This paper carries out a research on patients’ lower limb posture capture strategy based on the lower limb rehabilitation of patients with sports function injury. The study is based on the posture filtering algorithm and designed a lower limb joint localization model based on the quaternion Kalman filter. The model utilizes five IMUs to capture the patient’s lower limb movements to determine the posture of the patient’s critical limbs in three-dimensional space and establish the joint coordinate system. Based on the filtered pose quaternions, the joint coordinate system of the lower limb is solved to obtain the optimal estimation of the lower limb pose. The results of simulation experiments show that the algorithm of this paper can make the motion data smoother and satisfy the motion requirements. The valuation of this paper’s algorithm on the Z-axis in the single-axis rotation experiment is stable from – 90° to 90°, while the valuation on the X-axis and Y-axis is near 0°. And the error in the ankle motion trajectory is small, with a mean value of 1.36°. The example results illustrate that the rehabilitation system equipped with the algorithm of this paper is basically consistent with the thigh elevation curve of the optical method in the patient’s lower limb motion monitoring during walking, and the error is within 6°. The research in this paper provides a new technical means for lower limb rehabilitation training, which helps to improve the personalization and precision of rehabilitation training.