Journal of Information Processing
Online ISSN : 1882-6652
ISSN-L : 1882-6652
Toothbrushing Performance Evaluation Using Smartphone Audio Based on Hybrid HMM-recognition/SVM-regression Model
Joseph KorpelaRyosuke MiyajiTakuya MaekawaKazunori NozakiHiroo Tamagawa
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2016 Volume 24 Issue 2 Pages 302-313

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Abstract

This paper presents a method for evaluating toothbrushing performance using audio data collected by a smartphone. This method first conducts activity recognition on the audio data to classify segments of the data into several classes based on the brushing location and type of brush stroke. These recognition results are then used to compute several independent variables which are used as input to an SVM regression model, with the dependent variables for the SVM model derived from evaluation scores assigned to each session of toothbrushing by a dentist who specializes in dental care instruction. Using this combination of audio-based activity recognition and SVM regression, our method is able to take smartphone audio data as input and output evaluation score estimates that closely correspond to the evaluation scores assigned by the dentist participating in our research.

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© 2016 by the Information Processing Society of Japan
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