## 1 Introduction  
Recent research has demonstrated that device-assisted therapy is the primary approach for restoring physical functions in patients with physical disabilities [1]. The synchronization of motor intention with corresponding physical action is a critical determinant in optimizing the efficacy of rehabilitation exercises [2, 3]. Rehabilitation exoskeletons have received widespread attention for their ability to address the inherent limitations of traditional manual rehabilitation training, including insufficient precision in motion control, the shortage of rehabilitation therapists, and the high treatment costs [4, 5]. However, in the rehabilitation training stage, a lower limb rehabilitation exoskeleton is difficult to effectively comply with the patient, and there are many challenges in the interaction between the exoskeleton and the patient [6, 7]. For the above reasons, two methods to achieve compliance control in rehabilitation exoskeletons are proposed [8, 9]. The first is force/position hybrid control, which realizes the conflict-free control of position and force, but its limitation is that the human-robot interaction force is regarded as a disturbance [10]. The second solution is impedance control, which was first proposed by [PERSON] [11]. The primary goal of impedance control is to regulate the dynamic force/position relationship in human-robot interaction systems, enabling robots to emulate human biomechanics, as well as to enhance the flexibility and functional performance of applications like rehabilitation exoskeletons [12, 13, 14]. For its good robustness and availability, impedance control has been widely developed [15; 16]. A neural network-based impedance control framework for real-time dynamic adjustment of impedance parameters is proposed in [17], improving adaptability and interaction performance. In [18], an event-triggered impedance controller is designed to enable precise trajectory tracking in interactive environments. In the later period of lower limb rehabilitation, the patient has strong autonomous motor awareness and motor ability. At this stage, the rehabilitation task is led by the patient, and the exoskeleton is used as a collaborative device to assist training. If the exoskeleton does not know the patient's movement intention, it will be treated as a load and cause a loss of strength in the affected limb, affecting the effectiveness of rehabilitation training. Therefore, it is important to estimate the motion intention of the affected limb. Many algorithms for estimating human motion intention have been proposed [19; 20; 21]. In [22], the electroencephalogram is utilized to infer motion intentions, enabling accurate rehabilitation trajectory tracking. In [23], motion intentions were estimated using input data and Deep Learning algorithms, facilitating precise and reliable control of lower limb exoskeletons. To adapt to the changing rehabilitation needs of impaired limbs, achieving real-time adjustment of the desired impedance of the exoskeleton remains a key challenge in lower limb rehabilitation training. An online impedance controller is designed in [24] using a novel adaptive method for parameter estimation, achieving real-time control. Ref. [25] adopts reinforcement learning with an actor-critic algorithm to obtain the optimal value of impedance parameters for robot contact tasks. An iterative learning method is proposed in [26], where stiffness and viscosity parameters in the target impedance model can be adjusted. In [27], an estimation method of human upper limb impedance for a 2-DOF assembly robot with nonlinear friction is proposed. The researchers in [28] employed the recursive least squares algorithm to perform online real-time parameter estimation, effectively satisfying the system's real-time control requirements. Ensuring the safety of patients is essential during human-exoskeleton interaction, particularly in the scenario of lower limb rehabilitation training.