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 article, we propose Hygiea+, a convenient, energy-efficient, and highly accurate toothbrushing monitoring system based on wrist-worn wearables. By leveraging inertial measurement units (IMUs) 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.

中文翻译：

---

Hygiea+：通过腕戴式手势感应实现节能和高精度的刷牙监测


正确有效的刷牙技巧对于保持口腔健康至关重要。然而，个人在日常生活中接受牙刷姿势特定培训的机会通常有限。在本文中，我们提出了 Hygiea+，这是一种基于腕戴式可穿戴设备的方便、节能且高精度的刷牙监测系统。通过利用腕戴式设备中的惯性测量单元 （IMU） 进行手势感应，Hygiea+ 使用户能够准确有效地监控他们的刷牙活动，而无需对牙刷进行任何修改。我们提出了许多新技术来实现高传感精度和能源效率的目标。为了降低连续 IMU 采样的能耗，我们将传感问题建模为马尔可夫过程，并设计了一种基于部分可观察马尔可夫决策过程 （POMDP） 的自适应采样策略来动态调整采样频率。为了实现高传感精度，我们首先提出了一种新的信号预处理方法，以减轻不同牙刷类型和用户习惯导致的变化。然后，我们提出了一种基于深度强化学习的数据蒸馏机制，从连续的刷牙动作中提取关键片段，从而减少冗余数据和噪声的影响。在分类阶段，我们设计了一个基于注意力的长短期记忆 （AT-LSTM） 网络，用于细粒度的刷牙姿势识别。此外，为了解决新用户的准确性下降问题，我们采用了常见但有效的微调方法来减轻新用户的数据收集负担。 最后，我们连接高级大型语言模型 （LLMs），为用户提供有关刷牙行为和健康建议的必要反馈。使用手动和电动牙刷的广泛实验表明，Hygiea+ 在保持卓越能源效率的同时，在牙刷姿势识别方面实现了高达 98.8% 的准确率。