System and method for computing athletic performance

• US 8,533,001 B2
• Filed: 03/27/2008
• Issued: 09/10/2013
• Est. Priority Date: 03/28/2007
• Status: Expired due to Fees

• Current Assignee: Nike, Inc.
• Original Assignee: PhysFarm Training Systems, LLC
• Inventors: Skiba, Philip
• Primary Examiner(s): Brooks, Matthew L.
• Application Number:
  US12/079,949
• Publication Number:
  US 20080261776A1
• Time in Patent Office: 1,993 Days
• Field of Search: 705/1.1
• US Class Current: 705/1.1
• CPC Class Codes: A63B 2024/0009 Computerised real time comparison with previous movements or motion sequences of the user
• CPC Class Codes: A63B 2024/0068 Comparison to target or threshold, previous performance or not real time comparison to other individuals
• CPC Class Codes: A63B 2024/0078 Exercise efforts programmed as a function of time
• CPC Class Codes: A63B 2024/0093 the load of the exercise apparatus being controlled by performance parameters, e.g. distance or speed
• CPC Class Codes: A63B 2220/12 Absolute positions, e.g. by using GPS
• CPC Class Codes: A63B 2220/30 Speed
• CPC Class Codes: A63B 2225/20 with means for remote communication, e.g. internet or the like
• CPC Class Codes: A63B 2230/06 heartbeat rate only
• CPC Class Codes: A63B 24/0006 Computerised comparison for qualitative assessment of motion sequences or the course of a movement
• CPC Class Codes: A63B 24/0062 Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
• CPC Class Codes: A63B 24/0075 Means for generating exercise programs or schemes, e.g. computerized virtual trainer, e.g. using expert databases
• CPC Class Codes: A63B 24/0087 Electric or electronic controls for exercising apparatus of groups A63B21/00 - A63B23/00, e.g. controlling load
• CPC Class Codes: A63B 69/0028 for running, jogging or speed-walking movable endless bands A63B22/02

Abstract

A system and method of calculating athlete performance, may include receiving information relating to at least one date of performance of physical activity and generating a proposed training schedule, including one or more training sessions, corresponding to the at least one date of performance of physical activity. Further, the system and method may include receiving information relating to records of the athlete'"'"'s prior performances, and determining a performance model including predicted athlete performance based on the calculated training schedule and the prior performances.

Claims

1. A computer-implemented method of calculating athlete performance, comprising:

receiving at the processor information relating to records of the athlete'"'"'s prior training sessions and prior performances;

calculating, using the processor, training stresses associated with each of the prior training sessions, wherein the training stress accounts for a duration and intensity of the training session;

calculating, using the processor, a positive training effect and a negative training effect associated with each of the prior training sessions according to the equations;

t

Positive training effect=∫

(k₁·

w(u)·

e<sup>−

(t·

u)/τ

1</sup>)

0

t

Negative training effect=∫

(k₂·

w(u)·

e<sup>−

(t·

u)/τ

2</sup>)

0

wherein k₁ and k₂ are constants, τ

₁ and τ

₂ are exponential decay constants, (t-u) is a time between training sessions, and w(u) is the training stress for that prior training session;

deriving a past performance value for each prior training session, wherein the past performance value equals the positive training effect minus the negative training effect;

calculating, using the processor, a predicted positive training effect and a predicted negative training effect for at least one time in the future;

deriving a predicted performance value for the at least one time in the future by subtracting the predicted negative training effect from the predicted positive training effect;

converting the predicted performance value into a percentile (PPP) by computing;

PPP=(((PP+((|MinPP|)+(SCALEFACTOR)/(|MinPP|+(SCALEFACTOR)+(MaxPP)))

wherein PP is the predicted performance value for the at least one time in the future, |MinPP| is the absolute value of a lowest predicted performance value, SCALEFACTOR is a constant, and MaxPP is a highest predicted performance value.

2. The method of computing physical performance according to claim 1, wherein a brute force method or other optimization routine is used to determine the constants.

6. The method of claim 1, further comprising:

receiving at the processor information related to at least one test athlete performance;

converting the information related to the at least one test athlete performance into a percentile (TPP) using the equation;

TPP =TP/MaxTP

wherein TP is the test performance and MaxTP is a highest prior performance; and

modifying PPP by;

adjusting k₁, k₂, τ

₁, and τ

₂ until a sum of squares of a difference between PPP and TPP is minimized; and

iteratively varying SCALEFACTOR until a lowest possible sum of squares is achieved.

3. A system for predicting athlete performance, comprising:

an interface for inputting information relating to records of the athlete'"'"'s prior training sessions and prior performances;

a processor; and

a memory storing the information relating to the records of the athlete'"'"'s prior training sessions and prior performances and instructions executable by the processor for computing a predicted athlete performance based on the information relating to the prior training data, the instructions comprising;

calculating training stresses associated with each of the prior training sessions, wherein the training stress accounts for a duration and intensity of the training session,

calculating a positive training effect and a negative training effect associated with each of the prior training sessions according to the equations;

t

Positive training effect=∫

(k₁·

w(u)·

e<sup>−

(t·

u)/τ

1</sup>)

0

t

Negative training effect=∫

(k₂·

w(u)·

e<sup>−

(t·

u)/τ

2</sup>)

0

wherein k₁ and k₂ are constants, τ

₁ and τ

₂ are exponential decay constants, (t-u) is a time between training sessions, and w(u) is the training stress for that prior training session;

deriving a past performance value for each prior training session, wherein the past performance value equals the positive training effect minus the negative training effect;

calculating, using the processor, a predicted positive training effect and a predicted negative training effect for at least one time in the future;

deriving a predicted performance value for the at least one time in the future by subtracting the predicted negative training effect from the predicted positive training effect;

converting the predicted performance value into a percentile (PPP) by computing;

PPP=(((PP+((|MinPP|)+(SCALEFACTOR)/(|MinPP|+(SCALEFACTOR)+(MaxPP)))

wherein PP is the predicted performance value for the at least one time in the future, |MinPP| is the absolute value of a lowest predicted performance value, SCALEFACTOR is a constant, and MaxPP is a highest predicted performance value;

receiving at the processor information related to at least one test athlete performance.

4. The system for predicting athlete performance according to claim 3, further comprising a display for outputting the predicted performance percentile.

5. The system for predicting athlete performance according to claim 3, wherein the interface for inputting information comprises a power meter electrically coupled to the processor.