对智能眼镜中的光跟踪传感器的控制和现实生活调查，用于使用深度学习监测饮食行为：横断面研究

肥胖症患病率的日益普遍性需要创新的方法来更好地理解这种健康危机，尤其是由于它与诸如糖尿病，癌症和心血管疾病等慢性疾病的密切联系。监测饮食行为对于设计有效的干预措施至关重要，这些干预措施有助于降低肥胖症患病率并促进健康的生活方式。但是，传统的饮食跟踪方法受到参与者负担和回忆偏见的限制。探索微观饮食活动（例如饮食持续时间和咀嚼频率）除了饮食外，由于它们与肥胖和疾病风险的实质性关系至关重要。该研究的主要目标是开发出一种使用配备传感器的智能玻璃自动监测和咀嚼活动的准确且无创的系统。该系统将咀嚼与其他面部活动（例如说话和牙齿紧握）区分开。次要目标是使用实验室控制和现实生活中的用户研究的组合评估系统对看不见的测试用户的性能。与关注全面饮食发作的最先进的研究不同，我们的方法通过特异性检测每个饮食发作中的咀嚼细分市场提供了更细致的分析。该研究使用嵌入智能眼镜中的OCO光学传感器来监测与饮食和饮食和咀嚼活动相关的面部肌肉激活。传感器在2个维度（x和y）上测量皮肤表面上的相对运动。使用深度学习（DL）分析这些传感器的数据，以区分咀嚼与其他面部活动。为了解决现实生活中的咀嚼事件之间的时间依赖，我们将隐藏的马尔可夫模型集成为分析DL模型的输出的附加组件。平均传感器激活的统计测试揭示了所有6个比较对（P <.001）的统计学上显着差异（P <.001），涉及2个涉及2个传感器和3个面部活动（Cheeks and Temple）和3个面部活动（饮食，饮食，clench，clench，seephench，seephench）。这些结果证明了传感器数据的灵敏度。此外，卷积长的短期记忆模型是卷积和长期记忆神经网络的组合，它是咀嚼检测表现最佳的DL模型。在受控的实验室环境中，该模型的F1得分为0.91，表明性能强劲。在现实生活中，该系统表现出高精度（0.95）和召回（0.82），用于检测饮食领域。现实生活研究中评估的咀嚼率和咀嚼数量表明与预期的现实饮食行为保持一致。该研究代表了饮食监测和卫生技术的重大进步。通过提供一种可靠且无创的方法来跟踪饮食行为，它有可能革新收集和使用饮食数据的方式。这可能会导致更有效的健康干预措施，并更好地了解影响饮食习惯的因素及其健康影响。

The increasing prevalence of obesity necessitates innovative approaches to better understand this health crisis, particularly given its strong connection to chronic diseases such as diabetes, cancer, and cardiovascular conditions. Monitoring dietary behavior is crucial for designing effective interventions that help decrease obesity prevalence and promote healthy lifestyles. However, traditional dietary tracking methods are limited by participant burden and recall bias. Exploring microlevel eating activities, such as meal duration and chewing frequency, in addition to eating episodes, is crucial due to their substantial relation to obesity and disease risk.The primary objective of the study was to develop an accurate and noninvasive system for automatically monitoring eating and chewing activities using sensor-equipped smart glasses. The system distinguishes chewing from other facial activities, such as speaking and teeth clenching. The secondary objective was to evaluate the system's performance on unseen test users using a combination of laboratory-controlled and real-life user studies. Unlike state-of-the-art studies that focus on detecting full eating episodes, our approach provides a more granular analysis by specifically detecting chewing segments within each eating episode.The study uses OCO optical sensors embedded in smart glasses to monitor facial muscle activations related to eating and chewing activities. The sensors measure relative movements on the skin's surface in 2 dimensions (X and Y). Data from these sensors are analyzed using deep learning (DL) to distinguish chewing from other facial activities. To address the temporal dependence between chewing events in real life, we integrate a hidden Markov model as an additional component that analyzes the output from the DL model.Statistical tests of mean sensor activations revealed statistically significant differences across all 6 comparison pairs (P<.001) involving 2 sensors (cheeks and temple) and 3 facial activities (eating, clenching, and speaking). These results demonstrate the sensitivity of the sensor data. Furthermore, the convolutional long short-term memory model, which is a combination of convolutional and long short-term memory neural networks, emerged as the best-performing DL model for chewing detection. In controlled laboratory settings, the model achieved an F1-score of 0.91, demonstrating robust performance. In real-life scenarios, the system demonstrated high precision (0.95) and recall (0.82) for detecting eating segments. The chewing rates and the number of chews evaluated in the real-life study showed consistency with expected real-life eating behaviors.The study represents a substantial advancement in dietary monitoring and health technology. By providing a reliable and noninvasive method for tracking eating behavior, it has the potential to revolutionize how dietary data are collected and used. This could lead to more effective health interventions and a better understanding of the factors influencing eating habits and their health implications.