Exercise stress estimation method for increasing success rate of exercise prescription

• US 20060277067A1
• Filed: 03/09/2006
• Published: 12/07/2006
• Est. Priority Date: 06/07/2005
• Status: Abandoned Application

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Jang, Woo-young, Kim, Kyung-ho, Kim, Hong-sig, Yun, Myung-hwan, Eoh, Hong-jun, Suh, Young-bo
• Application Number:
  US11/371,007
• Publication Number:
  US 20060277067A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: A63B 2230/00 Measuring physiological parameters of the user
• CPC Class Codes: A63B 2230/01 User's weight
• CPC Class Codes: A63B 2230/04 heartbeat characteristics, e.g. ECG, blood pressure modulations
• CPC Class Codes: A63B 2230/30 blood pressure A63B2230/04 takes precedence
• CPC Class Codes: A63B 2230/70 body fat
• CPC Class Codes: A63B 24/00 Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A method of estimating stress caused by an exercise includes receiving questionnaire data including body measurements from a user, measuring health measurement data, deducing an exercise target and an exercise prescription based on the received and measured data, and transmitting the exercise target and exercise prescription to the user. The questionnaire data is then received back from the user, including the body measurements from the user and measuring again the health measurements data.; Parameters are designed based on the received and re-received questionnaire data and the measured and re-measured health measurement data and the parameters are converted to predetermined values. A regression analysis model is designed for estimating the exercise stress using the parameters and performing regression analysis and the exercise stress estimated through the regression analysis is transmitted to the user.

Claims

1. A method of estimating stress caused by an exercise comprising:

receiving questionnaire data including body measurements from a user, measuring health measurement data, deducing an exercise target and an exercise prescription based on the received and measured data, and transmitting the exercise target and exercise prescription to the user;

receiving again the questionnaire data including the body measurements from the user and measuring again the health measurements data;

designing parameters based on the received and re-received questionnaire data and the measured and re-measured health measurement data and converting the parameters to predetermined values;

designing a regression analysis model for estimating the exercise stress using the parameters and performing regression analysis; and

transmitting the exercise stress estimated through the regression analysis to the user.

2. The method of claim 1, wherein the questionnaire data includes one or more of physical information including the body measurements, diet information, information about exercise habits and lifestyle information.

3. The method of claim 1, wherein the health measurement data includes one or more of blood pressure, body fat percentage, an electrocardiogram and blood sugar level.

4. The method of claim 1, wherein the exercise target is to increase or reduce the body measurements or health measurements.

5. The method of claim 1, wherein the exercise prescription includes one or more of exercise type, time of day to exercise, exercise duration and exercise intensity.

6. The method of claim 5, wherein the exercise type includes one or more of aerobic exercise, weight training and stretching exercise.

7. The method of claim 1, wherein the parameters are changes in health measurements, changes in body measurements, and habit information.

8. The method of claim 7, wherein the changes in health measurements include changes in one or more of blood pressure, body fat percentage, electrocardiogram and blood sugar level.

9. The method of claim 7, wherein the changes in health measurements are categorized into three levels, which are not more than 1% of target measurements, more than 1% but not more than 3% of the target measurements and more than 3% of the target measurements.

10. The method of claim 7, wherein the changes in body measurements include changes in one or more of weight, the girth of the chest, waist measurement and hip measurement.

11. The method of claim 7, wherein the changes in body measurements are evaluated using Expression (1) when the measurements are increased or evaluated using Expression (2) when the measurements are reduced, wherein:

[(target measurements)−

(measurements after exercise)]÷

[(target measurements)−

(measurements before exercise)]  

 

(1)
[(target measurements)−

(measurements before exercise)]÷

[(target measurements)−

(measurements after exercise)]  

 

(2).

12. The method of claim 7, wherein the habit information includes one or more of diet information, information about exercise habits and lifestyle information.

13. The method of claim 1, wherein the predetermined values are between 0 and 1.

14. The method of claim 1, wherein the regression analysis model is represented by Expression (3)

Exercise stress=exp[α

×

((health measurement change)×

β

+(body measurement change)×

γ

+(habit)×

δ

)]/{1+exp[α

×

((health measurement change)×

β

+(body measurement change)×

γ

y+(habit)×

δ

)]}  

 

(3)

where α

denotes an exercise prescription index, β

denotes a health measurement index, γ

denotes a body measurement index, and δ

denotes a habit index.

35. A computer readable recording medium having embodied thereon a computer program for executing the method of claim 1.

15. A method of estimating stress caused by an exercise comprising:

receiving questionnaire data from a user, measuring the user'"'"'s health measurement data, deducing an exercise target and an exercise prescription on the basis of the received and measured data, and transmitting the exercise target and exercise prescription to the user;

receiving current questionnaire data from the user or measuring the current health measurements data and evaluating the level of achievement of the exercise target in three levels;

receiving the number of days for which the user performs the prescribed exercise and evaluating the level of performance of prescribed exercise in three levels;

deducing a new exercise prescription and transmitting the exercise prescription to the user when the evaluated level of achievement of the exercise target is determined as low or the evaluated level of performance of the prescribed exercise is determined as low;

when the evaluated level of achievement of the exercise target is determined as medium, the evaluated level of performance is determined as medium or high and the user requests a new exercise prescription; and

when the evaluated level of achievement of the exercise target is determined as high, the evaluated level of performance is medium and the user requests a new exercise prescription; and

estimating the exercise stress through regression analysis when the evaluated level of achievement of the exercise target is determined as medium, the evaluated level of performance is determined as medium or high and the user does not request a new exercise prescription;

when the evaluated level of achievement of the exercise target is determined as high, the evaluated level of performance is determined as medium and the user does not request a new exercise prescription; and

when the evaluated level of achievement of the exercise target is determined as high and the evaluated level of performance is determined as high.

16. The method of claim 15, wherein the questionnaire data includes one or more of physical information including body measurements, diet information, information about exercise habits and lifestyle information.

17. The method of claim 15, wherein the health measurement data includes one or more of blood pressure, body fat percentage, an electrocardiogram and blood sugar level.

18. The method of claim 15, wherein the exercise target is to increase or reduce the body measurements or health measurements.

19. The method of claim 15, wherein the exercise prescription includes one or more of exercise type, time of day to exercise, exercise duration and exercise intensity.

20. The method of claim 15, wherein the exercise type includes one or more of aerobic exercise, weight training and stretching exercise.

21. The method of claim 15, wherein when the exercise target is to lower the measurements, the level of the achievement of the exercise target is evaluated as high when a value obtained from Expression (4) is more than 0.2;

as medium when a value obtained from Expression (4) is more than −

0.2 but not more than 0.2; and

as low when a value obtained from Expression (4) is not more than −

0.2, wherein

[(initial measurement)−

(current measurement)]÷

[(initial measurement)−

(target measurement)]  

 

(4).

22. The method of claim 15, wherein when the exercise target is to increase the measurements, the level of the achievement of the exercise target is evaluated as high when a value obtained from Expression (5) is more than 0.2;

as medium when a value obtained from Expression (5) is more than −

0.2 but not more than 0.2; and

as low when a value obtained from Expression (5) is not more than −

0.2, wherein

[(current measurement)−

(initial measurement)]÷

[(target measurement)−

(initial measurement)]  

 

(5).

23. The method of claim 15, wherein the level of performance of prescribed exercise is evaluated as high when the user performs the prescribed exercise an average of 4 days or more a week;

as medium when the user performs the prescribed exercise an average of less than 4 but not less than 3 days a week;

or as low when the user performs the prescribed exercise an average of not more than 3 days a week.

24. The method of claim 15, wherein, when the new exercise is prescribed and transmitted to the user, information about the user'"'"'s preference for prescription is additionally received together with the request for the new exercise prescription from the user.

25. The method of claim 24, wherein the information about the user'"'"'s preference for prescription includes one or more of the existing exercise type, time of day to exercise, exercise duration and exercise intensity.

26. The method of claim 15, wherein the estimating of the exercise stress comprises:

designing parameters from the received data and converting the parameters;

designing a regression analysis model for estimation of the exercise stress by using the parameters and performing regression analysis; and

transmitting the exercise stress estimated through the regression analysis to the user.

27. The method of claim 26, wherein the parameters are changes in health measurements, changes in body measurements, and habit information.

28. The method of claim 27, wherein the changes in health measurements include changes in one or more of blood pressure, body fat percentage, an electrocardiogram and blood sugar level.

29. The method of claim 27, wherein the changes in health measurements are categorized into three levels, which are not more than 1% of target measurements, more than 1% but not more than 3% of the target measurements and more than 3% of the target measurements.

30. The method of claim 27, wherein the changes in body measurements include changes in one or more of weight, the girth of the chest, waist measurement and hip measurement.

31. The method of claim 27, wherein the changes in body measurements are evaluated using Expression (1) when the measurements are increased or evaluated using Expression (2) when the measurements are reduced, wherein

[(target measurements)−

(measurements after exercise)]÷

[(target measurements)−

(measurements before exercise)]  

 

(1)
[(target measurements)−

(measurements before exercise)]÷

[(target measurements)−

(measurements after exercise)]  

 

(2).

32. The method of claim 27, wherein the habit information includes one or more of diet information, information about exercise habits and lifestyle information.

33. The method of claim 26, wherein the parameters are converted into values between 0 and 1.

34. The method of claim 26, wherein the regression analysis model is represented by Expression (3), wherein

Exercise stress=exp[α

×

((health measurement change)×

β

+(body measurement change)×

γ

+(habit)×

δ

)]/{1+exp[α

×

((health measurement change)×

β

+(body measurement change)×

γ

+(habit)×

δ

)]}  

 

(3)

where α

denotes an exercise prescription index, β

denotes a health measurement index, γ

denotes a body measurement index, and δ

denotes a habit index.

36. A computer readable recording medium having embodied thereon a computer program for executing the method of claim 15.