority Date: 01/31/2013
• Status: Active Grant

• Current Assignee: KHN Solutions, LLC
• Original Assignee: KHN Solutions, Inc.
• Inventors: Nothacker, Keith Harry, Basaran, Pauline Anne, Rettus, Stacey Ilene, Strasser, Michael Jurgen, Aziz, Imraan, Walton, John Paul, Saul, Zachary Michael
• Primary Examiner(s): MULLEN, THOMAS J
• Application Number:
  US14/169,029
• Publication Number:
  US 20140210627A1
• Time in Patent Office: 278 Days
• Field of Search: 340/576, 340/439, 340/539.12, 180/272, 600/300, 600/309
• US Class Current: 340/539.12
• CPC Class Codes: A61B 2010/0009 Testing for drug or alcohol abuse
• CPC Class Codes: A61B 2010/0087 Breath samples
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/082 Evaluation by breath analysis, e.g. determination of the chemical composition of exhaled breath A61B5/083, A61B5/091 take precedence
• CPC Class Codes: A61B 5/097 Devices for facilitating collection of breath or for directing breath into or through measuring devices
• CPC Class Codes: A61B 5/18 for vehicle drivers or machine operators
• CPC Class Codes: A61B 5/4023 Evaluating sense of balance
• CPC Class Codes: A61B 5/4845 Toxicology, e.g. by detection of alcohol, drug or toxic products A61B5/4848 takes precedence
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/4863 Measuring or inducing nystagmus
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/743 Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: A61B 5/7445 Display arrangements, e.g. multiple display units
• CPC Class Codes: G01N 33/4972 Determining alcohol content for vehicle safety devices B60K28/06
• CPC Class Codes: G01N 33/98 involving alcohol, e.g. ethanol in breath
• CPC Class Codes: G08B 21/02 Alarms for ensuring the safety of persons
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass

Abstract

A method and system for monitoring a user'"'"'s intoxication including receiving a set of signals, derived from a set of samples collected from the user at a set of time points; providing a sobriety task to the user proximal to a time point of the set of time points; generating a performance dataset characterizing performance of the sobriety task by the user; receiving a supplementary dataset characterizing a demographic profile of the user and/or a physiological state of the user; determining a set of values of an intoxication metric, derived from the set of signals; generating a predicted temporal profile of the intoxication metric for the user based upon the set of values, the set of time points, and the supplementary dataset; generating an analysis of the user'"'"'s sobriety based upon the performance dataset and the predicted temporal profile; and providing a notification to the user based upon the analysis.

Claims

1. A method for monitoring intoxication of a user, comprising:

receiving a set of signals, derived from a set of samples collected from the body of the user at a set of time points, at a sample receiving module;

at a user interface, providing a sobriety task to the user proximal in time to at least one time point of the set of time points, wherein collection of the set of samples occurs within a time window, and providing the sobriety task occurs within the time window;

generating a performance dataset characterizing performance of the sobriety task by the user;

receiving a supplementary dataset characterizing at least one of a demographic profile of the user and a physiological state of the user;

at a processor coupled to the sample receiving module and the user interface, determining a set of values of an intoxication metric, derived from the set of signals;

at the processor, generating a predicted temporal profile of the intoxication metric for the user based upon the set of values, the set of time points, and the supplementary dataset;

at the processor, generating an analysis of the user'"'"'s sobriety based upon the performance dataset and the predicted temporal profile;

at the user interface, providing a notification to the user based upon the analysis, thus facilitating monitoring of the user'"'"'s intoxication.

2. The method of claim 1, wherein providing the sobriety task to the user comprises providing the sobriety task to the user proximal to every time point of the set of time points.

3. The method of claim 1, wherein providing the sobriety task includes providing a set of tasks at a mobile device of the user comprising the user interface, wherein the set of tasks includes at least one of a one-leg stand task, a walk and turn task, and a horizontal gaze nystagmus task.

4. The method of claim 3, wherein providing the set of tasks includes detecting performance of a task of the set of tasks by the user at an image sensor and an accelerometer of the mobile device.

5. The method of claim 1, wherein receiving the supplementary dataset includes receiving supplementary data at an aggregation module configured to access and aggregate digital content from digital social network accounts of the user, such that the supplementary dataset comprises at least one of demographic information, location information, and activity information.

6. The method of claim 1, wherein receiving the supplementary dataset includes receiving supplementary data from at least one of an object recognition module and a text recognition module configured to extract supplementary data from at least one of image content and text content indicative of consumables intended to be consumed by the user.

7. The method of claim 1, wherein determining the set of values of the intoxication metric includes determining a first BAC value of the user corresponding to a first breath sample of the set of samples received at a time point of the set of time points.

8. The method of claim 7, wherein generating the predicted temporal profile includes generating the predicted temporal profile of BAC vs. time for the user, based upon the first BAC value of the user, the time point of the set of time points, a predicted alcohol absorption profile, and a predicted alcohol elimination rate.

9. The method of claim 8, wherein the predicted alcohol elimination rate is determined using a second BAC value of the user corresponding to a second breath sample of the set of samples received at a second time point of the set of time points, such that the predicted alcohol elimination rate is determined from a slope between the first BAC value and the second BAC value.

10. The method of claim 8, wherein the predicted alcohol absorption profile and the predicted alcohol elimination rate are generated based upon at least five of:

a gender, an age, a fat-to-muscle ratio, a height, a weight, a family history, and a medical condition of the user from the supplementary dataset.

11. The method of claim 8, wherein the predicted alcohol absorption profile and the predicted alcohol elimination rate are generated based upon food consumption data extracted from images of food intended to be consumed by the user.

12. The method of claim 1, further comprising generating a prediction of a value of the intoxication metric for the user, based upon the performance dataset and a historical dataset including at least one previously generated performance dataset and at least one previously generated predicted temporal profile for the user.

13. The method of claim 1, wherein providing a notification to the user based upon the analysis includes providing a notification that the user is currently below a legal limit of intoxication, as determined from at least one of the set of values of the intoxication metric, but has performed the sobriety task unsatisfactorily.

14. The method of claim 1, further comprising generating a predicted BAC value at a future time point outside of the set of time points, based upon the predicted temporal profile, wherein providing the notification to the user based upon the analysis includes providing the notification informing the user of the predicted BAC value at the future time point.

15. The method of claim 1, further enabling the user to provide an input, at the user interface, indicative of an estimated intoxication state of the user, generating a comparison between the estimated intoxication state of the user and an actual intoxication state of the user captured in at least one of the set of values of the intoxication metric, and providing the notification to the user, wherein the notification is configured to inform the user of a comparison between the estimated intoxication state and the actual intoxication state of the user.

16. A system for monitoring intoxication of a user, comprising:

a body defining a cavity configured to accept a set of breath samples from of the user'"'"'s body at a set of time points;

a sample processing module disposed within the body and coupled to the body, the sample processing module configured to analyze the set of breath samples;

a data link coupled to the sample processing module and configured to communicate a set of signals derived from the set of breath samples; and

a processor including;

a first module configured to receive the set of signals, a supplementary dataset characterizing at least one of a demographic profile of the user and a physiological state of the user, and a performance dataset characterizing the user'"'"'s performance of a sobriety task in association with at least one of the set of breath samples accepted at the cavity,

a second module configured to determine a set of values of an intoxication metric, derived from the set of signals,

a third module configured to generate a predicted temporal profile of the intoxication metric for the user based upon the set of values, the set of time points, and the supplementary dataset,

a fourth module configured to generate an analysis of the user'"'"'s sobriety as derived from the performance dataset, and

a fifth module configured to generate a notification based upon the predicted temporal profile and the analysis, thus facilitating monitoring of the user'"'"'s intoxication.

17. The system of claim 16, wherein the cavity includes a first aperture and a second aperture configured to facilitate inflow and outflow of a breath sample of the user, a disposable mouthpiece coupleable to at least one of the first aperture and the second aperture, and a set of LEDs proximal to the cavity, and wherein light provided by the set of LEDs is visible through the cavity and configured to indicate an instruction to initiate provision of the breath sample and to indicate processing of the breath sample of the user.

18. The system of claim 16, wherein the sample processing module includes a pump configured to drive a breath sample of the set of breath samples toward a sensor configured to generate an electrical response upon detection of alcohol carried in the breath sample.

19. The system of claim 18, wherein the sensor is a fuel cell sensor configured to produce an electrical current in response to oxidation of alcohol carried in the breath sample.

20. The system of claim 16 further including a supplementary sensing module configured to communicate with the processor, wherein the supplementary sensing module includes at least one of an image sensor, a GPS, a biometric sensor, and an aggregation module configured to access digital content of the user from at least one digital social network account of the user, such that a supplementary dataset provided by the supplementary sensing module comprises at least one of demographic information, location information, and activity information.