In recent years, there has been a growing interest in the virtual world and e-gaming. A common challenge shared by these two domains, which may initially appear distinct, lies in the security issues associated with such environments, particularly with respect to activity recognition. This technological innovation has therefore underscored the pressing need for advancements in information systems focused on security, serving as the motivation for this study. Activity detection emerges as a crucial issue in many different fields, with the ultimate aim of verifying the actions performed by users in contexts such as metaverse and competitive e-gaming events. The outcomes of our work leverage biomechanical data, related to the above contexts, and propose a comprehensive methodology for organizing and modelling such data. This methodology is designed to be entirely independent of the context and the specific data collection approach, with the goal of employing these data to achieve activity recognition with a certain degree of reliability.