Reliable emergency communication is crucial for protecting human lives in extreme environmental conditions. However, conventional microphone-based systems often fail in noisy, obstructed, or submerged environments. Here, we present a deep learning enabled adhesive-interface multimodal sensor (AIMS) for multilingual speech recognition and emergency communication. The AIMS exhibits a broad pressure detection range (0.1-400 kPa), exceptional mechanical endurance (>100,000 fatigue cycles), and broadband vibration responses up to 5 kHz. Critically, it resolves high-frequency vibrations under varying static pressures, decoupling pressure and vibration signals, while maintaining stable performance even under full immersion. By mounting AIMS at the throat, it can simultaneously capture muscle movements (pressure cues) and vocal cord oscillations (vibration signals), enabling accurate speech recognition in noisy or physically obstructed environments. Integrated with convolutional neural networks, the system achieves >98% accuracy in phoneme recognition, tonal analysis, similar-word discrimination, speaker identification, and real-time multilingual translation. Furthermore, we demonstrate a voice-activated rescue mockup simulating an elderly fall scenario, where integrated GPS enables automated distress alerts. This multimodal sensing strategy establishes a versatile platform for next-generation intelligent emergency communication, with transformative potential for disaster response and continuous medical monitoring.

Electromagnetic interference (EMI) shielding in high-frequency range, especially the rapidly growing terahertz (THz) frequency range, attracts increasing attention due to the potential application of terahertz in 6G wireless communication, and security inspection. However, traditional conductive EMI films typically achieve high shielding effectiveness through strong reflection, which may cause secondary pollution to other devices. Here, a gradient structure strategy was proposed to construct Ti₃C₂T_x/hydroxypropyl methyl cellulose (HPMC) film, in which the content of Ti₃C₂T_x gradually increases along the thickness direction, resulting in different conductivity of the two surfaces (surface-M and surface-H) for the film. The obtained gradient-film exhibits an EMI shielding efficiency of over 48.5 dB in the THz range (0.2–1.6 THz) at a thickness of 40 μm. Especially, as the THz waves incident from the surface-H to the film, the absorption effectiveness reaches 48.2 dB (average absorbed power loss up to 91.4%), and the reflection effectiveness is only 0.3 dB. In additions, the gradient-film also demonstrates a high absorption rate of 95.5% in the infrared band (2.5–16.7 μm). Moreover, the gradient-film exhibits an excellent tensile stress and Young’s modulus value of 173.1 MPa and 2.8 GPa, respectively. Therefore, the gradient-film proposed in this work, with excellent electromagnetic absorption in both THz and infrared band, provides a promising candidate for the next-generation EMI shielding applications.

Flexible and wearable sensors have broad application prospects in health monitoring and artificial intelligence. Many different single-functional sensing devices have been developed in recent years, such as pressure sensors and temperature sensors. However, it is still a great challenge to design and fabricate tactile sensors with multiple sensing functions. Herein, we propose a simple direct stamping method for the fabrication of multifunctional tactile sensors. It can detect pressure and temperature stimuli signals simultaneously. This pressure/temperature sensor possesses high sensitivity (0.67 kPa⁻¹), large linear range (0.75–5 kPa), and fast response speed (15.6 ms) in pressure sensing. It also has a high temperature sensitivity (1.41%/°C) and great linearity (0.99) for temperature sensing in the range of −30 to 30 °C. All these excellent performances indicate that this pressure/temperature sensor has great potential in applications for artificial intelligence and health monitoring.