Evaluating Tooth Brushing Performance with Smartphone Sound Data
Publication Title2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015)
Publication DateSeptember 9 2015
DescriptionThis paper presents a new method for evaluating tooth brushing performance using audio collected from a smartphone. To do this, we use hidden Markov models (HMMs) to recognize audio data that include various types of tooth brushing actions, such as brushing the inner surface of the back teeth. We then use the output of the HMMs to build regression models to estimate tooth brushing performance scores, such as stroke quality of brushing for the back inner teeth and duration of brushing for the front teeth. The scores used to train these regression models are obtained from a dentist who specializes in dental care instruction, with the resulting regression models estimating performance scores that closely correspond to the scores assigned by the dentist.
How to cite this entry
Josephe Korpela, Ryosuke Miyaji, Takuya Maekawa, Kazunori Nozaki, Hiroo Tamagawa. (September 9 2015). "Evaluating Tooth Brushing Performance with Smartphone Sound Data". 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015). International Joint Conference On Pervasive And Ubiquitous Computing. Fabric of Digital Life. http://fabricofdigitallife.com/index.php/Detail/objects/1412