RT Journal Article
T1 Implementation and analysis of an oral health monitoring system using smartwatches and convolutional neural networks.
A1 González-Cañete, Francisco Javier
A1 Casilari-Pérez, Eduardo
K1 Internet de los objetos
K1 Aprendizaje automático (Inteligencia artificial)
K1 Redes neuronales (Informática)
AB Activity tracking provided by smartwatches offers a cost-effective widespread solution for the recognition of hand movements, which makes them an appealing tool to promote permanent health monitoring and, in particular, oral hygiene habits among the population. This study proposes the integration of wearable devices and artificial intelligence methods to identify manual movements associated with tooth brushing. The focus is on utilizing convolutional neural networks to recognize brushing gestures based on short samples of accelerometer data collected from wrist-worn devices. The architecture is systematically trained and validated using long-term datasets collected with different smartwatch models during the daily routines of a small group of experimental users. The results show the high effectiveness and capability of generalization of the detector under LOSO cross validation both when it is evaluated in an offline way and when it is trained and implemented on a smartwatch to operate in real time in a real-life scenario.
PB Elsevier
YR 2026
FD 2026-05-11
LK https://hdl.handle.net/10630/46619
UL https://hdl.handle.net/10630/46619
LA eng
NO F. J. González-Cañete, E. Casilari, Implementation and analysis of an oral health monitoring system using smartwatches and convolutional neural networks, Internet of Things, Volume 38, 2026, 101969, ISSN 2542-6605, https://doi.org/10.1016/j.iot.2026.101969
NO https://openpolicyfinder.jisc.ac.uk/publication/35797
NO Spanish Ministry of Science, Innovation, and Universities (MCIN/AEI/10.13039/501100011033) and NextGenerationEU/PRTR Funds under grant ED2021-130456B-I00, by Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech (grant B4-2023-12).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 15 may 2026