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Electromyogram (EMG) based continuous joint motion estimation is essential in various human-involved scenarios, and variations across speeds and subjects have been a long-time challenge. This paper proposes a novel adaptive oscillator-driven gait phase model for continuous motion estimation of lower limbs, and achieves robust performance in miscellaneous walking speed conditions. The proposed model consists of three key components. Firstly, the mapping relationships with the state of the gait phase and its time derivative (gait frequency) are analyzed and constructed by Gaussian process regression (GPR). With these relationships, the subject-specific profiles of the gait phase state and the joint motion cross speeds can be formulated. Then, an improved adaptive frequency oscillator (AFO) with EMG activation signals is designed to online estimate the inputs of the gait phase profiles, and an additional phase alignment module is elaborated to exponentially compensate for the offsets and errors. Afterward, the proposed model integrates the information derived from both the formulated position profile and estimated integral of the velocity with an adaptive unscented Kalman filter (UKF) scheme to reject disturbance, thus improving the accuracy and smoothness of the estimation outputs of joint motion. Comprehensive validation experiments were conducted to estimate the motion of three joints (hip, knee, and ankle) on both a benchmark dataset and a self-collected dataset. Compared with the existing methods, the proposed method achieved significantly better performance ( p < 0.01) in terms of root mean square error (RMSE), correlation coefficient (CC), and coefficient of determination ( R 2 ). Further, the standard deviations of RMSE decreased 31.8% over seventeen subjects and 36.3% over twentyeight speed conditions in the benchmark dataset, and decreased 38.1% and 42.7% in the self-collected dataset, both of which demonstrated good consistency in the cross-subject and cross-speed estimation of three joints. These results indicate that the proposed adaptive oscillator-driven gait phase model promises an accurate and robust solution for joint motion estimation in miscellaneous walking conditions.