给下面这篇文献综述写个摘要，包括关键字：1. Background


1.1	Introduction
Yang & Chen (2009) state that language has always been considered to be the most natural, convenient, and effective way to exchange information, and the development of human-computer voice interaction (HMI) technology is essential for social communication, cooperation, and exploration. Traditional acoustic sensors, such as condenser and piezoelectric microphones, often suffer from low sensitivity, high power consumption, or complex signal processing systems. Therefore, the development of new acoustic sensors to improve the quality and efficiency of voice interaction has become a research hotspot.

1.2	Human-computer voice interaction
When exploring the combination of metamaterial-based spherical focused acoustic sensors (MBAS) and voice interaction, the naturalness and efficiency of human-computer interaction (HRI) are crucial. As the most natural way of human communication, speech not only conveys linguistic information, but also contains rich paralinguistic information, such as prosody, tone and emotion. Luzanto et al. (2024) state that this information is essential for robots to understand human intentions and emotions. 
In HRI, speech recognition technology needs to maintain high accuracy and robustness in a wide range of ambient noise and reverberation conditions. The linguistic information in the speech signal, i.e., the semantic content, can be parsed by automatic speech recognition (ASR) and natural language processing (NLP) technologies. In recent years, deep neural network technology has also been used to extract semantic information directly from speech signals, improving the efficiency and accuracy of recognition.
Paralinguistic information, such as prosodic characteristics (pitch, duration, and sound quality), is equally important for understanding intentions, attitudes, and emotions in communication. This information can be extracted by analyzing the features of speech signals, or by utilizing neural network models to identify and learn. Spoken language carries not only information, but also accent, tone, voice quality, social groups, physical and psychological identity, emotions, etc. (Tiwari & Tiwari, 2012). In dynamic HRI scenarios, a high-quality acoustic signal is essential to accurately extract this information. 
However, noise and reverberation in the real-world environment can seriously affect the quality of speech signals, which in turn affects the performance of speech detection and information extraction (Han et al., 2019). Therefore, in order to improve the accuracy of voice interaction, it is necessary to separate the noise and reverberation in the audio through signal processing technology to obtain a clearer signal. This is especially important for MBAS technology, which requires real-time speech enhancement and intelligibility in a dynamically changing environment to support effective human-machine voice interaction. 

1.3	Acoustic metamaterials
It has been discovered that there are many challenges in how sound waves travel, are absorbed, or reflected in traditional acoustic materials. These materials do not perform well when dealing with low-frequency sounds, and are unable to effectively absorb or reflect low-frequency noise, resulting in their inadequate noise control capabilities in the low-frequency range. For example, when the incident frequency is too different from the natural frequency of the resonator, the vibration amplitude of the air column is very small, and the sound absorption and noise reduction effect is poor (Liang et al., 2022). 
In addition, there is another problem faced by traditional acoustic materials, which is that the speed of sound is positive. When sound waves travel from one medium to another, some of the sound waves are reflected, refracted or transparent. According to Snell's law (Li et al., 2023), sound waves are refracted at the interface of two mediums, and this refraction follows a specific law. Therefore, the materials present in nature all have a positive velocity of sound, and they are not able to bend sound waves efficiently. 
To overcome these limitations of natural materials, scientists have devised a new type of material, the metamaterial. The unique properties of such materials are not derived from their chemical composition or fundamental physical properties, but from the precise geometry of their microstructure. These finely arranged geometries or shapes, also known as macrostructures, give metamaterials special engineering properties. They often exist in the form of large synthetic structures with three-dimensional cellular structures that exhibit optimized properties not commonly found in nature. Aydın & San (2024) explored this innovative material design concept opens up new possibilities for the realization of special functions that cannot be offered by conventional materials. 

1.4	Metamaterial Spherical Focused Acoustic Sensors (MBAS)
Ma et al. (2022) argued that MBAS is an integrated acoustic metamaterial spherical shell with an equidistant defective piezoelectric chamber capable of tracking sound sources in an omnidirectional range while achieving low self-noise and high sensitivity. By integrating piezoelectric acoustic sensing in each chamber, MBAS realizes the focusing and amplification of sound waves, thereby improving the efficiency of sound pressure and vibration energy capture.
The conceptual scheme and structural design of MBAS are shown in Fig.1, which is mainly a three-dimensional spherical structure approximated by a dodecahedron. Each face is a planar acoustic metamaterial sheet carrying a piezoelectric sheet at the central defect for the confinement and transduction of vibrational energy. There are 11 faces for acoustic sensing and the last one is left empty for installation (Ma et al., 2022). 
The design of MBAS is based on planar local resonance acoustic metamaterials, which achieve omnidirectional beamforming through uniform distribution in the defect chamber at the center of each sheet. This design allows MBAS to achieve excellent signal-to-noise ratio (72 dB) and ultra-high sensitivity (137 mVpp/Pa or -26.3 dBV) without relying on external signal amplification or complex signal processing systems.
 
Fig. 1. Overall concept and mechanism of the MBAS.
(A) Schematic illustration of the design of the MBAS with a zoom-in of its pentagon-shaped supercell, together with its promising applications as a voice-based HMI for an intelligent service robot. (B) The acoustic frequency–wave number dispersive relation of one supercell, showing a pseudo-bandgap (gray shading area) embedding a defect vibrational mode (in blue), with the inset showing the first Brillouin zone for a planar metamaterial. Vibrational mode profiles are shown for the defect mode (out-of-plane vibration, z directional) and the two fast in-plane modes (x or y directional) inside the pseudo-bandgap. (C) Comparison of the simulated sound pressure distribution for three sensors, including a thin plate carrying a lead-zirconate-titanate (PZT) acoustic sensor, a spherical arrayed PZT sensor, and the MBAS, under 1-Pa plane-wave sound incident from the top.


2. Implications for the study


2.1 Theoretical implications
2.1.1 Innovations in acoustic sensors
The proposal of MBAS provides a new perspective for the design and theory of acoustic sensors. Traditional acoustic sensors, such as condenser and piezoelectric microphones, are often limited by low sensitivity and insufficient signal-to-noise ratio (SNR). By taking advantage of the properties of metamaterials, the MBAS realizes the focusing and amplification of sound waves, which significantly improves the sensitivity and SNR of the sensor. This innovation not only promotes the development of acoustic sensor technology, but also provides a new theoretical basis for research in the field of acoustics. 
2.1.2 New mode of human-computer interaction
The application of MBAS provides a new model for human-computer interaction. In traditional human-computer interaction, speech recognition often relies on complex signal processing algorithms and a large number of computing resources (Sun et al., 2024). The high sensitivity and wide-band responsiveness of MBAS enable it to accurately capture and identify speech signals without relying on complex algorithms, thereby simplifying the implementation process of voice interaction and providing the possibility for the development of more natural and intuitive human-computer interaction interfaces. 
2.1.3. New methods of speech signal processing
The proposal of MBAS provides a new method for speech signal processing. In noisy environments, traditional acoustic sensors often struggle to accurately capture and recognize voice signals. Through its unique acoustic wave confinement ability, MBAS can track sound sources in the omnidirectional range and realize precise positioning and spatial filtering of sound sources, which is of great significance for improving the accuracy and efficiency of speech signal processing.

2.2 Practical Significance
2.2.1 Voice interaction of smart devices
In the voice interaction of smart devices, the application of MBAS can significantly improve the user experience. For example, in smart homes, smart assistants, and wearables (Uchida et al., 2021), MBAS can achieve fast response and accurate recognition of user voice commands, and maintain good performance even in environments with high background noise. This not only improves the practicability of the device, but also brings a more convenient and comfortable interactive experience to users. 
2.2.2 Conference and collaboration systems
In conferencing and collaboration systems, MBAS can be used as a core component to accurately capture and recognize the voice of conference participants. Through its omnidirectional beamforming capability, MBAS is able to distinguish between the position and voice of different speakers, enabling efficient recording and analysis of meeting content. This is of great value for improving meeting efficiency and optimizing the remote collaboration experience.     
In order to visualize the application of MBAS in voice interaction of smart devices, a simple diagram (Fig.2) was created to show the performance of MBAS at different background noise levels, and the speech recognition accuracy of MBAS and traditional microphones under different signal-to-noise ratio (SNR) levels was compared (Ma et al., 2022). 
 
Fig. 2. Speech recognition accuracy of MBAS and traditional microphones at different signal-to-noise ratio (SNR) levels

2.2.3 Safety and rescue operations
In the field of safety and rescue, the application of MBAS can improve rescue efficiency and safety. For example, in a chemical plant or disaster site, MBAS can be mounted on a robot or drone to detect distress signals from trapped people or malfunction alarms from equipment. MBAS's high sensitivity and wide-band response capability enable it to accurately capture key acoustic signals in complex environments, providing important information to rescuers and improving the success rate of rescues (Ma et al., 2022). Fig.3 shows how it works. 
 
Fig. 3. Applications of the MBAS in factory exploring and rescuing systems.
(A) Schematic diagram of the MBAS-based factory exploring and rescuing robots. Speech recognition and sound source location can help with equipment failures or rescue in dangerous environments. (B) Experiment setup in a factory for multiple sound sources generating sound waves simultaneously at various incident angles. Background noises consist of environment noise, air compressor noise, and alarm. “WARNING!” is fixed (facing plane no. 8), and “HELP!” is moving from plane no. 9 to plane no. 4. (C) The voltage comparison between environment noise, air compressor noise, and the human voice. (D) The normalized plane-frequency maps to distinguish simultaneous “WARNING!” (plane no. 8) and “HELP! (planes no. 9, no. 6, and no. 4).

2.2.4 Health monitoring and medical equipment
In the field of health monitoring and medical devices, the application of MBAS can provide patients with more accurate medical services. For example, MBAS combined with wearable sensors such as EcMG, Figure 4 illustrates an overview of a fully integrated wearable single-sensor EcMG system, which combines intensity-controlled ultrasound waves and collects RF signals carrying information from deep tissues to make them more stable (Gao et al., 2024). 
It can be used to monitor patients' breathing patterns and heart rate changes, providing doctors with real-time physiological data. Its high sensitivity and low noise make MBAS able to accurately capture weak physiological signals without interfering with patients' rest, providing an important basis for the diagnosis and treatment of diseases.
 
Fig. 4. Overview of the fully integrated wearable single-transducer EcMG system.
a, Schematic of the exploded view of the system. The top panel shows the whole device structure, including a compact single transducer, fPCB and battery, all encapsulated in a silicone elastomer. The bottom-left insets show the single transducers designed for different applications. The bottom-right inset shows the exploded view of the single transducer. b, Image of the system attached to the chest for diaphragm monitoring. c, Images of the system attached to the forearm for hand gesture tracking. The serpentine connector can isolate the motion between the single transducer and fPCB. d, Working principle of the system. The system has a soft interface with the skin. It can capture and precondition ultrasound waves reflected by deep muscles and wirelessly transmit the data to an external station for post-processing. The single-transducer RF signal is analysed to extract the respiration status directly or dynamic hand gesture through deep learning. VIA, vertical interconnect access.

2.2.5 Education and training
In the field of education and training, MBAS can be used as a teaching aid to help students better understand and master knowledge. With MBAS, teachers can capture students' speech in real-time, perform speech recognition and analysis, and thus understand students' learning progress and comprehension. In addition, MBAS can also be used to simulate a real language environment to help students improve their language learning skills.

2.3 Conclusion
As a new type of acoustic sensor, MBAS shows great potential and value in advanced human-computer voice interaction. Its theoretical significance is to promote the development of acoustic sensor technology and provide new modes and methods for human-computer interaction. The practical implications are to enhance the voice interaction experience of smart devices, optimize conferencing and collaboration systems, improve the efficiency of security and rescue operations, improve the performance of health monitoring and medical devices, and promote the effectiveness of education and training. With the further development and application of technology, MBAS is expected to play a more important role in the field of human-computer interaction in the future.






















3. Development status of the combination of metamaterials and voice interaction at home and abroad


Metamaterials are man-made materials whose unique microstructure allows them to manipulate the properties of electromagnetic waves, sound waves, and other waves, revealing physical phenomena that have never been seen in nature. In recent years, with the advancement of nanotechnology and materials science, the research and application of metamaterials have been extended to the field of acoustics, which have attracted more and more attention from the research community due to their extraordinary acoustic properties, and have become one of the important research methods for manipulating sound waves (Xiao et al.,2024). In particular, it shows great potential in voice interaction technology. Yang, & Chen (2009) discussed that as a natural and intuitive way of communication, the development of voice interaction has attracted great attention from researchers and industry around the world. Since the literature on MBS is still relatively rare, many other metamaterials have been studied in large numbers and are also of reference significance. Therefore, in addition to the metamaterial spherical focused acoustic sensors (MBAS) based on acoustic wave constraints, how other types of metamaterials can be studied and applied in the fields of voice interaction and acoustics will be explored below. 

3.1 Domestic development status
3.1.1 Negative mass density and acoustic wave propagation
By constructing acoustic metamaterials with negative mass density, the anomalous behavior of sound wave propagation can be realized. The material exhibits a negative mass density and a negative refractive index (1500-5480 Hz) in a specific frequency range (1560-5580 Hz). This characteristic is of great significance for the design of new acoustic devices and voice interaction systems, because it can be used to control the propagation direction and frequency of sound waves, thereby improving the quality of voice signals and the accuracy of interaction (Liu et al., 2017). 
The acoustic metamaterials prepared in this study exhibit an anomalous Doppler effect, that is, when the sound source is close to the detector, the frequency detected by the detector decreases. And when the sound source is far away from the detector, the frequency detected by the detector increases. Liu et al. (2017) state that this is in contrast to the Doppler effect observed in conventional materials. In voice interaction systems, this anomalous Doppler effect can be used to develop more accurate sound source localization and tracking techniques to improve the accuracy and robustness of speech recognition. 
 3.1.2 Design metamaterials for acoustically penetrating stealth cloaks
In voice interaction, the accurate transmission and reception of sound waves is the basis for efficient speech recognition and communication. By using acoustic metamaterials, systems can be designed to optimize sound wave transmission and reduce noise interference, thereby improving the clarity and recognition of speech signals.
The two-way acoustic penetrating stealth cloak design can make the sound wave bypass the specific target, effectively solving the problem of scattered stealth of the target. In the field of voice interaction, this technology can be used to create quiet, intimate spaces or to enhance the signal of a specific sound source, such as improving the clarity of voice communication in noisy environments (Luyun & Xichun, 2024).
 3.1.3 Research on membrane-based acoustic metamaterials (MAMs)
Cao & Huang (2024) found that MAMs have excellent low- and mid-frequency acoustic performance, which is critical for voice interaction systems that need to efficiently process and transmit voice signals. By optimizing the design of MAMs, the clarity and quality of voice signals can be improved and the interference of background noise can be reduced. 
Effect of array density on noise reduction performance. In voice interaction systems, array design can help orient sound capture and transmission, improve the accuracy of speech recognition and the clarity of voice communication. Fig5 shows the MAM STL simulation results for the three array methods. As a result, different array methods and the design of arrays have different influences. The MAMs design and array analysis not only broadened the operating bandwidth of acoustic isolation, but also kept the structure lightweight. In voice interaction devices, such as smart assistants, conferencing systems, and hearing aids, lightweight and efficient acoustic metamaterials can provide better sound control and noise reduction. 
The array design and performance of membrane-based acoustic metamaterials are studied, which provides valuable theoretical and experimental data for the development of voice interaction technology. By optimizing the design and array configuration of acoustic metamaterials, the performance of voice interaction systems can be significantly improved, especially in speech recognition and sound transmission quality in noisy environments (Cao & Huang, 2024). 
 
Fig. 5. Overview of the fully integrated wearable single-transducer EcMG system.
STL simulation results of MAM for three array approaches.

3.2 Foreign development trends
Foreign researchers are exploring innovative solutions for acoustic metamaterials in low-frequency noise reduction. These materials break through the limitations of traditional porous materials in low-frequency noise absorption and complex structure applications, and achieve efficient sound absorption in different frequency ranges through the research of folded superporous materials, biporous metamaterials, hybrid acoustic metamaterials, and acoustic black holes. These advancements not only enhance sound absorption at low and wideband frequencies, but are also expected to play an important role in creating quieter, more livable urban environments. Through these studies, the application prospects of acoustic metamaterials in the field of acoustics and voice interaction are particularly broad, as they can provide sound manipulation capabilities beyond the limitations of traditional materials, and provide new ideas for designing more efficient sound absorption and noise control solutions (Rastegar et al., 2023). 

3.3 Differences in development at home and abroad
The research in the field of acoustic metamaterials at home and abroad has shown an active scientific research atmosphere and rapid progress, but there are some differences. Huang et al. (2024) state that domestic research tends to focus more on the basic properties, manufacturing processes, and practicality of acoustic metamaterials in practical applications such as building acoustics and noise control, while foreign research tends to explore the theoretical innovation, design principles, and applications of these materials in high-end technical fields such as aerospace and medical imaging (Terao, 2023). In terms of research methods, China may adopt more methods combining experimental and numerical simulation, and pay attention to actual performance testing and optimization. Abroad, more emphasis may be placed on the integrated approach of theoretical analysis, computational simulation and experimental verification. In terms of industrial application, the domestic industry pays more attention to meeting the needs of the domestic market, while the foreign industry focuses more on the expansion of the global market and the application of high-end technology. With the advancement of globalization and the strengthening of scientific research cooperation, these differences are gradually narrowing, and the research characteristics and advantages of different countries and regions are constantly changing and developing. 














4. Design Principles and Evaluation of Metamaterial Acoustic Sensors MBS Combined with Voice Interaction


4.1 Design Principles
4.1.1 Acoustic Performance Optimization
Acoustic performance is fundamental to the performance of MBS in voice interaction, so its optimization is a top priority in the design process.
The design of MBS needs to take advantage of the properties of acoustic metamaterials to limit the propagation of sound waves in a specific frequency range. This can be achieved by designing specific structures, such as periodic resonance cavities or defect patterns. Acoustic wave limiting not only increases the strength of the sound source signal, but also reduces the interference of environmental noise, thereby improving the signal-to-noise ratio of the system.
Beamforming is a powerful filtering technique that enables spatial discrimination and elimination of energy from interfering sources by combining the channels of a microphone array. Several methods of combining signals have been proposed, the simplest and most well-known of which is delay and summation beamforming, which, as the name suggests, applies a time delay to the channel to ensure that the signals from the desired direction of the target signal arrive in phase and enhance each other (Luzanto et al.,2024). It enables the MBS to receive sound from a specific direction, which is essential for source localization and sound filtering. Consideration needs to be given to how effective beamforming can be achieved through physical structures or signal processing algorithms. For example, the beamforming can be controlled by adjusting the relative position of the microphones on the MBS, or by employing advanced digital signal processing techniques. 
The sensitivity of an MBS determines its ability to capture weak sounds, while the signal-to-noise ratio directly affects the clarity and intelligibility of sounds. Appropriate materials and structures need to be selected to maximize the acoustic piezoelectric effect while reducing noise from internal friction and air movement.
4.1.2 Selection of structure and materials
The physical structure and materials of MBS have a direct impact on its properties.
Mathew et al. (2021) found that wearable acoustic sensors for speech recognition are typically composed of solid materials such as piezoelectric materials. Considering that MBS is a solid metamaterial that may be integrated into portable or wearable devices, it is necessary to install it in a lightweight and high-strength material at the time of design (Yan et al., 2022). This ensures that the device is both portable and durable, and will not be damaged by accidental drops or impacts. Under changing environmental conditions, MBS integrated product materials need to have a certain degree of water and dust resistance to ensure their reliability and durability. This can be achieved by sealing the design or using waterproof and dustproof materials. 
4.1.3 Electronic integration and signal processing
Electronic integration and signal processing are key to the realization of voice interaction functions in MBS.
The first is compatibility, MBS needs to be compatible with existing electronics and signal processing systems so that it can be easily integrated into a wide range of applications. The design needs to take into account parameters such as interface standards and operating voltages of different devices. Secondly, real-time processing also needs to be considered. In order to enable real-time voice interaction, MBS needs to integrate hardware and software that can handle high-speed data streams. This may include high-speed analog-to-digital converters (ADCs), digital signal processors (DSPs), or field-programmable gate arrays (FPGAs) (Han et al.,2024; Pei et al., 2023; Jahanirad, 2023). 
4.1.4 User Experience
Zhao (2022) discussed that user experience is an important indicator to measure the success of MBS and voice interaction. 
In terms of ease of use, the installation and use process of MBS-related voice interaction products should be simple and intuitive, and users can quickly get started without complex training (Lewis & Sauro, 2024). This can be achieved by designing an intuitive user interface and providing a clear user manual. 
In terms of user experience, in order to provide a better user experience, MBS-related voice interaction products can design feedback mechanisms, such as LED indicators to show the working status, or sound prompts to confirm the success of the operation (Lewis & Sauro, 2024). In this way, the user can intuitively understand how the device is working. 

4.2. Assessment methods
4.2.1 Acoustic performance evaluation
Acoustic performance is one of the most important performance indicators of MBS. This mainly involves frequency loudness testing and beamforming effect evaluation. 
The frequency response test uses a spectrum analyzer to measure the acoustic pressure response of the MBS at different frequencies. This requires accurate spectrum analyzers and standardized test procedures. Tez & Kaya (2023) argued that finite element method (FEM) simulation studies can be used to analyze its frequency response. Beamforming effect evaluation is to evaluate the beamforming capability of MBS by changing the position of the sound source. This requires precise position control and sound pressure measurement technology (Luzanto et al., 2024). 
4.2.2 Sensitivity and signal-to-noise ratio evaluation
Sensitivity and signal-to-noise ratio are key metrics to measure the performance of MBS.
The sensitivity test is to measure the output voltage of the MBS at different sound pressure levels and calculate its sensitivity. This requires precise sound pressure control and voltage measurement technology. Broadly speaking, it refers to the evaluation of the response or sensitivity of an acoustic model to changes in input parameters (Pilch, 2023). The MBS sensor can be tested for sensitivity consistency using a test platform, a pulse excitation signal is modulated by a signal generator, input to the emission sensor to generate sound waves, and the sensor under test obtains the acoustic signal, which is then transmitted to the instrument for analysis and processing through a signal cable (Zhang et al., 2022). 
Bhandari et al. (2019) discussed that the signal-to-noise ratio (SNR) is one of the most basic metrics used in signal processing. SNR is defined as the ratio of signal power to noise power. In phasor measurements, the average value of the signal represents the signal power, and the standard deviation of the noise represents the noise power. The signal-to-noise ratio test is to measure the output signal of the MBS in the presence of background noise and calculate its signal-to-noise ratio. This requires controlling the sound level of the background noise and accurately measuring the output signal of the MBS. 
4.2.3 Speech Recognition Assessment
Speech recognition is one of the core functions of MBS.
The recognition accuracy test uses standard speech samples to evaluate the recognition accuracy of MBS combined with speech recognition algorithms. This requires high-quality recording equipment and standard voice samples. The response time test is a measure of the response time of the MBS system to voice commands. This requires precise time measurement technology and real-time data processing capabilities. In addition, the following five metrics were used to evaluate the model: Signal Distortion Ratio (SDR), Short-Term Target Comprehensibility (STOI), Quality Perception Assessment (PESQ), Word Error Rate (WER), and Output Signal-to-Noise Ratio (SNR) (Vincent et al., 2006; Taal et al., 2011). SDR, STOI, and PESQ are all standard metrics that are commonly used to evaluate speech enhancement systems (Luzanto, 2024). To also consider the user experience, a user experience (UX) questionnaire for voice interaction can be constructed using the UEQ+ (an extension of the User Experience Questionnaire) framework, which has three scales for specific UX aspects of voice interaction, namely response behavior, response quality, and comprehension. These scales are designed to measure the quality of user experience with speech system interactions (Klein, 2020). 


























5. Literature Review


In this paper, the application prospect of spherical focused acoustic sensors (MBAS) based on metamaterials in human-computer voice interaction is reviewed, and the combination of metamaterials and acoustics is discussed. The article first emphasizes the importance of human-computer interaction (HMI) and points out that voice interaction has attracted much attention because of its naturalness and convenience. However, achieving efficient and natural voice interaction requires acoustic sensors with high sensitivity, high signal-to-noise ratio (SNR), and low noise interference, while traditional acoustic sensors have problems such as low sensitivity, high power consumption, or complex signal processing systems.
This paper innovatively proposes the application of MBAS in voice interaction, a new type of acoustic sensor that uses the characteristics of metamaterials to realize the focusing and amplification of sound waves, and significantly improves the sensitivity and SNR of the sensor. The design of MBAS is based on planar local resonance acoustic metamaterials that achieve omnidirectional beamforming through uniform distribution in the defect chamber at the center of each plate, resulting in excellent signal-to-noise ratio and ultra-high sensitivity without relying on external signal amplification or complex signal processing systems.
This paper theoretically provides a new perspective for the design and theory of acoustic sensors, promotes the development of acoustic sensor technology, and provides new modes and methods for human-computer interaction. In practice, MBAS has a wide range of applications, from voice interaction in smart devices, to security and rescue operations, to health monitoring and medical devices, as well as in education and training, showing its potential to enhance the user experience and improve operational efficiency. However, despite the great potential of MBAS, there are still technical challenges in practical applications, such as how to maintain stable performance under changing environmental conditions and how to be compatible with existing electronic devices and signal processing systems. 
This paper emphasizes the importance of user experience and proposes a design feedback mechanism to improve user interaction experience, which shows that designers pay attention to the actual feelings of users while developing technology. The paper also compares the differences between domestic and foreign research in the field of acoustic metamaterials, and points out that domestic research focuses more on practical applications, while foreign research is more inclined to theoretical innovation and application in the field of high-end technology.
Overall, this article provides a comprehensive review of the application of metamaterials in the field of acoustic sensors, and points out their potential value and challenges in human-machine voice interaction. With the further development of technology, MBAS is expected to play a more important role in the field of human-computer interaction in the future.