| Title |
A Machine Learning Model for Toothbrush Position Tracking using a Low-cost 6-axis IMU Sensor |
| Authors |
권민욱(Minwook Kwon) ; 임대연(DaeYeon Lim) ; 김도윤(Doyoon Kim) ; 류승재(Seungjae Ryu) ; 조은아(Eunah Jo) ; 김영욱(Young Wook Kim) ; 김진현(Jin Hyun Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2024.73.2.358 |
| Keywords |
Machine learning; 6-axis IMU sensor; Classification; Data analysis; Quaternion |
| Abstract |
The recent epidemic of respiratory diseases has underscored the importance of personal oral health care. Oral diseases, primarily caused by viral infections, can be reduced by regularly eliminating oral microorganisms. Effective tooth brushing is fundamental to oral health, but changing established brushing habits can be challenging. Adherence to recommended brushing techniques is challenging across all age groups, including children, older people, and adults. This study uses data from a low-cost, 6-axis IMU sensor and a machine learning-based classification algorithm for 13 brushing positions. We evaluate eight machine learning models using the sensor’s acceleration and angular velocity data and assess their performance using various metrics. Our results show that these models can classify brush positions with approximately 89% accuracy. This method enables monitoring of brushing areas and analysis of brushing patterns to improve brushing quality and adherence to recommended techniques. Consequently, by improving brushing quality, it is possible to maintain primary personal oral care and prevent various diseases. |