Accurately replicating real-world activities in a virtual environment is essential for advancing innovations in simulations, training, and data analysis. This capability is particularly valuable for performing tasks that are challenging, risky, or impractical in real life, thereby enabling comprehensive studies without compromising safety or feasibility. In this context, capturing and analyzing real-world data within a virtual environment allows for the detailed reproduction of actions and a deeper understanding of their implications. This paper presents a system designed for capturing and virtually replicating walking and running activities by leveraging gyroscope data to monitor the rotational movements of the lower limbs. Our approach ensures precise replication of these movements, facilitating detailed biomechanical analysis. Our results demonstrate that using specialized sensors, as opposed to general-purpose devices like smartwatches, yields more accurate and localized data, enhancing the granularity of movement analysis. Additionally, we review the use of multimodal data specifically the integration of motion sensors and image analysis for human activity recognition. Although this integration is not yet part of our current system, the review will guide future improvements to enhance the accuracy and scope of human activity recognition in virtual environments. Our system lays the groundwork for the development of advanced devices for human data capture and proposes a communication interface between the real and virtual realms, supporting in-depth studies on movement biomechanics with potential applications in sports science and orthopedics.