Using HMM Based Audio Recognition and SVM Regression to Evaluate Toothbrushing Performance

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 tooth brushing 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.