Method for Estimating Amount of Saliva Secreted Using a Throat Microphone
Kai Washino, Ayumi Ohnishi, Tsutomu Terada, Masahiko Tsukamoto
TL;DR
This paper introduces a wearable throat microphone method to estimate saliva secretion using sound and deep learning.
Contribution
A novel method using a throat microphone and deep learning to estimate saliva secretion in real-time.
Findings
The proposed method achieved 96.96% accuracy in classifying swallowing sounds.
The correlation coefficient (R) for saliva estimation was 0.600 with a mean absolute error (MAE) of 0.0487.
Abstract
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.
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Taxonomy
TopicsSalivary Gland Disorders and Functions · Advanced Chemical Sensor Technologies · Traditional Chinese Medicine Studies