Park Jonghwa, Kang Dong-Hee, Chae Heeyoung, Ghosh Sujoy Kumar, Jeong Changyoon, Park Yoojeong, Cho Seungse, Lee Youngoh, Kim Jinyoung, Ko Yujung, Kim Jae Joon, Ko Hyunhyub
School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 689-798, Republic of Korea.
Department of Electrical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 689-798, Republic of Korea.
Sci Adv. 2022 Mar 25;8(12):eabj9220. doi: 10.1126/sciadv.abj9220.
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.
在无周围噪声干扰的情况下准确传输生物信号是实现人机接口（HMI）的关键因素。我们基于具有铁电复合材料分层大穹顶/微孔/纳米颗粒结构的摩擦电传感器，提出了用于双模式人机接口的频率选择性声学和触觉传感器。我们的传感器在广泛的动态压力和共振频率下表现出高灵敏度和线性度，这使得在宽频率范围（145至9000赫兹）内具有高声学频率选择性，从而使独立于噪声的语音识别成为可能。我们的频率选择性多通道声学传感器阵列与人工神经网络相结合，对100至8000赫兹范围内的不同频率噪声表现出超过95%的准确语音识别率。我们证明，我们的双模式传感器在广泛的动态压力下具有线性响应和频率选择性，有助于利用声学和机械输入区分表面纹理并控制虚拟机器人，而不受周围噪声的干扰。
