System and method for assessment of health risks and visualization of weight loss and muscle gain

• US 8,891,839 B2
• Filed: 01/09/2012
• Issued: 11/18/2014
• Est. Priority Date: 04/27/2000
• Status: Expired due to Term

• Current Assignee: Daxko LLC
• Original Assignee: Inter-Images Partners, LP
• Inventors: Bravomalo, Mario J., Brucks, Russ Edward
• Primary Examiner(s): Bella, Matthew
• Assistant Examiner(s): HEIDEMANN, JASON E
• Application Number:
  US13/346,361
• Publication Number:
  US 20120108914A1
• Time in Patent Office: 1,044 Days
• Field of Search: None
• US Class Current: 382/128
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06T 11/00 2D [Two Dimensional] image generation
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/60 relating to nutrition control, e.g. diets
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

The present system combines image morphing technology, exercise programming, supplement sales, and motivational techniques into one product. Users begin by entering their current measurements, measurement goals and current picture into the system, preferably via a Web site. The picture is segmented into body components, and each affected segment is morphed based upon the measurements, goals, and the segment'"'"'s responsiveness to weight loss in order to create a modified image representative of the user in a post-regimen condition. This system helps health and fitness businesses obtain new members and retain existing members by showing the members how they will look after following a specific regimen of diet and/or exercise. The system also predicts health risks of diabetes, heart disease, and stroke associated with the user'"'"'s pre-regimen and post-regimen conditions.

Claims

1. A computer-implemented method of creating indications of health risk and personal appearance for a person, comprising the following being performed by a computer:

receiving a data set associated with said person in a pre-regimen condition, said data set comprising;

a weight,

a height,

an age,

a gender designation,

a designation regarding family history of diabetes, and

a designation regarding the person'"'"'s level of exercise;

calculating a health risk of diabetes for said person in said pre-regimen condition based on said data set;

receiving or creating a first image representative of said person in said pre-regimen condition;

receiving a second data set associated with said person in a post-regimen condition, said second data set comprising a selected compliance with respect to a regimen of diet, exercise, or diet and exercise;

calculating a predicted health risk of diabetes for said person in said post-regimen condition based on said second data set;

creating a second image predictive of said person in said post-regimen condition based on said second data set; and

generating a screen display suitable for displaying on a computer screen, said screen display comprising said first image, a first indication of said health risk of diabetes for said person in said pre-regimen condition associated with said first image, said second image, and a second indication of said predicted health risk of diabetes for said person in said post-regimen condition associated with said second image;

said method further comprising;

calculating the person'"'"'s body mass index (BMI) according to the equation

BMI=(Weight×

704.5)/Height×

Height;

calculating the person'"'"'s BMI factor according to the equation

BMI Factor=(BMI−

25)×

7.5;

calculating the person'"'"'s acceptable body fat according to the equation

Acceptable Body Fat=(Age×

0.0667−

1.3333)+14 (if said person is male);

Acceptable Body Fat=(Age×

0.0667−

1.3333)+17 (if said person is female);

calculating the person'"'"'s excess body fat factor according to the equation

Excess Body Fat Factor=% Body Fat−

Acceptable Body Fat;

calculating the person'"'"'s obesity factor according to the equation

Obesity Factor=(BMI Factor+Excess Body Fat Factor)/2;

scaling said obesity factor according to the equation

Body Fat Scaler=[4×

(% Body Fat)−

28]×

(Obesity Factor);

magnifying said Body Fat Scaler according to the equation

Magnifier=1.25×

Body Fat Scaler;

assigning a family history value, said family history value being equal to zero if said person has no family history of diabetes, said family history value being equal to 15 if said person has a family history of diabetes;

assigning an exercise value, said exercise value being equal to zero if said person is exercising, said exercise value being equal to 25 if said person is not exercising;

assigning an age value according to the equation

Age value=(Age/5)−

7 (if (Age/5)−

7 is greater than or equal to zero)

Age value=0 (if (Age/5)−

7 is less than zero);

calculating said risk of diabetes according to the equation

Diabetes Risk=Family History Value+Exercise Value+Age Value+Magnifier.

2. A computer-implemented method of creating indications of health risk and personal appearance for a person, comprising the following being performed by a computer:

receiving a data set associated with said person in a pre-regimen condition, said data set comprising;

a weight,

a height,

an age,

a gender designation,

a designation regarding family history of heart disease,

a designation regarding family history of diabetes,

a designation regarding the person'"'"'s level of exercise,

a designation regarding the person'"'"'s blood pressure,

a designation regarding the person'"'"'s smoking activity, and

a designation regarding the person'"'"'s cholesterol level;

calculating a health risk of heart disease for said person in said pre-regimen condition based on said data set;

receiving or creating a first image representative of said person in said pre-regimen condition;

receiving a second data set associated with said person in a post-regimen condition, said second data set comprising a selected compliance with respect to a regimen of diet, exercise, or diet and exercise;

calculating a predicted health risk of heart disease for said person in said post-regimen condition based on said second data set;

creating a second image predictive of said person in said post-regimen condition based on said second data set; and

generating a screen display suitable for displaying on a computer screen, said screen display comprising said first image, a first indication of said health risk of heart disease for said person in said pre-regimen condition associated with said first image, said second image, and a second indication of said predicted health risk of heart disease for said person in said post-regimen condition associated with said second image;

said method further comprising;

calculating the person'"'"'s body mass index (BMI) according to the equation

BMI=(Weight×

704.5)/Height×

Height;

calculating the person'"'"'s BMI factor according to the equation

BMI Factor=(BMI−

25)×

7.5;

calculating the person'"'"'s acceptable body fat according to the equation

Acceptable Body Fat=(Age×

0.0667−

1.3333)+14 (if said person is male);

Acceptable Body Fat=(Age×

0.0667−

1.3333)+17 (if said person is female);

calculating the person'"'"'s excess body fat factor according to the equation

Excess Body Fat Factor=% Body Fat−

Acceptable Body Fat;

calculating the person'"'"'s obesity factor according to the equation

Obesity Factor=(BMI Factor+Excess Body Fat Factor)/2;

scaling said obesity factor according to the equation

Body Fat Scaler=[4×

(% Body Fat)−

28]×

(Obesity Factor);

assigning an age value according to the equation

Age Value=Age−

59 (if (Age−

59) is greater than or equal to zero)

Age Value=0 (if (Age−

59) is less than zero);

assigning a heart disease family history value, said heart disease family history value being equal to zero if said person has no family history of heart disease, said heart disease family history value being equal to 15 if said person has a family history of heart disease;

assigning a diabetes family history value, said diabetes family history value being equal to zero if said person has no family history of diabetes, said diabetes family history value being equal to 5 if said person has a family history of diabetes;

assigning a smoking value, said smoking value being equal to zero if said person is not smoking, said smoking value being equal to 25 if said person is smoking;

assigning a gender value, said gender value being equal to zero if said person is female, said gender value being equal to 5 if said person is male;

assigning a blood pressure value, said blood pressure value being equal to zero if said person does not have high blood pressure, said blood pressure value being equal to 15 if said person has high blood pressure;

assigning an exercise value, said exercise value being equal to zero if said person is exercising, said exercise value being equal to 10 if said person is not exercising;

assigning a cholesterol value, said cholesterol value being equal to zero if said person does not have high cholesterol, said cholesterol value being equal to 10 if said person has high cholesterol;

calculating said risk of heart disease according to the equation

3. A computer-implemented method of creating indications of health risk and personal appearance for a person, comprising the following being performed by a computer:

receiving a data set associated with said person in a pre-regimen condition, said data set comprising;

a weight,

a height,

anan age,

a gender designation,

a designation regarding family history of stroke,

a designation regarding family history of diabetes,

a designation regarding the person'"'"'s smoking activity,

a designation regarding the person'"'"'s level of exercise,

a designation regarding the person'"'"'s blood pressure, and

a designation regarding the person'"'"'s cholesterol level;

calculating a health risk of stroke for said person in said pre-regimen condition based on said data set;

receiving or creating a first image representative of said person in said pre-regimen condition;

receiving a second data set associated with said person in a post-regimen condition, said second data set comprising a selected compliance with respect to a regimen of diet, exercise, or diet and exercise;

calculating a predicted health risk of stroke for said person in said post-regimen condition based on said second data set;

creating a second image predictive of said person in said post-regimen condition based on said second data set; and

generating a screen display suitable for displaying on a computer screen, said screen display comprising said first image, a first indication of said health risk of stroke for said person in said pre-regimen condition associated with said first image, said second image, and a second indication of said predicted health risk of stroke for said person in said post-regimen condition associated with said second image;

said method further comprising;

calculating the person'"'"'s body mass index (BMI) according to the equation

BMI=(Weight×

704.5)/Height×

Height;

calculating the person'"'"'s BMI factor according to the equation

BMI Factor=(BMI−

25)×

7.5;

calculating the person'"'"'s acceptable body fat according to the equation

Acceptable Body Fat=(Age×

0.0667−

1.3333)+14 (if said person is male);

Acceptable Body Fat=(Age×

0.0667−

1.3333)+17 (if said person is female);

calculating the person'"'"'s excess body fat factor according to the equation

Excess Body Fat Factor=% Body Fat−

Acceptable Body Fat;

calculating the person'"'"'s obesity factor according to the equation

Obesity Factor=(BMI Factor+Excess Body Fat Factor)/2;

scaling said obesity factor according to the equation

Body Fat Scaler=[4×

(% Body Fat)−

28]×

(Obesity Factor);

assigning an age value according to the equation

Age Value=Age−

59 (if (Age−

59) is greater than or equal to zero)

Age Value=0 (if (Age−

59) is less than zero);

assigning a stroke family history value, said stroke family history value being equal to zero if said person has no family history of stroke, said stroke family history value being equal to 15 if said person has a family history of stroke;

assigning a diabetes family history value, said diabetes family history value being equal to zero if said person has no family history of diabetes, said diabetes family history value being equal to 5 if said person has a family history of diabetes;

assigning a smoking value, said smoking value being equal to zero if said person is not smoking, said smoking value being equal to 15 if said person is smoking;

assigning a gender value, said gender value being equal to zero if said person is female, said gender value being equal to 5 if said person is male;

assigning a blood pressure value, said blood pressure value being equal to zero if said person does not have high blood pressure, said blood pressure value being equal to 25 if said person has high blood pressure;

assigning an exercise value, said exercise value being equal to zero if said person is exercising, said exercise value being equal to 5 if said person is not exercising;

assigning a cholesterol value, said cholesterol value being equal to zero if said person does not have high cholesterol, said cholesterol value being equal to 10 if said person has high cholesterol;

calculating said risk of stroke according to the equation