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Determining a time period a person is in bed

  • US 10,324,109 B2
  • Filed: 09/15/2016
  • Issued: 06/18/2019
  • Est. Priority Date: 07/12/2012
  • Status: Active Grant
First Claim
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1. A computer-implemented method to determine sleep patterns, comprising:

  • measuring, by a sensor of a wireless sensor device mounted on at least one of;

    a chest, torso, or thorax of a user, a first plurality of acceleration samples and a second plurality of acceleration samples;

    storing the first and second plurality of acceleration samples in a memory of the wireless sensor device;

    implementing an application coupled to the memory storing the first and second plurality of acceleration samples and determining a first and second bed entry data and a first and second bed exit data for a first and second bed period, respectively, by;

    utilizing the first and second plurality of acceleration samples in relation to at least one axis associated with a person'"'"'s body over a first and second predetermined time window, respectively,wherein the first and second plurality of acceleration samples are calibrated by a calibration procedure of the application that enables generation of at least one derived axis of acceleration data that lines up with the at least one axis associated with the person'"'"'s body, andwherein the first and second plurality of acceleration samples measure a first and second plurality of sleep parameters, respectively,calculating a polar angle for each acceleration sample of the first and second plurality of acceleration samples within the first and second predetermined time window,calculating a fraction of an amount of time within the first and second predetermined time window that the polar angle is greater than an angle threshold that indicates that the person is lying down, wherein in response to the fraction being greater than a predetermined angle threshold, the first and second predetermined time window are marked as a period the person is lying down,triggering a start of a first and second sleep algorithm by using the first and second plurality of sleep parameters measured in the first and second plurality of acceleration samples, respectively,determining the first bed entry data and the first bed exit data for the first bed period using the first sleep algorithm,determining the second bed entry data and the second bed exit data for the second bed period using the second sleep algorithm,determining whether a period of time between the first bed exit data and the second bed entry data is greater than a maximum out-of-bed period,in response to a determination that the period of time between the first bed exit data and the second bed entry data is less than a maximum out-of-bed period;

    combining the first bed period and the second bed period to determine a time period a person is in bed,triggering an end of the first and second sleep algorithm to complete the first and second sleep algorithm by using the first and second plurality of sleep parameters measured in the first and second plurality of acceleration samples, anddetermining sleep patterns using the completed first and second sleep algorithm.

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