HearForce: Force Estimation for Manual Toothbrushing with Earables

Excessive tooth brushing force can contribute to oral health issues such as gum recession and bleeding. While some electric toothbrushes offer force feedback, manual toothbrushes remain widely used due to their affordability and accessibility but do not give this sort of information to users. Therefore, they often lack awareness of the force they apply while brushing. In this paper, we introduce HearForce, the first system to estimate tooth brushing force from manual toothbrushing using widely available earbuds. Unlike existing solutions that require significant modifications to toothbrushes, HearForce leverages in-ear microphones on commercial earbuds to capture bone-conducted toothbrushing sound propagating from the oral cavity to the ear canals. Our key insight is that variations in brushing force modulate these toothbrushing sounds due to the friction effect, allowing us to infer force levels through deep learning. However, individual habitual and anatomical differences introduce significant challenges for force estimation. To mitigate this, we propose a self-supervised representation learning network with a cross-attention mechanism to suppress user-dependent variability and a heuristic calibration strategy to adapt the model to different brushing habits. Through extensive evaluation, HearForce demonstrates force estimation capabilities with a Mean Absolute Error (MAE) of 37.3g in user-independent settings, corresponding to 11.4% of the typical force dynamic range. Our study makes the first step in the use of everyday earbuds for manual toothbrushing force monitoring, paving the way for accessible solutions to improve brushing habits for manual toothbrush users globally.

CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing design and evaluation methods.