A Machine Learning Model for Toothbrush Position Tracking using a Low-cost 6-axis IMU Sensor

被引：0

作者：

Kwon M. ^{[1
]}

Lim D. ^{[2
]}

Kim D. ^{[3
]}

Ryu S. ^{[4
]}

Jo E. ^{[4
]}

Kim Y.W. ^{[4
]}

Kim J.H. ^{[1
]}

机构：

[1] Dept. of AI Convergence Engineering(BK21), Gyeongsang National University

[2] Information & Communication Engineering, Gyeongsang National University

[3] Dept. of Intelligence and Communication Engineering, Gyeongsang National University

[4] PAIST (ProxiHealthcare Advanced Institute for Science and Technology), Seoul

来源：

Transactions of the Korean Institute of Electrical Engineers | 2024年 / 73卷 / 02期

基金：

新加坡国家研究基金会;

关键词：

6-axis IMU sensor; Classification; Data analysis; Machine learning; Quaternion;

D O I：

10.5370/KIEE.2024.73.2.358

中图分类号：

学科分类号：

摘要：

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. © 2024 Korean Institute of Electrical Engineers. All rights reserved.

引用

页码：358 / 367

页数：9

共 28 条

[21]

Song Y.-Y., Ying L., Decision tree methods: applications for classification and prediction, Shanghai archives of psy- chiatry, 27, 2, (2015)

[22]

Dreiseitl S., Ohno-Machado L., Logistic regression and artificial neural network classification models: a method- ology review, Journal of biomedical informatics, 35, 5-6, pp. 352-359, (2002)

[23]

Breiman L., Bagging predictors, Machine learning, 24, pp. 123-140, (1996)

[24]

Ferreira A. J., Figueiredo M. A., Boosting algorithms: A review of methods, theory, and applications, Ensemble ma- chine learning: Methods and applications, pp. 35-85, (2012)

[25]

Guryanov A., Histogram-based algorithm for building gradient boosting ensembles of piecewise linear decision trees, Analysis of Images, Social Networks and Texts: 8th Inter- national Conference, AIST 2019, pp. 39-50, (2019)

[26]

Dorogush A. V., Ershov V., Gulin A., Catboost: gradient boosting with categorical features support, (2018)

[27]

Pal M., Random forest classifier for remote sensing classification, International journal of remote sensing, 26, 1, pp. 217-222, (2005)

[28]

Kim Y. W., Subramanian S., Gerasopoulos K., Ben-Yoav H., Wu H.-C., Quan D., Carter K., Meyer M. T., Bentley W. E., Ghodssi R., Effect of electrical energy on the efficacy of biofilm treatment using the bioelectric effect, npj Biofilms and Microbiomes, 1, 1, pp. 1-8, (2015)

← 1 2 3 →