Abstract
The present paper discusses the measurement and analysis of toothbrushing activity under real-life conditions. To measure the activity, acceleration data are collected by means of a sensor brush. The data logging is triggered by super-threshold values of acceleration, which can give rise to false activations by non-brushing activities. Thus, a post-processing of appropriate data features is performed, which involves the application of an adaptive clustering technique. The timing of the identified toothbrushing events allows, e.g., to construct a time profile of brush usage over the day. The method is illustrated with results from an oralcare intervention campaign. This shows the potency to generate reliable insights, such as the effect of the intervention on subgroups, which are difficult to obtain by classical methods such as questionnaires.
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