Scalable Dynamic Tactile Sensing Enabled by Passive and Flexible Acoustic Waveguides

2026-06-11

AI Summary

This work addresses the longstanding challenge in large-area dynamic tactile sensing—namely, the difficulty in simultaneously achieving high sensitivity, robustness, and mechanical compliance, compounded by complex wiring and high costs. The authors propose a distributed sensing paradigm based on passive flexible acoustic waveguides, employing Helmholtz resonators encapsulated in elastic membranes and spring-reinforced microtubes to form a bending-invariant acoustic network. This architecture enables real-time localization and waveform reconstruction of low-frequency tactile signals using only sparsely deployed microphones. By decoupling sensor performance from structural flexibility, the approach achieves over 99% localization accuracy and 4 mm spatial resolution with just four microphones across a 64-node array, while maintaining a response latency below 5.5 ms. The system demonstrates high sensitivity across diverse platforms—including fingertip arrays, tactile gloves, and large-area electronic skins—capable of detecting subtle dynamic stimuli ranging from single-hair contact to impacts by 5 mg particles.

Abstract

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.

Problem

• tactile sensing
• scalability
• flexible electronics
• large-area arrays
• wiring complexity

Innovation

• acoustic waveguides
• dynamic tactile sensing
• Helmholtz resonators
• scalable flexible electronics
• sparse microphone array