The accelerometer devices as traditionally used in the epidemiological field for physical activity monitoring (e.g. Actigraph, Actical, and RT3) provide manufacturer-dependent output values called counts that are computed by obscure and proprietary signal processing techniques. This lack of transparency poses a challenge for comparison of historical accelerometer data in counts with data collected using raw accelerometry in S.I. units — m/s². The purpose of this study was to develop a method that facilitates the compatibility between both methods through conversion of raw accelerometer output data collected with inertial acceleration sensors into Actigraph counts — the most widely used (de facto standard) device brand in epidemiological studies. The basics of the conversion algorithm were captured from the technical specifications of the Actigraph GT1M. Fine-tuning of the algorithm was achieved empirically under controlled conditions using a mechanical shaker device. A pilot evaluation was carried out through physical activity monitoring in free-living scenarios of 19 adult participants (age: 47 ± 11 yrs, BMI: 25.2 ± 4.1 kg-m⁻²) wearing both devices. The results show that Actigraph counts estimated by the proposed method explain 94.2% of the variation in Actigraph counts (p < 0.001). The concordance correlation coefficient was 0.93 (p < 0.05). The sensitivity for classifying intensity ranged from 93.4% for light physical activity to 70.7% for moderate physical activity.