Washino Kai, Ohnishi Ayumi, Terada Tsutomu, Tsukamoto Masahiko
Graduate School of Engineering, Kobe University, 1-1 Rokkodaicho, Nada, Kobe 657-8501, Japan.
Sensors (Basel). 2025 Jun 6;25(12):3584. doi: 10.3390/s25123584.
Saliva is an important secretion, and a continued insufficient amount of saliva secreted causes glossitis, stomatitis, and so on. Since the amount of saliva secreted changes daily, adverse effects occur daily. Therefore, it is necessary to constantly measure the amount of saliva secreted and take appropriate measures when it decreases. However, there is no method to constantly measure saliva. We propose a method to estimate the amount of saliva secreted from the sound acquired by a wearable throat microphone. The proposed method uses deep learning to classify whether the sound acquired by the throat microphone is swallowing or not. Based on the swallowing information, the proposed method estimates the amount of saliva secreted. The accuracy of the classification of swallowing was 96.96%. For the estimation of the amount of saliva secreted, the R was 0.600 and MAE was 0.0487.
唾液是一种重要的分泌物,持续分泌量不足会导致舌炎、口腔炎等。由于唾液分泌量每天都在变化,因此每天都会产生不良影响。所以,有必要持续测量唾液分泌量,并在其减少时采取适当措施。然而,目前尚无持续测量唾液的方法。我们提出了一种通过可穿戴式喉部麦克风获取的声音来估计唾液分泌量的方法。该方法利用深度学习对喉部麦克风获取的声音是否为吞咽进行分类。基于吞咽信息,该方法估计唾液分泌量。吞咽分类的准确率为96.96%。对于唾液分泌量的估计,R值为0.600,平均绝对误差为0.0487。