Methods and systems for identification of event data having combined activity and location information of portable monitoring devices

• US 8,938,368 B2
• Filed: 05/15/2014
• Issued: 01/20/2015
• Est. Priority Date: 09/30/2010
• Status: Active Grant

• Current Assignee: Fitbit, Inc. (Alphabet Inc.)
• Original Assignee: Fitbit, Inc. (Alphabet Inc.)
• Inventors: Yuen, Shelten Gee Jao, Park, James, Lee, Hans Christiansen
• Primary Examiner(s): Raymond, Edward
• Application Number:
  US14/279,011
• Publication Number:
  US 20140249774A1
• Time in Patent Office: 250 Days
• Field of Search: 702/155, 702/160, 702182-185, 702/188
• US Class Current: 702/155
• CPC Class Codes: A61B 2560/0214 of power generation or supply
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/1117 Fall detection
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/222 combined with detection or measurement of physiological parameters, e.g. heart rate
• 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 5/6838 Clamps or clips
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/743 Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: A61B 5/744 Displaying an avatar, e.g. an animated cartoon character
• CPC Class Codes: G01C 22/006 Pedometers
• CPC Class Codes: G01P 13/00 Indicating or recording presence, absence, or direction, of movement electric switches H01H; counting moving objects G06M7/00
• CPC Class Codes: G16H 20/30 : relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass

Abstract

Systems and methods for segmenting a period of time into identification of locations of a user who performs activities are described. One of the methods includes detecting activity of a monitoring device worn by the user. The activity includes an amount of movement of the monitoring device. The activity is performed for a period of time. The method further includes obtaining geo-location data for the monitoring device and storing, during the period of time, the detected activity and corresponding geo-location data. The method also includes analyzing the detected activity and the corresponding geo-location data to identify one or more events. Each event is associated with a group of activity data and one or more of the groups of activity data is associated with an identifier, which is obtained using the geo-location data.

Claims

1. A method comprising:

receiving motion data from an activity tracker, the motion data captured for a period of time;

receiving geo-location data for the activity tracker, the geo-location being captured during the period of time;

analyzing the motion data captured during the period of time, the analyzing acting to group one or more portions of the motion data, each portion having similar motion characteristics;

associating at least one portion of the motion data to a location identified from the geo-location data;

identifying a graphical identifier that is descriptive of the motion data for the at least one portion that is associated to the location; and

sending data to a device for display of the motion data and the graphical identifier.

2. The method of claim 1, wherein the motion data is associated with multiple positions of the monitoring device.

3. The method of claim 1, wherein the activity tracker includes a watch, or a wristband, or a bracelet.

4. The method of claim 1, wherein the period of time includes a portion of a day or a portion of an hour.

5. The method of claim 1, wherein the geo-location data includes a latitude, or a longitude, or an altitude, or a combination thereof.

6. The method of claim 1, wherein analyzing the motion data captured during the period of time comprises:

determining activity levels based on the motion data and the period of time; and

determining a number of activities performed by the user while wearing the activity tracker based on the activity levels.

7. The method of claim 1, wherein the one or more portions of the group include one or more activity levels identifying an activity.

8. The method of claim 1, wherein the similar motion characteristics include activity levels that identify an activity performed while wearing the activity tracker.

9. The method of claim 1, wherein associating the at least one portion of the motion data to the location identified from the geo-location data comprises determining that an activity identified based on the at least one portion of the motion data is performed at the location.

10. The method of claim 1, wherein the graphical identifier includes an icon, or a symbol, or an animation, or a video, or a multimedia.