US 9,808,185 B2
Movement measure generation in a wearable electronic device
Jacob Antony Arnold, Fremont, CA (US); and Subramaniam Venkatraman, Walnut Creek, CA (US)
Assigned to Fitbit, Inc., San Francisco, CA (US)
Filed by Fitbit, Inc., San Francisco, CA (US)
Filed on Sep. 18, 2015, as Appl. No. 14/859,192.
Claims priority of provisional application 62/068,622, filed on Oct. 24, 2014.
Claims priority of provisional application 62/067,914, filed on Oct. 23, 2014.
Claims priority of provisional application 62/063,941, filed on Oct. 14, 2014.
Claims priority of provisional application 62/054,380, filed on Sep. 23, 2014.
Prior Publication US 2016/0007934 A1, Jan. 14, 2016
Int. Cl. A61B 5/11 (2006.01); A61B 5/00 (2006.01); A61B 5/0205 (2006.01); A61B 5/024 (2006.01); A61B 5/053 (2006.01); A61B 5/08 (2006.01)
CPC A61B 5/1123 (2013.01) [A61B 5/0205 (2013.01); A61B 5/02055 (2013.01); A61B 5/1118 (2013.01); A61B 5/4809 (2013.01); A61B 5/4812 (2013.01); A61B 5/681 (2013.01); A61B 5/6801 (2013.01); A61B 5/7246 (2013.01); A61B 5/7278 (2013.01); A61B 5/743 (2013.01); A61B 5/02405 (2013.01); A61B 5/02416 (2013.01); A61B 5/0533 (2013.01); A61B 5/0816 (2013.01); A61B 5/1121 (2013.01); A61B 5/1122 (2013.01); A61B 5/7264 (2013.01); A61B 2560/0209 (2013.01); A61B 2560/0242 (2013.01); A61B 2562/0219 (2013.01); A61B 2562/04 (2013.01)] 24 Claims
OG exemplary drawing
 
1. A wearable electronic device to be worn by a user, the wearable electronic device comprising:
a set of one or more motion sensors to generate motion data samples that represent motion of the wearable electronic device, the motion data samples include a plurality of first motion data samples generated during a first time interval and a plurality of second motion data samples generated during a second time interval;
a set of one or more processors coupled to the set of motion sensors; and
a non-transitory machine readable storage medium coupled to the set of one or more processors and having stored therein instructions, which when executed by the set of one or more processors, cause the set of one or more processors to:
obtain the motion data samples generated by the set of motion sensors,
generate a first movement measure based on a combination of the first motion data samples into a single numerical value representative of the motion of the wearable electronic device during the first time interval,
generate a second movement measure based on a combination of the second motion data samples into a single numerical value representative of the motion of the wearable electronic device during the second time interval,
cause the non-transitory machine readable storage medium to store the first movement measure and the second movement measure as time series data,
derive a first feature value corresponding to the first time interval based on the single numerical values of the first and second movement measures,
derive a second feature value corresponding to the second time interval based on at least the single numerical value of the second movement measure,
assign a first sleep state to the user of the wearable electronic device for the first time interval based on the first feature value, and
assign a second sleep state to the user of the wearable electronic device for the second time interval based on the second feature value.
 
14. A method executed by a wearable electronic device, the method comprising:
obtaining a plurality of first motion data samples generated by a set of one or more motion sensors, the first motion data samples being generated during a first time interval;
generating a first movement measure based on a combination of the first motion data samples into a single numerical value representative of the motion of the wearable electronic device during the first time interval;
obtaining a plurality of second motion data samples generated by the set of motion sensors, the second motion data samples being generated during a second time interval;
generating a second movement measure based on a combination of the second the motion data samples into a single numerical value representative of the motion of the wearable electronic device during the second time interval;
causing a non-transitory machine readable storage medium to store the first movement measure and the second movement measure as time series data,
deriving a first feature value corresponding to the first time interval based on the single numerical values of the first and second movement measures;
deriving a second feature value corresponding to the second time interval based on at least the single numerical value of the second movement measure;
assigning a first sleep state to a user of the wearable electronic device for the first time interval based on the first feature value, and
assigning a second sleep state to the user of the wearable electronic device for the second time interval based on the second feature value.
 
21. A computer readable storage device that includes instructions that, when executed by one or more processors, cause the one or more processors to:
obtain a first movement measure comprising a first single numerical value representative of motion of a wearable device during a first time interval, the first single numerical value being generated based on a combination of a plurality of first motion data samples generated by a set of one or more motion sensors of the wearable electronic device;
obtain a second movement measure comprising a second single numerical value representative of the motion of the wearable device during a second time interval, the second single numerical value being generated based on a combination of a plurality of second motion data samples generated by the motion sensors;
derive a first feature value corresponding to the first time interval based on the single numerical values of the first and second movement measures,
derive a second feature value corresponding to the second time interval based on at least the single numerical value of the second movement measure,
classify a first level of activity of the user for the first time interval based on the first feature value;
classify a second level of activity of the user for the second time interval based on the second feature value;
determine a sleep state for the first time interval based on the first level of activity; and
determine a sleep state for the second time interval based on the second level of activity.