## 2 Materials and Methods
### Experimental Process
In this study, the MMG signal was collected by small contact microphones, which are shown in Figure 3. Contact microphones are not affected by ambient noise, nor by gravity or body movement acceleration; therefore, it is suitable for measuring MMG data related to human motion research. The contact sensor is first connected to the signal amplification module. The signal amplification module has a built-in Butterworth bandpass filter circuit, and the filtered MMG signal was transmitted to a Raspberry Pi. The MMG signal acquisition prototype is shown in Figure 4.  
In this study, 9-axis inertial measurement units (IMUs) were used to record the angle of joints throughout the movement process. The appearance of the IMU is shown in Figure 5. The IMU integrates a tri-axis accelerometer, gyroscope, and magnetometer, providing powerful functionality for tracking comprehensive motion dynamics. The 9-axis IMU sensor has matching data processing software, which can convert the data of the IMU sensor into human joint angle data. The positions of the IMU and the small contact microphones are shown in Figure 6, the interface of the data processing software is shown in Figure 7, the connection of the joint angle acquisition prototype is shown in Figure 8.  
Figure 3: Small contact microphone. The microphone is located on the right side and is about the same size as the USB receiver. Figure 2: Data collection process. During the experiment, both MMG and joint angles were collected. During the data collection process, scripts control the collection process of MMG data and joint angle data, and the simultaneity of the two data collections was confirmed using timestamps. Since the purpose of this study was to use MMG signals to predict joint angles, MMG signals were used to estimate joint angles as a function of their temporal characteristics after a certain time interval, the time interval ranges from 10 ms, 30 ms, to 60 ms. In previous studies [31], sEMG signals were used to predict joint angles, and the time in the future was set to 50 ms. Considering that MMG is not more ahead of human movement than sEMG, the time in the future used in this study is not much larger than 50 ms. As the time in the future increases, the prediction error gradually increases, and excessive prediction errors will cause the angle prediction to lose its practical value. Therefore, the maximum time in the future of 60 ms used in this study is reasonable. Figure 4: MMG signal acquisition prototype. The prototype consists of 4 small contact microphones, 4 voltage amplifier modules, a battery to power the microphone, a battery to power the Raspberry Pi, and Raspberry Pi. Figure 5: The 9-axis IMU. The sensor can collect 3-axis acceleration, a 3-axis angle, and angular velocity. This sensor was used to measure joint angle in this study. After bandpass filtering, the MMG signal underwent a series of preprocessing steps [31], which runs on the Raspberry Pi, as delineated below:  
1. Full-wave rectification;
2. Upper envelope signal extraction. To accurately locate the muscle bellies of each head of the biceps brachii and the deltoid, two anatomical landmarks were selected as reference points, and palpation was used for confirmation [32]. Figure 9 is a schematic diagram of the muscle belly position. The long head of the biceps brachii originates from the supraglenoid tubercle of the scapula. The short head originates from the coracoid process of the scapula. Its insertion is at the radial tuberosity anterior to the elbow joint, which is the tendon insertion point of the biceps  
Figure 8: Schematic diagram of the connection of the joint angle acquisition prototype. Figure 3.0 (a) Sensor configuration interface of data processing software; (b) Data display interface of data processing software. When wearing the IMU, first confirm the position and posture of the IMU as shown in (a), then run the acquisition software, and the software will automatically calculate the joint angle. brachi.