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Automated health data acquisition, processing and communication system and method

  • US 10,424,404 B2
  • Filed: 11/23/2016
  • Issued: 09/24/2019
  • Est. Priority Date: 11/13/2013
  • Status: Active Grant
First Claim
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1. A system to classify user activity, the system comprising:

  • a computing device configured with at least one processor, non-transitory processor readable media, and instructions stored on the non-transitory processor readable media;

    a first sensing subsystem for passively tracking a physiological and/or metabolic condition of a person, the first sensing subsystem comprising a biosensor that is in contact with or within a person'"'"'s body and that periodically transmits sensed information associated with the person'"'"'s physiological and/or metabolic condition; and

    a second sensing subsystem for passively tracking a location of the person, the second sensing subsystem comprising a Global Positioning System (“

    GPS”

    ) receiver that receives GPS information that is usable to determine a location of the person;

    wherein the computing device is configured to execute at least some of the instructions that cause the computing device to;

    receive and/or access the sensed information and information representing the determined location,classify and predict user activity and/or user health as a function of one or more machine learning techniques;

    detect a change from a respective baseline condition associated with at least some of the sensed information, and i) at least some information stored in a user profile associated with the person and ii) external information;

    in response to detecting the change, the computing device is further configured to;

    define a first activity unit having a first start time that corresponds to detection of the user being engaged in an activity;

    monitor the sensed information, the external information or both;

    establish a first end time of the first activity unit using the monitored information;

    automatically ascribe a classification of the first activity unit;

    output the classification of the first activity unit to a display;

    store the classification of the first activity unit in the database;

    provide a user interface that includes selectable options associated with the first activity unit;

    revise, in response to at least a received selection of at least one of the selectable options, the classification by;

    joining or merging the first activity unit and a second activity unit having a second start time and a second end time, such that the revised classification has a start time equal to the first start time and an end time equal to the second end time;

    ordividing the first activity unit into at least two activity units, each of at least two activity units having a different respective start time and a different respective end time;

    output the revised classification to a display of a computing device;

    provide the revised classification to the classification engine; and

    use the revised classification for machine learning.

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