The creation of reliable fall detection systems requires access to large quantities of labeled sensor data. Collecting such data in real world settings, especially for critical events such as falls, poses practical, ethical, and safety challenges. In this work, we propose a digital twin of a pressure-sensor-based floor system designed to simulate realistic human-floor interactions in a virtual environment. Our virtual replica mirrors the physical floor structure, composed of tightly networked sensor tiles, enabling the simulation of various fall scenarios using rigid-body ragdoll models. Synthetic sensor data is generated through virtual collisions, providing time-series pressure patterns analogous to real-world recordings. This method enables scalable data generation for training machine learning models focused on binary fall detection, with future expansion to multi-class activity recognition. Our approach aims to bridge the gap between the physical constraints of data collection and the need for extensive training data-sets in ambient assisted living systems.