## 1 Introduction
This MMG sensor was stuck on the thigh muscles to detect and acquire human motion intention signal, then trigger the controller and actuate the LLRE [18]. [PERSON] et al. developed an MMG sensor using a flexible piezoresistive MMG signal sensor based on a pyramidal polydimethylsiloxane (PDMS) microarray sprayed with carbon nanotubes (CNTs). The experiment was conducted, and the results show that the sensitivity of the sensor can reach 0.4 kPa\({}^{-1}\) in the measurement range of 0-1.5 kPa, and the correlation reached 96% [19]. [PERSON] et al. developed a new, simple, non-invasive sensor based on a force-sensitive resistor (FSR), which is able to measure muscle contraction. The sensor, applied on the skin through a rigid dome, senses the mechanical force exerted by the underlying contracting muscles. In addition to the larger contraction signal, the sensor was able to detect the MMG. The frequency response of the FSR sensor was found to be large enough to correctly measure the MMG. Simultaneous recordings from flexor carpi ulnaris showed a high correlation (Pearson's r > 0.9) between the FSR output and the MMG linear envelope [20]. From recent research, it can be found that the three acquisition methods have their own advantages and disadvantages. The MMG signal collected by the accelerometer is highly accurate, but it is easily interfered by gravity acceleration and limb movement acceleration; the MMG signal collected by the microphone is not interfered by gravity acceleration and limb movement acceleration, but the influence of environmental noise cannot be ignored; the new piezoelectric and piezoresistive materials are highly comfortable to wear and have high acquisition accuracy, but the material preparation is complex, which makes it difficult to obtain the material and ultimately leads to a high cost of the sensor. Recent advances in human activity recognition have demonstrated the effectiveness of time-series regression algorithms in analyzing body sensor data, particularly for joint torque and angle prediction. [PERSON] et al. compared EMG and MMG sensing modalities for assist-as-needed lower-limb exoskeleton (LLE) control, showing that MMG achieves slightly lower torque prediction accuracy than EMG in isometric conditions (94.8 \(\pm\) 0.7% vs. 97.6 \(\pm\) 0.8% with neural networks) but comparable performance in dynamic exercises [21]. For joint angle estimation, [PERSON] et al. developed a deep belief network (DBN)-based regression model using multichannel MMG signals, reducing the RMSE of lower-limb flexion/extension angles by 50% compared to PCA-based methods during walking [22]. Similarly, [PERSON] et al. employed an artificial neural network to predict knee flexion angles during gait, revealing that prediction accuracy significantly degraded with longer time horizons (_p_ < 0.001), with the RMSE increasing from 0.68\({}^{\circ}\) (50 ms) to 4.62\({}^{\circ}\) (200 ms) [23]. [PERSON] further enhanced dynamic motion analysis by combining MMG with low-frequency acceleration signals (<6 Hz) for knee joint torque estimation. Using Support Vector Regression (SVR), this approach improved the coefficient of determination in healthy subjects and reduced the RMSE during closed kinetic chain (CKC) motions, such as walking-to-standing transitions and chair rises [24]. Despite the progress made, there are still some challenges. Differences in human anatomy and kinematics make it complex to create a universal model, variations in the wearing position can lead to changes in the collected signals, and there are also differences in the signals collected from different individuals; environmental factors such as electromagnetic interference and temperature can affect sensor performance, and non-invasive sensors, such as sEMG sensors based on dry electrodes and MMG sensors based on microphones, are convenient to wear but are susceptible to external noise. According to previous studies, although sEMG is the standard method for evaluating muscle activation, MMG also has its unique advantages.