PressInPose: Integrating Pressure and Inertial Sensors for Full-Body Pose Estimation in Activities

The accurate assessment of human body posture through wearable technology has significant implications for sports science, clinical diagnostics, rehabilitation, and VR interaction. Traditional methods often require complex setups or are limited by the environment’s constraints. In response to these challenges, this paper presents an innovative approach to human posture estimation under complex motion scenarios through the development of an advanced shoe insole embedded with pressure sensors and an Inertial Measurement Unit (IMU). Coupled with a single wrist-mounted IMU, our system facilitates a comprehensive analysis of human biomechanics by integrating physical kinematics modeling based on pressure data with a multi-region human posture estimation network. To enhance the robustness of our system model, we employed large language models to generate virtual human motion sequences. These sequences were utilized to create synthetic IMU data for data augmentation purposes, addressing the challenge of limited real-world data availability and variability. Our approach uniquely combines physical modeling with data-driven techniques to improve the accuracy and reliability of posture estimation. Experimental results demonstrate that our integrated system significantly advances wearable technology for motion analysis. The Mean Per Joint Position Error (MPJPE) was reduced to 7.75 cm, highlighting the effectiveness of our multi-modal modeling and virtual data augmentation in refining posture estimation. CCS Concepts: • Human-centered computing → Human computer interaction (HCI); Ubiquitous and mobile computing systems and tools.