KineticsSense: A Multimodal Wearable Sensor Framework for Modeling Lower-Limb Motion Kinetics

Current human motion analysis primarily emphasizes kinematics, often neglecting underlying biomechanical factors such as muscle activation and force generation. This restricts a comprehensive understanding of movement mechanics, particularly in rehabilitation and sports science. To bridge this gap, we propose KineticsSense, a multimodal framework integrating inertial measurement units (IMUs) and plantar pressure data to predict lower-limb electromyography (EMG) signals, capturing the kinetics of human motion. By leveraging the complementary strengths of IMU-derived kinematic features and pressure-based ground reaction force estimations, KineticsSense provides a richer biomechanical representation beyond conventional kinematic analyses. Through extensive experiments covering a diverse range of lower-limb activities --- including walking, running, squats, and jumps --- we demonstrate the robustness and adaptability of KineticsSense in accurately estimating muscle activation patterns. Our results further indicate that the system generalizes well across individuals with varying physical attributes, highlighting its potential for real-world applications. Furthermore, case studies in rehabilitation assessment and athletic performance analysis showcase the practical value of our approach in monitoring neuromuscular function and optimizing movement strategies. By bridging kinematics and biomechanics, this work enhances understanding of human motion dynamics and lays a foundation for advances in rehabilitation, sports science, and human-computer interaction.