Objective physical activity (PA) quantification is traditionally achieved using lightweight accelerometers accounting for activity frequency, intensity and duration. The accelerometer data are usually converted into activity counts and these counts can be used on their own to quantify the intensity and duration of a PA period or they can serve as features for energy expenditure computation or activity classification. This paper investigates the way how Actigraph counts are computed. Several points are discussed regarding bandpass filtering and amplitude non-linearities that may hamper some analysis. Experimental data were used 1) to assess reconstructed filter performances to replicate ActiGraph counts during an urban-circuit involving 20 subjects wearing an ActiGraph GT3X+ and 2) explain filter limitations (e.g. plateauphenomenon) thanks to a treadmill test with incremental speed (n=4). This study reproduces well ActiLife filter and reveals the impact of band-pass filtering on ActiLife count conversion. These results provide some keys to interpret knowingly ActiLife count based studies.