The human voice stands out for its rich information transmission capabilities. However, voice communication is susceptible to interference from noisy environments and obstacles. Here, we propose a wearable wireless flexible skin-attached acoustic sensor (SAAS) capable of capturing the vibrations of vocal organs and skin movements, thereby enabling voice recognition and human-machine interaction (HMI) in harsh acoustic environments. This system utilizes a piezoelectric micromachined ultrasonic transducers (PMUT), which feature high sensitivity (-198 dB), wide bandwidth (10 Hz-20 kHz), and excellent flatness (±0.5 dB). Flexible packaging enhances comfort and adaptability during wear, while integration with the Residual Network (ResNet) architecture significantly improves the classification of laryngeal speech features, achieving an accuracy exceeding 96%. Furthermore, we also demonstrated SAAS’s data collection and intelligent classification capabilities in multiple HMI scenarios. Finally, the speech recognition system was able to recognize everyday sentences spoken by participants with an accuracy of 99.8% through a deep learning model. With advantages including a simple fabrication process, stable performance, easy integration, and low cost, SAAS presents a compelling solution for applications in voice control, HMI, and wearable electronics.

Sound signals not only serve as the primary communication medium but also find application in fields such as medical diagnosis and fault detection. With public healthcare resources increasingly under pressure, and challenges faced by disabled individuals on a daily basis, solutions that facilitate low‐cost private healthcare hold considerable promise. Acoustic methods have been widely studied because of their lower technical complexity compared to other medical solutions, as well as the high safety threshold of the human body to acoustic energy. Furthermore, with the recent development of artificial intelligence technology applied to speech recognition, speech recognition devices, and systems capable of assisting disabled individuals in interacting with scenes are constantly being updated. This review meticulously summarizes the sensing mechanisms, materials, structural design, and multidisciplinary applications of wearable acoustic devices applied to human health and human–computer interaction. Further, the advantages and disadvantages of the different approaches used in flexible acoustic devices in various fields are examined. Finally, the current challenges and a roadmap for future research are analyzed based on existing research progress to achieve more comprehensive and personalized healthcare.

This review traces the evolution from classical to integrative plasmonics, detailing the optical multi-effects of plasmons and their acousto-optic-electric-thermal fusion. It discusses key integration mechanisms, applications, and future prospects.

Infrared absorption spectroscopy is an effective tool for the detection and identification of molecules. However, its application is limited by the low infrared absorption cross-section of the molecule, resulting in low sensitivity and a poor signal-to-noise ratio. Surface-Enhanced Infrared Absorption (SEIRA) spectroscopy is a breakthrough technique that exploits the field-enhancing properties of periodic nanostructures to amplify the vibrational signals of trace molecules. The fascinating properties of SEIRA technology have aroused great interest, driving diverse sensing applications. In this review, we first discuss three ways for SEIRA performance optimization, including material selection, sensitivity enhancement, and bandwidth improvement. Subsequently, we discuss the potential applications of SEIRA technology in fields such as biomedicine and environmental monitoring. In recent years, we have ushered in a new era characterized by the Internet of Things, sensor networks, and wearable devices. These new demands spurred the pursuit of miniaturized and consolidated infrared spectroscopy systems and chips. In addition, the rise of machine learning has injected new vitality into SEIRA, bringing smart device design and data analysis to the foreground. The final section of this review explores the anticipated trajectory that SEIRA technology might take, highlighting future trends and possibilities.

Surface plasmons have proven their ability to boost the sensitivity of mid-infrared hyperspectral imaging by enhancing light-matter interactions. Surface phonons, a counterpart technology to plasmons, present unclear contributions to hyperspectral imaging. Here, we investigate this by developing a plasmon-phonon hyperspectral imaging system that uses asymmetric cross-shaped nanoantennas composed of stacked plasmon-phonon materials. The phonon modes within this system, controlled by light polarization, capture molecular refractive index intensity and lineshape features, distinct from those observed with plasmons, enabling more precise and sensitive molecule identification. In a deep learning–assisted imaging demonstration of severe acute respiratory syndrome coronavirus (SARS-CoV), phonons exhibit enhanced identification capabilities (230,400 spectra/s), facilitating the de-overlapping and observation of the spatial distribution of two mixed SARS-CoV spike proteins. In addition, the plasmon-phonon system demonstrates increased identification accuracy (93%), heightened sensitivity, and enhanced detection limits (down to molecule monolayers). These findings extend phonon polaritonics to hyperspectral imaging, promising applications in imaging-guided molecule screening and pharmaceutical analysis.

Optical sensors have undergone significant evolution, transitioning from discrete optical microsystems toward sophisticated photonic integrated circuits (PICs) that leverage artificial intelligence (AI) for enhanced functionality. This review systematically explores the integration of optical sensing technologies with AI, charting the advancement from conventional optical microsystems to AI-driven smart devices. First, we examine classical optical sensing methodologies, including refractive index sensing, surface-enhanced infrared absorption (SEIRA), surface-enhanced Raman spectroscopy (SERS), surface plasmon-enhanced chiral spectroscopy, and surface-enhanced fluorescence (SEF) spectroscopy, highlighting their principles, capabilities, and limitations. Subsequently, we analyze the architecture of PIC-based sensing platforms, emphasizing their miniaturization, scalability, and real-time detection performance. This review then introduces the emerging paradigm of in-sensor computing, where AI algorithms are integrated directly within photonic devices, enabling real-time data processing, decision making, and enhanced system autonomy. Finally, we offer a comprehensive outlook on current technological challenges and future research directions, addressing integration complexity, material compatibility, and data processing bottlenecks. This review provides timely insights into the transformative potential of AI-enhanced PIC sensors, setting the stage for future innovations in autonomous, intelligent sensing applications.