Toothbrushing plays an important role in daily dental plaque removal for preventive dentistry. Prior work has investigated improvements on toothbrushing with sensing technologies. But existing toothbrushing support focuses mostly on estimating brushing coverage. Users thus only have indirect information about how well their toothbrushing removes dental plaque. We present LumiO, a toothbrush that offers users continuous feedback on the amount of plaque on teeth. LumiO uses a well-known method for plaque detection, called Quantitative Light-induced Fluorescence (QLF). QLF exploits a red fluorescence property that bacterium in the plaque demonstrates when a blue-violet ray is cast. Blue-violet light excites this fluorescence property, and a camera with an optical filter can capture plaque in pink. We incorporate this technology into an electric toothbrush to achieve improvements in performance on plaque removal in daily dental care. This paper first discusses related work in sensing for oral activities and interaction as well as dental care with technologies. We then describe the principles of QLF, the hardware design of LumiO, and our vision-based plaque detection method. Our evaluations show that the vision-based plaque detection method with three machine learning techniques can achieve F-measures of 0.68 – 0.92 under user-dependent training. Qualitative evidence also suggests that study participants were able to have improved awareness of plaque and build confidence on their toothbrushing.


T. Yoshitani, M. Ogata, and K. Yatani. LumiO: A Plaque-aware Toothbrush. in Proceedings of UbiComp 2016, 2016.
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