Typically, the kinetics of blood flow (BF) are determined with data processing methods that yield poor kinetics characterization (i.e., large confidence intervals). In this study we examined the validity and usefulness of a low pass filter (LP_FILTER) to reduce point-to-point variability and enhance parameter estimation of the kinetics of BF. Computer simulations were used to determine the power spectrum (fast Fourier transform) of simulated responses. In addition, we studied the leg BF response to a single transition in four subjects during supine knee-extension exercise using three methods of data processing [Beat-by-Beat, average of 3 cardiac cycles (AVG_{3 BEATS}) and LP_FILTER]. Femoral artery blood velocity was measured by Doppler ultrasound [and converted to flow using resting values of femoral artery diameter]. The power spectrum of BF, which contained the kinetics information ( 0.2 Hz) did not overlap with the main sources of 'noise' (muscle contraction and cardiac cycle) in either simulated responses or Doppler measurements. For the data from 4 subjects, there were no significant differences between the parameter estimates for a 2 exponential fitting function using Beat-by-Beat, AVG_{3 BEATS} and LP_FILTER (P > 0.05). However, LP_FILTER (cutoff = 0.2 Hz) resulted in a significantly lower standard error of the estimate (SEE) for all parameters (P < 0.05). The means ± SD for the SEE of time-related parameters for Beat-by-Beat, AVG_{3 BEATS} and LP_FILTER were: time constant-phase 1 = 5.0 ± 1.1 s, 4.5 ± 2.1 s and 0.3 ± 0.2 s; time delay-phase 2 = 17.8 ± 7.9 s, 12.8 ± 7.5 s and 1.4 ± 1.4 s; time constant-phase 2 = 15.8 ± 4.6 s, 9.9 ± 2.9 s and 1.1 ± 0.5 s, respectively. In conclusion, LP_FILTER appeared to be a valid procedure providing the highest signal-to-noise ratio while maintaining original data density. This resulted in better kinetic characterization of BF response (i.e., smallest confidence interval for parameter estimates).