Frequency-selective acoustic and haptic smart skin for dual-mode dynamic/static human-machine interface

Accurate transmission of biosignals without interference of surrounding noises is a key factor for the realization of human-machine interfaces (HMIs). We propose frequency-selective acoustic and haptic sensors for dual-mode HMIs based on triboelectric sensors with hierarchical macrodome/micropore/nanoparticle structure of ferroelectric composites. Our sensor shows a high sensitivity and linearity under a wide range of dynamic pressures and resonance frequency, which enables high acoustic frequency selectivity in a wide frequency range (145 to 9000 Hz), thus rendering noise-independent voice recognition possible. Our frequency-selective multichannel acoustic sensor array combined with an artificial neural network demonstrates over 95% accurate voice recognition for different frequency noises ranging from 100 to 8000 Hz. We demonstrate that our dual-mode sensor with linear response and frequency selectivity over a wide range of dynamic pressures facilitates the differentiation of surface texture and control of an avatar robot using both acoustic and mechanical inputs without interference from surrounding noise.

Automated summary

This research proposes frequency-selective acoustic and haptic sensors for dual-mode human-machine interfaces (HMIs) based on triboelectric sensors. The sensors demonstrate high sensitivity and linearity under a wide range of dynamic pressures and resonance frequency, enabling high acoustic frequency selectivity and noise-independent voice recognition. By utilizing a frequency-selective multichannel acoustic sensor array and an artificial neural network, over 95% accurate voice recognition is achieved for different frequency noises. The dual-mode sensors with linear response and frequency selectivity facilitate the differentiation of surface texture and control of an avatar robot using both acoustic and mechanical inputs without interference from surrounding noise.