## 1 Introduction  
In recent years, wearable exoskeletons and robotic limbs have gained widespread adoption across industrial, service, and medical rehabilitation fields, serving to reduce physical strain and restore mobility [1, 2]. Often described as a \"third hand\" or \"third leg,\" these assistive devices enhance human capabilities by providing supplementary support for demanding tasks [3, 4]. However, seamless integration with users requires precise synchronization--exoskeletons must align with human biomechanics, while robotic limbs must accurately interpret motion intent. Consequently, research in human motion intention recognition has become crucial, encompassing pattern classification, joint kinematics prediction, and end-point trajectory analysis [5, 6]. The critical significance of continuous motion prediction for rehabilitation exoskeletons lies in enhancing the spatiotemporal coherence of motion control [7]. Through temporal neural networks, continuous motion prediction captures the dynamic temporal features of signals, achieving a leap from discrete action recognition to continuous joint angle prediction. For instance, in exoskeletons, predictive algorithms can anticipate swing trajectories in advance, enabling the exoskeleton to adjust torque output beforehand and avoid motion stuttering caused by traditional discrete control [7]. This forward-looking control in the time dimension reduces the phase difference between assistance and human motion rhythm, significantly lowering energy consumption during movement. According to recent research [8], MMG, sEMG, inertial sensors, and ultrasound have all been applied to the research on human motion intention perception. The following is an introduction and comparison of them. MMG captures low-frequency mechanical vibration signals (2-150 Hz) generated during muscle contraction using accelerometers or microphones. It offers high signal-to-noise ratio, strong resistance to electromagnetic interference, and the ability to collect signals through clothing. Its advantage lies in directly reflecting mechanical changes in muscles, making it suitable for dynamic motion monitoring, though it has lower spatial resolution. sEMG records muscle electrical activity signals (frequency range: 20-500 Hz) on the skin surface, containing rich neural control information. However, it is susceptible to electrode placement, variations in skin impedance, and electromagnetic interference, requiring direct skin contact. Its strength is high temporal resolution, making it ideal for fine motion recognition, but signal stability is relatively poor. Inertial sensors (accelerometers, gyroscopes) measure limb motion states by detecting acceleration and angular velocity, offering real-time performance and non-contact operation. However, they cannot directly reflect muscle activity and suffer from cumulative errors. They are suitable for navigation and motion trajectory analysis but require integration with other modalities to improve accuracy. Ultrasound uses high-frequency sound waves to image muscle morphology and dynamic changes, enabling the observation of deep muscle structures. However, it has high equipment costs, poor portability, and relies on operator expertise. When combined with sEMG, it can provide complementary morphological information about muscles. Recent studies have increasingly focused on human motion intention recognition using physiological signals, among which mechanomyography (MMG) has emerged as a prominent modality. MMG signals, which capture muscle contraction-induced vibrations, exhibit a strong correlation with human movement dynamics. For instance, [PERSON] et al. developed a wearable hand gesture recognition system based on pneumatic MMG (pMMG) for direct wrist tendon monitoring, achieving 98.12% accuracy--surpassing conventional sEMG systems (93.89%) [9]. Similarly, [PERSON] et al.