Ear2Pos: A Dual-IMU Framework for Full-Body Pose Estimation Using Earbuds
Haolong Wang, Zihao Yang, Hao Wang, Lin Wang · IEEE Internet of Things Journal · 2025
In this paper, we present Ear2Pos, a novel framework for full-body pose estimation using only two Inertial Measurement Units (IMUs) integrated into earbuds. Unlike traditional motion capture systems requiring multiple sensors, Ear2Pos leverages a minimal setup to achieve high-accuracy 3D motion reconstruction. The system incorporates a dualcoordinate framework and Transformer-based modeling to predict joint positions and rotations. Additionally, we propose a personalized skeletal parameterization mechanism, utilizing extracted bone lengths from a single image to enhance individual adaptability. Extensive evaluations demonstrate that Ear2Pos achieves state-of-the-art accuracy in pose estimation when using two sensors, outperforming other methods in upper-body motion prediction with an average joint position error of 5.04 cm. Furthermore, we explore clinical applications, particularly in gait analysis for cervical spondylotic myelopathy (CSM) patients, showcasing the frameworks potential for rehabilitation assessment. These findings indicate that Ear2Pos is a promising lightweight solution for non-invasive motion capture, offering robust performance in both research and real-world applications.