TECHNIQUES FOR CLASSIFYING SLEEP SESSIONS

• US 20160192218A1
• Filed: 07/14/2015
• Published: 06/30/2016
• Est. Priority Date: 07/14/2014
• Status: Abandoned Application

• Current Assignee: Under Armour Incorporated
• Original Assignee: Under Armour Incorporated
• Inventors: Peters, Christopher
• Application Number:
  US14/798,585
• Publication Number:
  US 20160192218A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/4815 Sleep quality
• CPC Class Codes: H04W 76/27 Transitions between radio resource control [RRC] states

Abstract

Techniques are provided herein for categorizing and classifying sleep session data. A server device receives sleep data from a sleep monitoring device. The sleep data comprises information that is indicative of sleep patterns of a user over a period of time. After receiving the sleep data, the server analyzes the information to determine a starting time instance and a stopping time instance to define a sleep session over the period of time. The server associates the starting time instance to a first calendar time instance and associates the stopping time instance to a second time instance. The server classifies the sleep session as belonging to a calendar day associated with the second calendar time instance.

Claims

1. A method for analyzing sleep data, the method comprising:

at a server device, receiving from a sleep monitoring device over a network sleep data, wherein the sleep data comprises information that is indicative of sleep patterns of a user over a period of time;

after receiving the sleep data, analyzing the information to determine a starting time instance and a stopping time instance to define a sleep session over the period of time;

associating the starting time instance to a first calendar time instance;

associating the stopping time instance to a second calendar time instance; and

classifying the sleep session as belonging to a calendar day associated with the second calendar time instance.

2. The method of claim 1, further comprising:

after receiving the sleep data, analyzing the information to identify one or more sleep defined events, wherein the sleep defined events include one or more of a sleep interruption event and a sleep resumption event;

when a sleep interruption event is identified, determining whether the sleep interruption event is indicative of a sleep ending event or whether the sleep interruption event is indicative of a temporary sleep pausing event; and

when a sleep resumption event is identified, determining whether the sleep resumption event is indicative of a sleep beginning event or whether the sleep resumption event is indicative of a temporary sleep initiating event.

3. The method of claim 2, further comprising:

when the sleep resumption event is determined to be indicative of the sleep beginning event;

classifying the sleep resumption event as a sleep session start event; and

associating the starting time instance with the sleep session start event; and

when the sleep interruption event is determined to be indicative of the sleep ending event;

classifying the sleep interruption event as a sleep session stop event; and

associating the stopping time instance with the sleep session stop event.

4. The method of claim 2, wherein determining whether the sleep interruption event is indicative of the sleep ending event comprises determining that the sleep interruption event is indicative of the sleep ending event when the sleep data indicates that the sleep interruption event has occurred for longer than a predetermined period of time.

5. The method of claim 2, wherein determining whether the sleep resumption event is indicative of the sleep beginning event comprises determining that the sleep resumption event is indicative of the sleep beginning event when the sleep data indicates that the sleep resumption event has occurred for longer than a predetermined period of time.

6. The method of claim 1, wherein classifying comprises classifying the sleep session as belonging to the calendar day that is associated with both the first calendar time instance and the second calendar time instance.

7. The method of claim 1, wherein classifying comprises classifying the entire sleep session as belonging to the calendar day that is associated with the second calendar time instance only even if the first calendar time instance is associated with a different calendar day.

8. One or more computer readable storage media encoded with software comprising computer executable instructions and when the software is executed operable to:

receive sleep data over a network from a sleep monitoring device, wherein the sleep data comprises information that is indicative of sleep patterns of a user over a period of time;

analyze the information to determine a starting time instance and a stopping time instance to define a sleep session over the period of time;

associate the starting time instance to a first calendar time instance;

associate the stopping time instance to a second calendar time instance; and

classify the sleep session as belonging to a calendar day associated with the second calendar time instance.

9. The computer readable storage media of claim 8, further comprising instructions operable to:

analyze the information to identify one or more sleep defined events, wherein the sleep defined events include one or more of a sleep interruption event and a sleep resumption event;

determine, when a sleep interruption event is identified, whether the sleep interruption event is indicative of a sleep ending event or whether the sleep interruption event is indicative of a temporary sleep pausing event; and

determine, when a sleep resumption event is identified, whether the sleep resumption event is indicative of a sleep beginning event or whether the sleep resumption event is indicative of a temporary sleep initiating event.

10. The computer readable medium of claim 9, further comprising instructions operable to:

classify the sleep resumption event as a sleep session start event and associate the starting time instance with the sleep session start event when the sleep resumption event is determined to be indicative of the sleep beginning event; and

classify the sleep interruption event as a sleep session stop event and associate the stopping time instance with the sleep session stop event when the sleep interruption event is determined to be indicative of the sleep ending event.

11. The computer readable medium of claim 9, wherein the instructions operable to determine whether the sleep interruption event is indicative of the sleep ending event comprise instructions operable to determine that the sleep interruption event is indicative of the sleep ending event when the sleep data indicates that the sleep interruption event has occurred for longer than a predetermined period of time.

12. The computer readable medium of claim 9, wherein the instructions operable to determine whether the sleep resumption event is indicative of the sleep beginning event comprise instructions operable to determine that the sleep resumption event is indicative of the sleep beginning event when the sleep data indicates that the sleep resumption event has occurred for longer than a predetermined period of time.

13. The computer readable medium of claim 8, wherein the instructions operable to classify the sleep session comprise instructions operable to classify the sleep session as belonging to the calendar day that is associated with both the first calendar time instance and the second calendar time instance.

14. The computer readable medium of claim 8, wherein the instructions operable to classify the sleep session comprise instructions operable to classify the entire sleep session as belonging to the calendar day that is associated with the second calendar time instance only even if the first calendar time instance is associated with a different calendar day.

15. An apparatus comprising:

a network interface unit; and

a processor unit coupled to the network interface unit and configured to;

receive via the network interface unit sleep data over a network from a sleep monitoring device, wherein the sleep data comprises information that is indicative of sleep patterns of a user over a period of time;

analyze the information to determine a starting time instance and a stopping time instance to define a sleep session over the period of time;

associate the starting time instance to a first calendar time instance;

associate the stopping time instance to a second calendar time instance; and

classify the sleep session as belonging to a calendar day associated with the second calendar time instance.

16. The apparatus of claim 15, wherein the processor is further configured to:

analyze the information to identify one or more sleep defined events, wherein the sleep defined events include one or more of a sleep interruption event and a sleep resumption event;

determine, when a sleep interruption event is identified, whether the sleep interruption event is indicative of a sleep ending event or whether the sleep interruption event is indicative of a temporary sleep pausing event; and

determine, when a sleep resumption event is identified, whether the sleep resumption event is indicative of a sleep beginning event or whether the sleep resumption event is indicative of a temporary sleep initiating event.

17. The apparatus of claim 16, wherein the processor is further configured to:

classify the sleep resumption event as a sleep session start event and associate the starting time instance with the sleep session start event when the sleep resumption event is determined to be indicative of the sleep beginning event; and

classify the sleep interruption event as a sleep session stop event and associate the stopping time instance with the sleep session stop event when the sleep interruption event is determined to be indicative of the sleep ending event.

18. The apparatus of claim 16, wherein the processor is further configured to determine that the sleep interruption event is indicative of the sleep ending event when the sleep data indicates that the sleep interruption event has occurred for longer than a predetermined period of time.

19. The apparatus of claim 16, wherein the processor is further configured to determine that the sleep resumption event is indicative of the sleep beginning event when the sleep data indicates that the sleep resumption event has occurred for longer than a predetermined period of time.

20. The apparatus of claim 15, wherein the processor is further configured to classify the sleep session as belonging to the calendar day that is associated with both the first calendar time instance and the second calendar time instance.