Abstract: An ideal target weight training recommendation method ascertains a user's ideal target weight with a user-data algorithm that is operable to approximate an available weight value for a user to utilize for a given target weight training recommendation in addition to generating a rate in which a user's muscle fatigues. To do so, the user generates initial completed repetitions until the muscles fatigue. A baseline strength value is calculated with the initial completed repetitions and a baseline strength coefficient. A baseline strength value is created and used in calculating ideal target weight values. A y-intercept approximate functions involving user-selected desired target repetition values. The user manipulates a resistance structure associated with the ideal target weight values and said values are rounded to the nearest whole number. The user-selected desired target repetition values generate a second completed repetition values for subsequent sets for adjustment of a strength curve.

Abstract: An ideal target weight training recommendation method ascertains a user's ideal target weight with a user-data algorithm that is operable to approximate an available weight value for a user to utilize for a given target weight training recommendation in addition to generating a rate in which a user's muscle fatigues. To do so, the user generates initial completed repetitions until the muscles fatigue. A baseline strength value is calculated with the initial completed repetitions and a baseline strength coefficient. A baseline strength value is created and used in calculating ideal target weight values. A y-intercept approximate functions involving user-selected desired target repetition values. The user manipulates a resistance structure associated with the ideal target weight values and said values are rounded to the nearest whole number. The user-selected desired target repetition values generate a second completed repetition values for subsequent sets for adjustment of a strength curve.

Abstract: An ideal target weight training recommendation system and method ascertains a user's ideal target weight with a user-data algorithm that computes a strength coefficient based on weight repetitions. The user generates initial completed repetitions until the muscles fatigue. A baseline strength value is calculated with the initial completed repetitions and a baseline strength coefficient. A resistance value of the free weights divided by the baseline strength coefficient, produces the baseline strength value. The baseline strength value is used in calculating ideal target weight values. A user-data defined y-intercept approximate functions. Each y-intercept approximate functions involving user-selected desired target repetition values. The user manipulates a resistance structure associated with the ideal target weight values. The ideal target weight values are rounded to the nearest whole number. The user-selected desired target repetition values generate second completed repetition values for subsequent sets.