Gyroscope-Based Activity Classification and Virtual Twin for Human Movement Analysis

Simulating real-world activities in virtual environments with high fidelity is crucial for advancing training, simulation, and data analysis. This capability is particularly advantageous for complex, hazardous, or impractical tasks in real settings, as it enables extensive investigation without compromising safety or feasibility. This paper introduces a system for capturing and virtually simulating walking and running motions by utilizing gyroscope data to monitor the rotational dynamics of the lower limbs. Our approach provides an accurate recording of these movements, supporting a detailed biomechanical analysis. Our findings demonstrate that specialized sensors, as opposed to general-purpose devices like smartwatches, yield more precise, targeted data, thereby enhancing the quality of movement analysis.We also consider the potential of integrating multimodal data, such as combining motion sensors with image-based analysis for enhanced human activity recognition. Although not yet implemented, this integration offers a promising direction for future system improvements, aiming to increase the scope and precision of activity recognition in virtual environments. Our system lays foundational work for advanced human data capture devices, creating a link between the physical and virtual realms. This enables comprehensive studies of biomechanics with potential applications in fields like sports science and orthopedics.