Motion2Press: Cross Model Learning from IMU to Plantar Pressure for Gait Analysis

Human locomotion is a fundamental aspect of daily life and a critical indicator of overall health. This study proposes a cross-modal learning framework based on inertial measurement units (IMUs) to infer plantar pressure distribution, ground reaction force (GRF), and center of pressure (COP). Traditional methods for pressure measurement rely on expensive and complex laboratory equipment, limiting their applicability in real-world scenarios. By integrating IMU data with deep learning techniques, this research achieves real-time, cost-effective, and accurate pressure estimation. The proposed method leverages IMU sensors to reconstruct foot pressure distribution in real-time, with high precision and adaptability to individual characteristics. A robust data pipeline and pretrained model enable accurate inference of GRF and COP using minimal IMU data, validated through case studies. Furthermore, The study builds a high-quality training dataset and optimizes the system for lightweight deployment, enhancing suitability for low-resource environments. Experimental results demonstrate the superior performance of the proposed approach across multiple evaluation metrics, particularly in individual adaptability, spatiotemporal feature capture, and deployment in resource-constrained settings. This work provides a novel and practical solution for clinical gait analysis, sports training, and health monitoring, while paving the way for advanced applications of intelligent devices in motion analysis.