US 12,444,500 B2
Adaptive athletic activity prescription systems
Daniel Sanders, Portland, OR (US); Brett S. Kirby, Portland, OR (US); David Clark, Beaverton, OR (US); Clifton W. Bynum, Portland, OR (US); Bradley W. Wilkins, Aloha, OR (US); and Philip F. Skiba, Highland Park, IL (US)
Assigned to NIKE, Inc., Beaverton, OR (US)
Filed by NIKE, Inc., Beaverton, OR (US)
Filed on May 22, 2023, as Appl. No. 18/200,491.
Application 18/200,491 is a continuation of application No. 17/507,370, filed on Oct. 21, 2021, granted, now 11,699,523.
Application 17/507,370 is a continuation of application No. 15/459,626, filed on Mar. 15, 2017, granted, now 11,177,037, issued on Nov. 16, 2021.
Claims priority of provisional application 62/368,559, filed on Jul. 29, 2016.
Claims priority of provisional application 62/353,394, filed on Jun. 22, 2016.
Claims priority of provisional application 62/308,766, filed on Mar. 15, 2016.
Claims priority of provisional application 62/308,749, filed on Mar. 15, 2016.
Prior Publication US 2023/0298748 A1, Sep. 21, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G16H 40/67 (2018.01); A41D 1/00 (2018.01); A43B 3/34 (2022.01); A63B 24/00 (2006.01); A63B 71/06 (2006.01); G06N 3/04 (2023.01); G06N 3/047 (2023.01); G06N 3/08 (2023.01); G16H 20/30 (2018.01); G06N 20/00 (2019.01)
CPC G16H 40/67 (2018.01) [A41D 1/002 (2013.01); A43B 3/34 (2022.01); A63B 24/0003 (2013.01); A63B 24/0062 (2013.01); A63B 24/0075 (2013.01); A63B 71/0622 (2013.01); G06N 3/04 (2013.01); G06N 3/047 (2023.01); G06N 3/08 (2013.01); G16H 20/30 (2018.01); A63B 2071/0625 (2013.01); A63B 2071/0655 (2013.01); A63B 2220/12 (2013.01); A63B 2220/34 (2013.01); A63B 2220/40 (2013.01); A63B 2220/50 (2013.01); A63B 2220/51 (2013.01); A63B 2220/72 (2013.01); A63B 2220/803 (2013.01); A63B 2220/836 (2013.01); A63B 2225/50 (2013.01); A63B 2230/06 (2013.01); A63B 2230/50 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. An apparatus, comprising:
a processor;
a user interface;
a sensor configured to capture data indicative of motion of a user; and
a non-transitory computer-readable medium comprising computer-executable instructions that when executed by the processor are configured to perform at least:
transmitting, using the user interface, a request for the user to complete a benchmark running test;
receiving motion data from the sensor as the user performs the benchmark running test at an identified intensity for an identified distance or an identified duration;
classifying, based upon at least one of: the received motion data or biographic data of the user, the user into one of a plurality of running experience classifications;
calculating with the processor, and using the received motion data, a critical velocity and a finite work capacity of the user;
calculating and updating, with the processor, an adaptive running activity prescription for the user, wherein the adaptive running activity prescription is calculated based upon the critical velocity and the finite work capacity, and goal data from the user, wherein the adaptive running activity prescription for the user includes a daily training impulse (totalDayTRIMP) calculated using sensor data received from a heart rate sensor, an activity duration (activityDuration), an average heart rate (avgHeartRate), a resting heart rate (restHeartRate), and a maximum heart rate (maxHeartRate);
based on receiving subsequent motion data from the sensor, adapting and outputting, to the user interface, the calculated adaptive running activity prescription for the user while performing a current run, wherein adapting the calculated adaptive running activity prescription to the user includes providing and displaying adaptive running activity information specific to the current run;
based the calculated daily training impulse, identifying a plurality of activity options calculated to fulfill a daily training impulse goal;
based on user classification data and the calculated daily training impulse, determining a likelihood that the user will complete a respective activity of the plurality of activity options and ranking the plurality of activity options in order of likelihood that the user will complete a respective activity; and
outputting, to the user interface, the plurality of activity options ranked in order of the likelihood that the user will complete a respective activity.