Systems and techniques for tracking sleep consistency and sleep goals

• US 10,325,514 B2
• Filed: 06/02/2016
• Issued: 06/18/2019
• Est. Priority Date: 06/02/2016
• Status: Active Grant

• Current Assignee: Fitbit, Inc. (Alphabet Inc.)
• Original Assignee: Fitbit, Inc. (Alphabet Inc.)
• Inventors: Arnold, Jacob Antony, Cheng, Yeqing, Baiani, Yasaman, Russell, Allison Maya
• Primary Examiner(s): Yip, Jack
• Application Number:
  US15/171,049
• Publication Number:
  US 20170352287A1
• Time in Patent Office: 1,111 Days
• Field of Search: 434236
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A61B 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/4815 Sleep quality
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/7405 using sound
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/743 Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: A61B 5/7455 characterised by tactile indication, e.g. vibration or electrical stimulation
• CPC Class Codes: A61B 5/7475 User input or interface means, e.g. keyboard, pointing device, joystick
• CPC Class Codes: G06F 3/0481 based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• CPC Class Codes: G06F 3/0484 for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G09B 19/00 Teaching not covered by other main groups of this subclass teaching or practice apparatus for gun-aiming or gun-laying F41G3/26
• CPC Class Codes: G09B 5/00 Electrically-operated educational appliances working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F

Abstract

Methods, techniques, apparatuses, and systems for setting up and tracking sleep consistency goals of users are provided. In one example, a computing system for setting a sleep schedule of a user of a biometric monitoring device may obtain sleep data derived from sensor data generated by the biometric monitoring device, store the sleep data in a sleep log data store as one or more sleep logs associated with an account assigned to the user, and calculate a target bedtime based on a scheduled waketime of the user and a sleep efficiency derived, at least in part, from the sleep data for one or more users stored in the sleep log data store. The computing system may also be configured to provide a number of personalized user interfaces to an individual for the purposes of setting a sleep schedule. Such interfaces may include parameters that are tailored to the individual sleep needs and/or characteristics of the individual'"'"'s sleep.

Claims

1. A system for setting a sleep schedule of a user of a wearable biometric monitoring device, the wearable biometric monitoring device comprising one or more sensors, the system comprising:

one or more processors; and

a memory, wherein;

the one or more processors are communicatively connected with the memory, and

the memory stores instructions that, when executed, cause the one or more processors to;

obtain sleep data derived from sensor data generated by the one or more sensors in the wearable biometric monitoring device, the sensor data including data generated by at least one of the one or more sensors in the wearable biometric monitoring device responsive to movement of the user of the wearable biometric monitoring device, the sleep data including data regarding a plurality of sleep sessions and specifying various sleep states of the user for the respective sleep sessions, wherein the data generated by the at least one of the one or more sensors of the wearable biometric monitoring device is generated during each of the sleep sessions while the wearable biometric monitoring device is being worn by the user,

store the sleep data in a sleep log data store as one or more sleep logs associated with an account assigned to the user, the sleep log data store also storing other sleep logs including other sleep data derived from other sensor data generated by one or more other sensors in other wearable biometric monitoring devices of other users, the other sensor data including other data generated by at least one of the one or more other sensors in the other wearable biometric monitoring devices responsive to movements by the other users of the other wearable biometric monitoring devices, and

calculate a target bedtime based on a scheduled waketime of the user and a selected sleep duration of the user weighted by a factor based on a sleep efficiency derived, at least in part, from the sleep data for one or more users stored in the sleep log data store.

2. The system of claim 1, wherein the target bedtime is based, at least in part, on the sleep efficiency of the other users of the other wearable biometric monitoring devices.

3. The system of claim 1, wherein the target bedtime is based, at least in part, on the sleep efficiency of the user of the wearable biometric monitoring device.

4. The system of claim 1, wherein:

the memory further stores instructions that, when executed, cause the one or more processors to;

obtain sleep state duration data stored in the sleep log data store for one or more of the sleep sessions, the sleep state duration data for each of the one or more sleep sessions being representative of the total amount of time the user that is associated with that sleep session spent in one or more sleep states during that sleep session, and

determine sleep session duration data for each of the one or more sleep sessions, the sleep session duration data for each sleep session being representative of the total time of that sleep session; and

the sleep efficiency is representative, at least in part, of a correlation between the sleep state duration data for the one or more sleep sessions and the sleep session duration data for the one or more sleep sessions.

5. The system of claim 4, wherein the memory further stores instructions that, when executed, cause the one or more processors to obtain the scheduled waketime of the user from the user via a graphical user interface.

6. The system of claim 4, wherein the calculation of the target bedtime is further based, at least in part, on the sleep efficiency of the user of the wearable biometric monitoring device.

7. The system of claim 4, wherein the calculation of the target bedtime is further based on the sleep efficiency of other users of the other wearable biometric monitoring devices.

8. The system of claim 4, wherein the memory further stores instructions that, when executed, cause the one or more processors to determine a recommended bedtime based on the target bedtime, wherein the recommended bedtime is further based on one or more of:

sleep state duration data for a plurality of sleep sessions of other users of other biometric monitoring devices stored in the sleep log data store and sleep session duration data for the plurality of sleep sessions of the other users of other biometric monitoring devices stored in the sleep log data store.

9. The system of claim 1, wherein the memory further stores instructions that, when executed, cause the one or more processors to obtain the selected sleep duration of the user for the sleep session through a user-selectable input.

10. The system of claim 1, wherein:

the sleep efficiency for each sleep session is calculated, at least in part, by dividing the sleep state duration data for each sleep session by the sleep session duration data for each corresponding sleep session, respectively, and

the sleep efficiency is accounted for, at least in part, by multiplying the selected sleep duration by a factor that is based on the sleep efficiencies for the plurality of sleep sessions to determine a predicted sleep session duration.

11. The system of claim 1, wherein the memory further stores instructions that, when executed, cause the one or more processors to cause a notification mechanism to produce a notification relating to a comparison of sleep state duration data for one or more sleep sessions with the selected sleep duration data.

12. The system of claim 1, wherein the memory further stores instructions that, when executed, cause the one or more processors to determine a recommended bedtime based on the target bedtime, wherein the recommended bedtime is further based on one or more of:

one or more specific days of the week, a specific time of year, holidays, workdays of the user, non-workdays of the user, a seasonal time change, a geographic location, travel by the user between at least two time zones, exercise of the user, and a duration of daylight in a day.

13. The system of claim 1, wherein the sleep efficiency is further based on of historical sleep efficiency data associated with one or more of:

a proper subset of one or more specific days of the week, a time of year, holidays, workdays of the user, non-workdays of the user, a seasonal time change, a geographic location, travel by the user between at least two time zones, exercise of the user, and a duration of daylight in a day.

14. The system of claim 1, further comprising the wearable biometric monitoring device that includes:

the one or more sensors, wherein the sensors are configured to generate the sensor data, and

a communications interface to communicate the sensor data to the one or more processors.

15. The system of claim 1, wherein the calculation of the target bedtime is further based on one or more of:

one or more specific days of the week, a specific time of year, holidays, workdays of the user, non-workdays of the user, a seasonal time change, a geographic location, travel by the user between at least two time zones, exercise of the user, and a duration of daylight in a day.

16. The system of claim 1, wherein the memory further stores instructions that, when executed, cause the one or more processors to determine a recommended bedtime based on the target bedtime, wherein the recommended bedtime is further based on a selected sleep duration of the user for a sleep session.

17. The system of claim 1, wherein the memory further stores instructions that, when executed, cause the one or more processors to determine a recommended bedtime based on the target bedtime, wherein the recommended bedtime is the first hourly division on the hour that is closest to the target bedtime.

18. The system of claim 17, wherein the memory further stores instructions that, when executed, cause the one or more processors to determine:

an amount of time by which the user missed previous selected bedtimes, and

a recommended bedtime based on the target bedtime and taking into account the amount of time by which the user missed the previous selected bedtimes.

19. The system of claim 1, wherein the memory further stores instructions that, when executed, cause the one or more processors to determine a recommended bedtime based on the target bedtime, wherein the recommended bedtime is further based on one or more of:

sleep state duration data for a plurality of sleep sessions of the user stored in the sleep log data store and sleep session duration data for the plurality of sleep sessions of the user stored in the sleep log data store.

20. The system of claim 1, wherein the memory further stores instructions that, when executed, cause the one or more processors to cause a notification mechanism to produce a notification relating to a comparison of sleep data representative of a start of one or more sleep sessions with the target bedtime.

21. The system of claim 1, wherein the memory further stores instructions that, when executed, cause the one or more processors to cause a notification mechanism to produce a notification relating to a comparison of sleep state data representative of an end of one or more sleep sessions with the scheduled waketime.

22. A system for setting a sleep schedule of a user of a wearable biometric monitoring device, the wearable biometric monitoring device comprising one or more sensors, the system comprising:

one or more processors; and

a memory, wherein;

the one or more processors are communicatively connected with the memory, and

the memory stores instructions that, when executed, cause the one or more processors to;

obtain sleep data derived from sensor data generated by the one or more sensors in the wearable biometric monitoring device, the sensor data including data generated by at least one of the one or more sensors in the wearable biometric monitoring device responsive to movement of the user of the wearable biometric monitoring device, the sleep data including data regarding a plurality of sleep sessions and specifying various sleep states of the user for the respective sleep sessions, wherein the data generated by the at least one of the one or more sensors of the wearable biometric monitoring device is generated during each of the sleep sessions while the wearable biometric monitoring device is being worn by the user,

store the sleep data in a sleep log data store as one or more sleep logs associated with an account assigned to the user, the sleep log data store also storing other sleep logs including other sleep data derived from other sensor data generated by one or more other sensors in other wearable biometric monitoring devices of other users, the other sensor data including other data generated by at least one of the one or more other sensors in the other wearable biometric monitoring devices responsive to movements by the other users of the other wearable biometric monitoring devices, and

calculate a target bedtime using a regression model that is based on the scheduled waketime of the user and the sleep data for one or more users stored in the sleep log data store, wherein the sleep data for the one or more users stored in the sleep log data store and on which the regression model is, at least in part, based accounts for a sleep efficiency of each sleep session.

23. The system of claim 22, wherein the regression model relates the sleep session duration data for a plurality of sleep sessions stored in the sleep log data store to corresponding sleep state duration data for the plurality of sleep sessions.

24. The system of claim 23, wherein the sleep efficiency for each sleep session is representative of the sleep state duration data for that sleep session divided by the sleep session duration data for that sleep session.

25. The system of claim 23, wherein the plurality of sleep sessions are for a plurality of the other users of the other wearable biometric monitoring devices.

26. The system of claim 23, wherein the regression model is a regression model selected from the group consisting of:

a linear regression model, a nonlinear regression model, a parametric regression model, a nonparametric regression model, a semiparametric regression model, and a multivariate linear regression model.

27. The system of claim 23, wherein the sleep session duration data and corresponding sleep state duration data for the plurality of sleep sessions included in the regression model are associated with sleep sessions with waketimes that are within a first threshold amount of the scheduled waketime.

28. The system of claim 23, wherein the sleep session duration data and corresponding sleep state duration data for the plurality of sleep sessions included in the regression model are associated with sleep state duration data that are within a second threshold amount of the selected sleep duration.

29. The system of claim 23, wherein the sleep session duration data and corresponding sleep state duration data for the plurality of sleep sessions included in the regression model are associated with sleep sessions with waketimes that have occurred on the same day of the week as the day of the week on which the scheduled waketime occurs.

30. The system of claim 23, wherein the regression model accounts for one or more of:

one or more specific days of the week, a specific time of year, holidays, workdays of the user, non-workdays of the user, a seasonal time change, a geographic location, travel by the user between at least two time zones, exercise of the user, and a duration of daylight in a day.