인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.2
- 수록면
- 358 - 367 (10page)
- DOI
- 10.5370/KIEE.2024.73.2.358
이용수
초록· 키워드
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.
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목차
- Abstract
- 1. 서론
- 2. 관련 연구
- 3. 자세 기반 양치구역 추정을 위한 접근 방법
- 4. 양치구역 추정 모델 성능 평가
- 5. 결론
- References