FoRM: Foot-driven Reconstruction of Human Motion Using Dual-Modal Plantar Pressure and Inertial Sensing

Human motion reconstruction has wide applications in health monitoring, human-computer interaction, and virtual reality. While vision-based methods have made significant strides, they face challenges in daily scenarios due to occlusion, privacy concerns, and environmental constraints. Alternative approaches using wearable sensors often require complex device deployment or raise privacy issues. To address these challenges, we explore foot-based sensing as a non-invasive solution that maintains mobility and practicality. Supporting this approach, we construct a dual-modal human motion dataset with synchronized plantar pressure and inertial measurements, demonstrating the feasibility of reconstructing full-body motion using only foot-based sensing through a dual-modal motion reconstruction network. To enhance global motion reconstruction accuracy, we develop a motion-aware trajectory estimation strategy and implement a two-stage reconstruction pipeline that separates orientation estimation from other motion parameters. Our experiments show a Mean Per Joint Position Error of 69.43mm and a Root Trajectory Error of 0.267m for 2-second predictions. This work presents a practical approach for non-invasive and privacy-preserving motion capture. Code and dataset are available for research purposes at this link.