Method of determining an optimum workload corresponding to user's target heart rate and exercise device therefor

A method of determining an optimum workload corresponding to a target heart rate of a user, said method comprising the steps of:

• setting said target heart rate value of the user;
• measuring a first steady heart rate of the user during a first exercise cycle, in which a first workload is applied to the user, said first workload being derived in accordance with said target heart rate and a statistically obtained workload versus heart rate correlation corresponding to at least one factor selected from the group consisting of the user's age, gender, body weight, body height, and whole body fat;
• measuring a second steady heart rate of the user during at least one second exercise cycle, in which a second workload is applied to the user, said second workload being derived by entering input parameters including the applied workload and the measured heart rate at an immediately previous exercise cycle into a first multivariate model equation, said multivariate model equation being defined as a polynomial in said input parameters which is an expression involving a finite sum of terms of form bx.sub.1.sup.p1 x.sub.2.sup.p2 . . . x.sub.m.sup.pm, where x.sub.1,x.sub.2, . . . x.sub.m are said input parameters, b is some number, and p1,p2, . . . pm are integers;
• and
• entering said input parameters including the applied workload and the measured heart rate at the last second exercise cycle into said first model equation to thereby determine said optimum workload.

A method of determining an optimum workload corresponding to a target heart rate of a user, said method comprising the steps of:

• setting said heart rate of the user;
• measuring a steady heart rate of the user during an exercise cycle, in which a first workload is applied to the user, said first workload being derived in accordance with said target heart rate and a statistically obtained workload versus heart rate correlation corresponding to at least one factor selected from the group consisting of the user's age, gender, body weight, body height, and whole body fat;
• and
• determining said optimum workload from a maximum aerobic capacity defined as a maximum workload corresponding to a maximum heart rate peculiar to the user, said aerobic capacity being predicted by entering input parameters including said first workload and the measured heart rate at said exercise cycle into a first multivariate model equation, said multivariate model equation being defined as a polynomial in said input parameters which is an expression involving a finite sum of terms of form bx.sub.1.sup.p1 x.sub.2.sup.p2 . . . x.sub.m.sup.pm, where x.sub.1,x.sub.2, . . . x.sub.m are said input parameters, b is some number, and p1,p2, . . . pm are integers.

A method of determining an optimum workload corresponding to target heart rate of a user, said method comprising the steps of:

• setting said heart rate of the user;
• measuring a first steady heart rate of the user during a first exercise cycle, in which a first workload is applied to the user, said first workload being derived in accordance with said target heart rate and a statistically obtained workload versus heart rate corresponding to at least one factor selected from the group consisting of the user's age, gender, body weight, body height, and whole body fat;
• measuring a second steady heart rate of the user during an at least one second exercise cycle, in which a second workload is applied to the user, said second workload being derived from a maximum aerobic capacity defined as a maximum workload corresponding to a maximum heart rate peculiar to the user, said aerobic capacity being predicted by entering input parameters including the applied workload and the measured heart rate at an immediately previous exercise cycle into a first multivariate model equation, said multivariate model equation being defined as a polynomial in said input parameters which is an expression involving a finite sum of terms of form bx.sub.1.sup.p1 x.sub.2.sup.p2 . . . x.sub.m.sup.pm, where x.sub.1, x.sub.2, . . . x.sub.m, are said input parameters, b is some number, and p1,p2, . . . pm are integers;
• and
• determining said optimum workload from said maximum aerobic capacity predicted by entering said input parameters including the applied workload and the measured heart rate at the last second exercise cycle into said first model equation.

A method of determining an optimum workload corresponding to a target heart rate of a user, said method comprising the steps of:

• setting said target heart rate of the user;
• measuring a steady heart rate of the user during an exercise cycle, in which a first workload is applied to the user, said first workload being derived in accordance with said target heart rate and a statistically obtained workload versus heart rate correlation corresponding to at least one factor selected from the group consisting of the user's age, gender, body weight, body height, and whole body fat;
• and
• entering input parameters including said first workload and the measured heart rate in said exercise cycle into a first multivariate model equation to thereby determine said optimum workload;
• wherein said multivariate model equation is defined as a polynomial in said input parameters, said polynomial being prepared by means of a neural network analysis characterized in that a plurality of hidden layers are set between said optimum workload and said input parameters to determine said optimum workload by using all of said hidden layers, each of which is determined by using all of said input parameters.

A method of determining an optimum workload corresponding to a target heart rate of a user, said method comprising the steps of:

• setting said target heart rate value of the user;
• measuring a first steady heart rate of the user during a first exercise cycle, in which a first workload is applied to the user, said first workload being derived in accordance with said target heart rate and a statistically obtained workload versus heart rate correlation corresponding to at least one factor selected from the group consisting of the user's age, gender, body weight, body height, and whole body fat;
• measuring a second steady heart rate of the user during at least one second exercise cycle, in which a second workload is applied to the user, said second workload being derived by entering input parameters including the applied workload and the measured heart rate at an immediately previous exercise cycle into a first multivariate model equation, said multivariate model equation being defined as a polynomial in said input parameters, said polynomial being prepared by means of a neural network analysis characterized in that a plurality of hidden layers are set between said second workload and said input parameters to determine said second workload by using all of said hidden layers, each of which is determined by using all of said input parameters;
• and
• entering said input parameters including the applied workload and the measured heart rate at the last second exercise cycle into said first model equation to thereby determine said optimum workload.

A method of determining an optimum workload corresponding to a target heart rate of a user, said method comprising the steps of:

• setting said target heart rate of the user;
• measuring a steady heart rate of the user during an exercise cycle, in which a first workload is applied to the user, said first workload being derived in accordance with said target heart rate and a statistically obtained workload versus heart rate correlation corresponding to at least one factor selected from the group consisting of the user's age, gender, body weight, body height, and whole body fat;
• and
• determining said optimum workload from a maximum aerobic capacity defined as a maximum workload corresponding to a maximum heart rate peculiar to the user, said aerobic capacity being predicted by entering input parameters including said first workload and the measured heart rate at said exercise cycle into a first multivariate model equation;
• wherein said multivariate model equation is defined as a polynomial in said input parameters, said polynomial being prepared by means of a neural network analysis characterized in that a plurality of hidden layers are set between said maximum aerobic capacity and said input parameters to determine said maximum aerobic capacity by using all of said hidden layers, each of which is determined by using all of said input parameters.

A method of determining an optimum workload corresponding to target heart rate of a user, said method comprising the steps of:

• setting said target heart rate value of the user;
• measuring a first steady heart rate of the user during a first exercise cycle, in which a first workload is applied to the user, said first workload being derived in accordance with said target heart rate and a statistically obtained workload versus heart rate corresponding to at least one factor selected from the group consisting of the user's age, gender, body weight, body height, and whole body fat;
• measuring a second steady heart rate of the user during an at least one second exercise cycle, in which a second workload is applied to the user, said second workload being derived from a maximum aerobic capacity defined as a maximum workload corresponding to a maximum heart rate peculiar to the user, said aerobic capacity being predicted by entering input parameters including the applied workload and the measured heart rate at an immediately previous exercise cycle into a first multivariate model equation, said first model equation being defined as a polynomial in said input parameters, said polynomial being prepared by means of a neural network analysis characterized in that a plurality of hidden layers are set between said maximum aerobic capacity and said input parameters to determine said maximum aerobic capacity by using all of said hidden layers, each of which is determined by using all of said input parameters;
• and
• determining said optimum workload from said maximum aerobic capacity predicted by entering said input parameters including the applied workload and the measured heart rate at the last second exercise cycle into said first model equation.

A method of determining a physical strength of a user, said method comprising the steps of:

• measuring a first steady heart rate of the user during a first exercise cycle, in which a first workload is applied to the user, said first workload being derived in accordance with said target heart rate and a statistically obtained workload versus heart rate correlation corresponding to at least one factor selected from the group consisting of the user's age, gender, body weight, body height, and whole body fat;
• measuring a second steady heart rate of the user during at least one second exercise cycle, in which a second workload is applied to the user, said second workload being derived by multiplying an exercise level (%)/100 by a maximum aerobic capacity which is predicted by entering input parameters including the applied workload and the measured heart rate at an immediately previous exercise cycle into a first multivariate model equation, said multivariate model equation being defined as a polynomial in said input parameters which is an expression involving a finite sum of terms of form bx.sub.1.sup.p1 x.sub.2.sup.p2 . . . x.sub.m.sup.pm, where x.sub.1, x.sub.2, . . . x.sub.m are said input parameters, b is some number, and p1,p2, . . . pm are integers;
• and
• preparing a regression line indicative of a workload versus heart rate relationship of the user in accordance with results of said first and second exercise cycles, to thereby determine a maximum workload corresponding to a maximum heart rate of the user as said physical strength.

A method of determining a physical strength of a user, said method comprising the steps of:

• measuring a first steady heart rate of the user during a first exercise cycle, in which a first workload is applied to the user, said first workload being derived in accordance with said target heart rate and a statistically obtained workload versus heart rate correlation corresponding to at least one factor selected from the group consisting of the user's age, gender, body weight, body height, and whole body fat;
• measuring a second steady heart rate of the user during at least one second exercise cycle, in which a second workload is applied to the user, said second workload being derived by multiplying an exercise level (%)/100 by a maximum aerobic capacity which is predicted by entering input parameters including the applied workload and the measured heart rate at an immediately previous exercise cycle into a first multivariate model equation, said first model equation being defined as a polynomial in said input parameters, said polynomial being prepared by means of a neural network analysis characterized in that a plurality of hidden layers are set between said maximum aerobic capacity and said input parameters to determine said maximum aerobic capacity by using all of said hidden layers, each of which is determined by using all of said input parameters, and
• preparing a regression line indicative of a workload versus heart rate relationship of the user in accordance with results of said first and second exercise cycles, to thereby determine a maximum workload corresponding to a maximum heart rate of the user as said physical strength.