Training device for determining timing of next training session

Abstract

A device helps a user to plan the proper timing for setting a next training session based on the intensity of a training stimulus of a previous session. The user is shown when there will have been enough recovery time since the last training session to suggest the best time for starting the next training session. The timing recommendations are based on a supercompensation time curve that depends on a user dependent factor that includes at least the training load of the last training session and preferably also depends on the training status of the user. Further parameters like age and gender and variables like behavior affecting recovery, as there are for example tiredness from lack of sleep, dehydration from insufficient drinking, insufficient calories, protein, mineral or vitamin intake, consumption of alcohol and other drugs, can additionally be used to influence the recovery timing calculations.

1 . A training device for determining a timing for a next training session, comprising:
 an input device receiving an indication of a user dependent factor, the input device being at least one of a user input device and a sensor unit that monitors an activity of the user; and   a processor that defines a time to perform the next training session, the processor uses an equation that estimates at least a portion of a supercompensation curve, the equation being a function of at least one parameter that is varied by the processor based on the user dependent factor.   
     
     
         2 . The training device of  claim 1 , further comprising a storage device that stores values of the at least one parameter for at least two different values of the user dependent factor. 
     
     
         3 . The training device of  claim 1 , wherein the user dependent factor is a training load of the previous training session based on a measurement by the sensor unit. 
     
     
         4 . The training device of  claim 3 , wherein the user dependent factor also includes a training status of the user. 
     
     
         5 . The training device of  claim 3 , wherein the user dependent factor also includes at least one of age and gender of the user. 
     
     
         6 . The training device of  claim 3 , wherein the user dependent factor also includes a further variable affecting recovery selected from the group including tiredness from a lack of sleep, dehydration from insufficient drinking, insufficient calories, protein, mineral, or vitamin intake, consumption of alcohol or drugs. 
     
     
         7 . The training device of  claim 1 , wherein the user dependent factor also includes whether the user took a walk after the last training session, a diet of the user after the last training session, and whether the user had a massage after the last training session. 
     
     
         8 . The training device of  claim 1 , wherein the input device includes the sensor unit, and the sensor unit measures at least one of acceleration impacts and vibrations correlating to physical stress. 
     
     
         9 . The training device of  claim 8 , wherein the at least one of acceleration impacts and vibrations correlate to physical stress on a runner's legs. 
     
     
         10 . The training device of  claim 8 , wherein the at least one of acceleration impacts and vibrations correlate to at least one of foot pronation and tibia rotation. 
     
     
         11 . The training device of  claim 1 , wherein the equation is a combination of an ascending sigmoid curve equation and a descending sigmoid curve equation, and defines a section of the supercompensation curve. 
     
     
         12 . The training device of  claim 1 , wherein the supercompensation curve is defined by subtracting a descending sigmoid curve from an ascending sigmoid curve. 
     
     
         13 . The training device of  claim 11 , wherein the ascending sigmoid curve is defined as gain_a*TAN H(time_a*t−time_constant_a)+offset_a and the descending sigmoid curve is defined as gain_d*TAN H(time_d*t−time_constant_d)+offset_d, wherein TAN H is a hyperbolic tangent function, gain_a, time_a, time_constant_a, offset_a, gain_d, time_d, time_constant_d, and offset_d are parameters and t is an elapsed time after the last training session. 
     
     
         14 . The training device of  claim 13 , wherein gain_a, time_a, offset_a, and time_d vary based on the user dependent factor, gain_d and offset_d are proportional to gain_a, and time_constant_a and time_constant_d are constants. 
     
     
         15 . The training device of  claim 11 , further comprising a storage device that stores values of gain_a, time_a, offset_a, and time_d for at least two different values of the user dependent factor. 
     
     
         16 . The training device of  claim 1 , further comprising an output device that outputs the timing for a next training session to the user. 
     
     
         17 . The training device of  claim 16 , wherein the timing for the next training session is output as the time associated with the highest point on the supercompensation curve. 
     
     
         18 . The training device of  claim 16 , wherein the timing for the next training session is output as the time period associated with a supercompensation section of the supercompensation curve. 
     
     
         19 . The training device of  claim 1 , further comprising a remote server with a storage device storing data for a plurality of users. 
     
     
         20 . A method for determining timing of a next training session, comprising the steps of:
 obtaining data indicating a training load of a training session of a user by at least one of sensing the data using a sensor unit or receiving the data by user input using an input/output unit;   setting, by the input/output unit, parameters of an equation that estimates at least a portion of a recovery-supercompensation curve based on the data; and   calculating and displaying the recovery-supercompensation curve to the user on a display of the input/output unit.   
     
     
         21 . The method of  claim 20 , further comprising the step of inputting a user's subjective evaluation of the training session, and said step of setting parameters uses the user's subjective evaluation. 
     
     
         22 . The method of  claim 20 , further comprising the step of determining whether the user is in a recovery phase of a previous training session. 
     
     
         23 . The method of  claim 20 , wherein the data sensed by the sensor unit is monitored during the training session to determine whether the data indicates a load level is exceeded. 
     
     
         24 . The method of  claim 20 , wherein data from a plurality of users is stored in a database, and the parameters of each of the plurality of users is updated based on the collective data. 
     
     
         25 . The method of  claim 20 , wherein the equation is a combination of an ascending sigmoid curve equation and a descending sigmoid curve equation, and defines a section of the supercompensation curve. 
     
     
         26 . The method of  claim 25 , wherein the ascending sigmoid curve is defined as gain_a*TAN H(time_a*t−time_constant_a)+offset_a and the descending sigmoid curve is defined as gain_d*TAN H(time_d*t−time_constant_d)+offset_d, wherein TAN H is a hyperbolic tangent function, gain_a, time_a, time_constant_a, offset_a, gain_d, time_d, time_constant_d, and offset_d are parameters and t is an elapsed time after the last training session.