A Multimodal Sensing-Driven Smart Shoe System for Gait Phase Recognition in Exoskeleton Applications

To enhance the precision and naturalness of exoskeleton control, this paper proposes a novel multimodal smart shoe design for precise gait analysis. Unlike conventional gait models that rely solely on kinematic data, our system integrates foot pressure distribution and foot deformation sensing to construct a more comprehensive foot motion model. The smart shoe is equipped with inertial measurement units (IMUs), a plantar pressure sensor and bending sensors. Multimodal data was collected and analyzed to extract gait features including trajectory, pressure distribution, and foot deformation patterns. A multimodal gait analysis model was developed using sensor fusion techniques. Experimental results demonstrate that the proposed system provides a more accurate and holistic representation of foot motion, offering enhanced biomechanical and dynamic information for future exoskeleton control systems.