View source for 18013738. Methods and Systems for Improving Measurement of Sleep Data by Classifying Users Based on Sleeper Type simplified abstract (Google LLC)

=Methods and Systems for Improving Measurement of Sleep Data by Classifying Users Based on Sleeper Type=


==Organization Name==

[[:Category:Google LLC|Google LLC]]


[[Category:Google LLC]]

==Inventor(s)==

[[:Category:Alexander Statan of Berkeley CA (US)|Alexander Statan of Berkeley CA (US)]][[Category:Alexander Statan of Berkeley CA (US)]]

[[:Category:Sarah Ann Stokes Kernasovskiy of San Francisco CA (US)|Sarah Ann Stokes Kernasovskiy of San Francisco CA (US)]][[Category:Sarah Ann Stokes Kernasovskiy of San Francisco CA (US)]]

[[:Category:Alexandros Antonios Pantelopoulos of El Cerrito CA (US)|Alexandros Antonios Pantelopoulos of El Cerrito CA (US)]][[Category:Alexandros Antonios Pantelopoulos of El Cerrito CA (US)]]

[[:Category:Conor Joseph Heneghan of Campbell CA (US)|Conor Joseph Heneghan of Campbell CA (US)]][[Category:Conor Joseph Heneghan of Campbell CA (US)]]

==Methods and Systems for Improving Measurement of Sleep Data by Classifying Users Based on Sleeper Type - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18013738 titled 'Methods and Systems for Improving Measurement of Sleep Data by Classifying Users Based on Sleeper Type

==Simplified Explanation==

The present disclosure is directed towards systems and methods for improving analysis of sleep data by classifying users based on sleeper type. A wearable computing system can obtain motion sensor data from a user during a first period, determine the sleeper type of the user, select a sleep analysis model based on the sleeper type, and analyze motion sensor data from a second period to determine sleep characteristics for the user.

* Wearable computing system collects motion sensor data from user during sleep
* Determines sleeper type of user based on collected data
* Selects sleep analysis model based on sleeper type
* Analyzes motion sensor data from another period to determine sleep characteristics

== Potential Applications ==
This technology can be applied in various fields such as healthcare, fitness tracking, and sleep research.

== Problems Solved ==
This technology helps in accurately analyzing sleep data and providing personalized insights based on individual sleeper types.

== Benefits ==
The benefits of this technology include improved sleep analysis, personalized sleep recommendations, and better understanding of sleep patterns.

== Potential Commercial Applications ==
Potential commercial applications of this technology include wearable devices for sleep tracking, sleep clinics, and healthcare providers offering personalized sleep solutions.

== Possible Prior Art ==
One possible prior art could be existing sleep tracking devices that analyze motion sensor data but may not classify users based on sleeper type.

=== Unanswered Questions ===

=== How does the wearable computing system determine the sleeper type of the user based on the motion sensor data collected during sleep? ===
The wearable computing system likely uses algorithms and machine learning techniques to analyze the patterns in the motion sensor data and classify the user into a specific sleeper type category.

=== What are the specific sleep characteristics that the wearable computing system can determine for the user during the second period of data collection? ===
The specific sleep characteristics could include metrics such as sleep duration, sleep quality, sleep stages, and movement patterns during sleep. The system may use the selected sleep analysis model to interpret the motion sensor data and derive these characteristics.


==Original Abstract Submitted==

The present disclosure is directed towards systems and methods for improving analysis of sleep data by classifying users based on sleeper type. In particular, a wearable computing system can obtain a first set of motion sensor data from the motion sensor for a user during a first period. The wearable computing system can determine a sleeper type from a plurality of sleeper types for the user based on the first set of motion sensor data received from the motion sensor. The wearable computing system can select a sleep analysis model from a plurality of sleep analysis models based on the sleeper type determined for the user. The wearable computing system can use the selected sleep analysis model to analyze a second set of motion sensor data from a second period to determine one or more sleep characteristics for the user during the second period.



[[Category:A61B5/00]]
[[Category:A61B5/11]]
[[Category:G01P13/00]]
[[Category:G01P15/18]]
[[Category:G06N3/084]]