We recently explored if methods to automate the identification of movement would be accurate to determine body position on the bike. In this study, it is apparent that an existing algorithm to detect human body segments (OpenPose) provides strong agreement in relation to a criterion measure. This innovation provides opportunities for developing tools that could automatically determine body posture on the bike, which should reduce variability in bike fitting, for example. In addition, implementing these tools for remote assessment of cyclists can expand access to bike fitting services in remote areas with minimum resources required (i.e. smartphone or webcam).