Abstract
Dental disease is largely preventable and closely linked to poor
toothbrushing behaviors. Motion-sensors, such as accelerometers,
gyroscopes, and magnetometers, allow for mon- itoring of toothbrushing
behaviors. Researchers have attempted to infer tooth surface coverage
using sensors attached to the toothbrush handle or embedded in
smartwatches. However, the inferences may be deficient because the
datasets were collected under structured toothbrushing assumptions
performed in con- trolled laboratory settings and not the free-form and
irregular brushing patterns observed in real-world settings. To address
the aforementioned problem, we collected a dataset of 187 brush- ing
sessions, including free-form brushing. We present, to our knowledge,
the first motion-sensor dataset obtained during free- form brushing.
Using our experiences, we discuss the challenges of studying
toothbrushing behaviors in naturalistic settings. We also propose a
three-stage method (i.e. pre-processing, brush transition time
detection, and time-series classification) to detect the teeth surfaces
brushed during a session. Our findings are two-fold: (a) the
classification of teeth surfaces during free- form toothbrushing is more
challenging than during brushing in controlled settings; (b) high
classification accuracy can be achieved using random train-test split of
the data (i.e. k-fold cross-validation); however, generalization beyond
the participants in the training set poses difficulties. Beyond
publishing the first dataset of free-form toothbrushing, we validate our
findings by applying our proposed method to our provided dataset, as
well as the datasets of toothbrushing in controlled settings.