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The attempt of swallowing discrimination by heart rate variability using machine learning

The number of patients with lifestyle‐related diseases has increased because of irregular dietary life, which is attributed in part to social issues. For maintaining a regular dietary life, managing the dietary history and information is needed. The current method of maintaining rhythm in regular dietary life is cumbersome and has not generally diffused. This paper focuses on swallowing, and experiments evaluate the detection method of swallowing by longitudinal data in heart rate variability with a device capable of measuring biological information. As a result, the detection model for the convolutional neural network has an average accuracy of 83.2%.