Optical Sensing-Based Intelligent Toothbrushing Monitoring System

Kaixin Chen, Lei Wang, Yongzhi Huang, Kaishun Wu, Lu Wang

Abstract: Incorrect brushing methods normally lead to poor oral hygiene, and result in severe oral diseases and complications. While effective brushing can address this issue, individuals often struggle with incorrect brushing, like aggressive brushing, insufficient brushing, and missing brushing. To break this stalemate, in this paper, we proposed LiT, a toothbrushing monitoring system to assess the brushing status on 16 surfaces using the Bass technique. LiT utilizes commercial LED toothbrushes’ blue LEDs as transmitters, and incorporates only two low-cost photodetectors as receivers on the toothbrush head. It is challenging to determine optimal deployment positions and minimize photodetectors number to establish the light transmission channel in oral cavity. To address these challenges, we established mathematical models within the oral cavity based on the two photodetectors’ deployment to theoretically validate the feasibility and prove robustness. Furthermore, we designed a comprehensive framework to fight against the implementation challenges including brushing action separation, light interference on the outer surfaces of front teeth, toothpaste diversity, user variations, brushing hand variability, and incorrect brushings. Experimental results demonstrate that LiT achieves a highly accurate surface recognition rate of 95.3%, an estimated error for brushing duration of 6.1%, and incorrect brushing detection accuracy of 96.9%. Furthermore, LiT retains stable capability under a variety of circumstances, such as various lighting conditions, user movement, toothpaste diversity, and left and right-handed users.