The objective of this pilot study is to propose an adaptable approach for automated generation of personalized estimation equations for blood pressure (BP) monitoring during home exercise. We investigated both population-based and individual-based bivariate correlations between blood pressure (BP) and possible BP predictors derived from electrocardiogram (ECG) and photoplethysmogram (PPG). The predicting parameters consisted of pulse transit time (PTT) obtained from characteristic points of original raw PPG signal, as well as the first and second PPG derivatives. The population-based parameter that showed the most prominent association with systolic blood pressure (SBP) was PTT Peak (R=-0.787, p<0.01) for the all analyzed cases (N=45), and R-A - for all bivariate correlations (R=-0.805, p<0.01). The individual-based results showed similar associations as in population-based analysis. However, the strength of association between BP and ECG/PPG derivatives varied from participant to participant. We concluded that automated generation of BP prediction equations should be based on different ECG/PPG derivatives in different persons depending on individualized bivariate analyses of all possible BP predictors.