## 1 Introduction
proposed a gesture recognition framework combining Continuous Wavelet Transform (CWT) feature extraction with a Domain-Adversarial CNN (DACNN), reporting accuracies of 87.43% (same-posture) and 64.29% (different-posture), which improved to 92.32% and 71.75%, respectively, when extending the MMG window from 200 ms to 600 ms [10]. Further advancing this field, [PERSON] et al. leveraged the Hilbert-Huang Transform (HHT) and random forest regression to estimate joint torque from MMG signals, enabling precise control of rehabilitation exosuits [11]. Complementing these efforts, [PERSON] et al. demonstrated significant correlations (\(p<0.05\)) between forearm anthropometrics, MMG root-mean-square values, and flexion torque, highlighting MMG's potential for muscle activation and size prediction [12]. From recent research, it can be found that both MMG and surface electromyography (sEMG) signals are signals generated by muscle contraction, and they have some differences and connections. MMG measures the vibrational characteristics of muscles, while sEMG detects the electrical activity associated with muscle activation; MMG monitors the mechanical changes during muscle contraction, whereas sEMG tracks the timing of muscle activation; MMG has a lower frequency than sEMG, requiring a lower sampling frequency; and MMG sensors are more convenient to wear compared to sEMG sensors, as they do not require skin preparation or adhesive attachment. In recent studies, there were three main types of MMG acquisition methods: one was with an accelerometer, one was with a microphone, and the other was with a new piezoelectric sensor or piezoresistive sensor. [PERSON] et al. developed high-sensitivity acceleration sensors to detect vibrations that are >10 dB smaller than those detected by conventional commercial sensors. They were the first to measure high-frequency micro-vibrations in muscle fibers, termed micro MMG in patients with Parkinson's disease (PwPD), using a high-sensitivity acceleration sensor [13]. [PERSON] et al. investigated the relationship of the accelerometer mass and the MMG signal. When the accelerometer mass increased from 8 g to 13 g, the amplitude of the MMG signal increased the most, and the MF of the MMG signal decreased the most. However, for accelerometers heavier than 13 g, no significant change was observed in both the amplitude and MF [14]. [PERSON] et al. compared MMG recorded by a condenser microphone (MIC) and an accelerometer (ACC) during submaximal isometric, concentric, and eccentric contractions in 14 males. The results showed that the information contained in microphone- and accelerometer-based MMG signals is different despite similar trends. It can be concluded that at low-moderate movement velocity, concentric contractions can be investigated by means of an accelerometer and microphone [15]. [PERSON] et al. introduced a new mode of MMG signal capture for enhancing the performance of human-machine interfaces (HMIs) through the modulation of normal pressure at the sensor location. Utilizing this novel approach, increased MMG signal resolution is enabled by a tunable degree of freedom normal to the microphone-skin contact area [16]. [PERSON] presented a new design of an MMG sensor, which consists of two coupled piezoelectric disks in a single housing. The sensor's functionality was verified in two experimental setups related to typical MMG applications: an estimation of the force/MMG relationship under static conditions and a neural network-based gesture classification [17]. [PERSON] et al. developed a new type of fiber-based MMGmsensor, which was used as a motion on/off trigger sensor for a lower limb rehabilitation exoskeleton (LLRE). Piezoelectric material, polyvinylidene difluoride (PVDF), was applied to fabricate an MMG sensor, using the near-field electrospinning technology to electrically spin PVDF fibers to 5-10 \(\upmu\)m in diameter. PVDF fibers attached on an interdigitated (IDT) electrode with different pole pairs and interspaces were packaged into an MMG sensor.