Continuum robots suffer from low control accuracy and poor safety performance due to large modeling errors, complex and variable shapes, and susceptibility to external dynamic disturbances, which makes it challenging to achieve precise operations and safe interactions in confined or complex environments. To address these issues, a self-sensing approach for continuum robot shape estimation based on IMU measurements and a piecewise polynomial curvature model is proposed, enabling the detection and reconstruction of their three-dimensional curved shapes. First, a shape detection system is designed by deploying multiple IMUs along the continuum robot, and the PPC model is employed for kinematic modeling and analysis to accurately characterize non-uniform bending deformations. To estimate the robot′s bending profile and end-effector position, a self-sensing approach for shape estimation that fuses IMU measurements with a PPC model is introduced. In this framework, the modal coefficients of each curvature segment are determined from a limited number of attitude observations, thereby reconstructing the overall shape of the robot. Finally, an experimental platform for shape detection is established, and the proposed method is validated through theoretical simulations and multiple experiment trials. The results demonstrate that the proposed approach achieves reliable performance under various bending angles and loading conditions, with an average reconstruction error of approximately 2.5 mm and a deviation below 3 mm under loaded scenarios. In addition, dynamic bending experiments further validated the peoposed method′s real-time capability and shape tracking performance during continuous motion, with an average end-effector position error of approximately 2.57 mm. This validates the effectiveness and accuracy of the constructed motion model and proposed shape detection method, providing a reliable shape sensing foundation for precise operation and closed-loop control of continuum robots in constrained environments.