In the present work we demonstrate the novel approach to improve the sensitivity of the “out of lab” portable capillary electrophoretic measurements. Nowadays, many enhancement methods are: (i) underused (non-optimal), (ii) overused (distorts the data), or (iii) inapplicable in field-portable instrumentation due to lack of computational power. Described innovative migration velocity-adaptive moving average method uses optimal averaging window size and can be easily implemented with microcontroller. The contactless conductivity detection was used as a model for the development of a signal processing method and the demonstration of its impact on the sensitivity. The frequency characteristics of the recorded electropherograms and peaks were clarified. Higher electrophoretic mobility analytes exhibit higher frequency peaks, while lower electrophoretic mobility analytes exhibit lower frequency peaks. Based on obtained data, a migration velocity-adaptive moving average algorithm was created, adapted and programmed into capillary electrophoresis data processing software. Employing the developed algorithm, each data point is processed depending on a certain migration time of the analyte. Because of the implemented migration velocity-adaptive moving average method the signal-to-noise ratio improved up to 11 times for sampling frequency of 4.6 Hz and up to 22 times for sampling frequency of 25 Hz. This paper could potentially be used as a methodological guideline for the development of new smoothing algorithms that require adaptive conditions in capillary electrophoresis, and other separation methods.