Artificial dynamic tactile sensing requires sensitivity, robustness, and compliance, yet existing technologies face trade-offs when scaling to large-area arrays, compounded by wiring complexity and cost. Here, we report a passive distributed paradigm using deep sub-wavelength acoustic waveguides that decouples performance from structural flexibility. Elastic-membrane-capped Helmholtz resonators interconnected by spring-reinforced microtubes form an enclosed network with invariant acoustic transmission under macroscopic bending. By sparsely embedding microphones, the system achieves real-time localization (4 mm highest spatial resolution; >99% accuracy in a 4 microphones 64-node sensing array) and waveform reconstruction of low-frequency signals (<100 Hz). Fast Continuous Wavelet Transform and a lightweight neural network enable inference within 5.5 ms. We demonstrate conformable prototypes-fingertip arrays, a tactile glove, and large-area skins-detecting stimuli from single-hair contact to 5-mg particle impacts, arterial pulse waves, feather touches, and finger contact. This establishes a scalable, flexible, low-cost paradigm for next-generation human-machine interfaces.

翻译：人工动态触觉传感需要灵敏度、鲁棒性和柔顺性，但现有技术在大面积阵列扩展时面临权衡，布线复杂性和成本问题尤为突出。本文提出一种基于深亚波长声波导的被动分布式范式，将传感性能与结构柔性解耦。弹性膜覆盖的亥姆霍兹谐振器通过弹簧增强微管互连，形成封闭网络，在大尺度弯曲条件下保持声传输不变性。通过稀疏嵌入麦克风，该系统实现了低频信号（<100 Hz）的实时定位（4 mm最高空间分辨率；4麦克风64节点传感阵列中准确率>99%）和波形重建。快速连续小波变换与轻量级神经网络可在5.5 ms内完成推理。我们展示了共形原型——指尖阵列、触觉手套及大面积电子皮肤——可检测从单根毛发接触到5 mg颗粒冲击、动脉脉搏波、羽毛触碰和手指接触等刺激。这为下一代人机界面建立了一种可扩展、柔性、低成本的范式。