Hygiea+: Toward Energy-Efficient and Highly Accurate Toothbrushing Monitoring via Wrist-Worn Gesture Sensing

Proper and effective toothbrushing technique is crucial for maintaining oral health. However, there are often limited opportunities for individuals to receive specific training in toothbrushing posture in their daily lives. In this paper, we propose Hygiea+, a convenient, energy-efficient, and highly accurate toothbrushing monitoring system based on wrist-worn wearables. By leveraging Inertial Measurement Units (IMU) in wrist-worn devices for gesture sensing, Hygiea+ enables users to accurately and efficiently monitor their toothbrushing activities without any modifications to the toothbrush. We propose a number of novel techniques to achieve the goal of high sensing accuracy and energy efficiency. To reduce the energy consumption of continuous IMU sampling, we model the sensing problem as a Markov process and design a Partially Observable Markov Decision Process (POMDP)-based adaptive sampling strategy to dynamically adjust the sampling frequency. To achieve high sensing accuracy, we first propose a novel signal preprocessing method to mitigate variations resulting from different toothbrush types and user habits. Then, we propose a deep reinforcement learning-based data distillation mechanism to extract key segments from continuous toothbrushing actions, thus reducing the impact of redundant data and noise. In the classification stage, we design an attention-based Long Short-Term Memory (AT-LSTM) network for fine-grained toothbrushing posture recognition. In addition, to address the accuracy degradation of new users, we adopt the common but effective fine-tuning method to alleviate the data collection burden on new users. Finally, we connect advanced Large Language Models (LLMs) to provide users with necessary feedback on toothbrushing behavior and health recommendations. Extensive experiments using both manual and electric toothbrushes demonstrate Hygiea+ achieves up to 98.8% accuracy in toothbrushing posture recognition while maintaining superior energy efficiency. © 2024 IEEE.