Manual and Electronic Toothbrushing Detection Using Wearable Technology

Proper oral hygiene is crucial for maintaining good dental health, and regular toothbrushing is an essential component of this process. Furthermore, failure to brush has been linked to neurodegenerative conditions such as Alzheimer’s disease and dementia. However, monitoring and evaluating an individual’s toothbrushing can be challenging. This study presents a proof-of-concept approach for automatically detecting both manual and electronic toothbrushing activities using data from a 3-axis accelerometer. We propose a machine-learning framework that classifies electronic and manual brushing with 91.7% accuracy. The proposed framework employs machine learning techniques to analyze the accelerometer data and classify the recorded movements as either manual or electronic toothbrushing events. Tracking these two types of brushing activities can provide valuable insights into an individual’s oral hygiene practices, enabling personalized feedback and interventions to improve brushing habits. The paper discusses the data collection process, feature extraction techniques, and the classification algorithm employed. Experimental results demonstrate the effectiveness of the proposed approach, paving the way for developing intelligent oral hygiene and cognitive impairment monitoring systems among seniors in care homes.