Flexible and skin-attachable vibration sensors have been studied for use as wearable voice-recognition electronics. However, the development of vibration sensors to recognize the human voice accurately with a flat frequency response, a high sensitivity, and a flexible/conformable form factor has proved a major challenge. Here, we present an ultrathin, conformable, and vibration-responsive electronic skin that detects skin acceleration, which is highly and linearly correlated with voice pressure. This device consists of a crosslinked ultrathin polymer film and a hole-patterned diaphragm structure, and senses voices quantitatively with an outstanding sensitivity of 5.5 V Pa⁻¹ over the voice frequency range. Moreover, this ultrathin device (<5 μm) exhibits superior skin conformity, which enables exact voice recognition because it eliminates vibrational distortion on rough and curved skin surfaces. Our device is suitable for several promising voice-recognition applications, such as security authentication, remote control systems and vocal healthcare.

Flexible materials with sufficient mechanical endurance under bending or folding is essential for flexible electronic devices. Conventional rigid materials such as metals and ceramics are mostly brittle so that their properties can deteriorate under a certain amount of strain. In order to utilize high-performance, but brittle conventional materials in flexible electronics, we propose a novel flexible substrate structure with a low-modulus interlayer. The low-modulus interlayer reduces the surface strain, where active electronic components are placed. The bending results with indium tin oxide (ITO) show that a critical bending radius, where the conductivity starts to deteriorate, can be reduced by more than 80% by utilizing the low-modulus layer. We demonstrate that even rigid electrodes can be used in flexible devices by manipulating the structure of flexible substrate.

Dysphagia, a swallowing disorder, requires continuous monitoring of throat-related events to obtain comprehensive insights into the patient’s pharyngeal and laryngeal functions. However, conventional assessments were performed by medical professionals in clinical settings, limiting persistent monitoring. We demonstrate feasibility of a ubiquitous monitoring system for autonomously detecting throat-related events utilizing a soft skin-attachable throat vibration sensor (STVS). The STVS accurately records throat vibrations without interference from surrounding noise, enabling measurement of subtle sounds such as swallowing. Out of the continuous data stream, we automatically classify events of interest using an ensemble-based deep learning model. The proposed model integrates multiple deep neural networks based on multi-modal acoustic features of throat-related events to enhance robustness and accuracy of classification. The performance of our model outperforms previous studies with a classification accuracy of 95.96%. These results show the potential of wearable solutions for improving dysphagia management and patient outcomes outside of clinical environments.