Tooth brushing monitors have the potential to enhance oral hygiene and encourage the development of healthy brushing habits. However, previous studies fall short of recognizing each tooth due to limitations in external sensors and variations among users. To address these challenges, we present ToothFairy, a real-time tooth-by-tooth brushing monitor that uses earphone reverse signals captured within the oral cavity to identify each tooth during brushing. The key component of ToothFairy is a novel bone-conducted acoustic attenuation model, which quantifies sound propagation within the oral cavity. This model eliminates the need for machine learning and can be calibrated with just one second of brushing data for each tooth by a new user. ToothFairy also addresses practical issues such as brushing detection and tooth region determination. Results from extensive experiments, involving 10 volunteers and 25 combinations of five commercial off-the-shelf toothbrush and earphone models each, show that ToothFairy achieves tooth recognition with an average accuracy of 90.5%.

Yang Wang

Feng Hong

Yufei Jiang

Chenyu Bao

Chao Liu

Zhongwen Guo

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

ToothFairy

A real-time tooth-by-tooth brushing monitor that uses earphone reverse signals captured within the oral cavity to identify each tooth during brushing, using a novel bone-conducted acoustic attenuation model, which quantifies sound propagation within the oral cavity.

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ToothFairy

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Journal Info

Category	Quartile
TELECOMMUNICATIONS	2