使用深度学习在智能眼镜中控制和实时监测进食行为的光学追踪传感器研究：横断面研究

随着肥胖症患病率的不断上升，亟需创新方法以更好地理解这一健康危机，特别是考虑到其与糖尿病、癌症和心血管疾病等慢性疾病的密切关联。监测饮食行为对于设计有效干预措施、降低肥胖率和促进健康生活方式至关重要。然而，传统的饮食追踪方法受到参与者负担和回忆偏差的限制。除了进食事件外，微观层面的进食活动，如用餐时长和咀嚼频率，也具有重要意义，因为它们与肥胖和疾病风险密切相关。本研究的主要目标是开发一种基于传感器的智能眼镜，能够自动且非侵入性地监测进食和咀嚼活动。该系统能够区分咀嚼与其他面部活动（如说话和牙齿紧闭）。次要目标是评估该系统在未见过的测试用户中的性能，结合实验室控制和真实生活场景的用户研究。与现有的检测完整进食事件的研究不同，我们的方法提供更细粒度的分析，专门检测每个进食期间的咀嚼片段。该研究利用嵌入在智能眼镜中的OCO光学传感器监测与进食和咀嚼相关的面部肌肉激活。这些传感器测量皮肤表面在二维（X和Y）上的相对运动。通过深度学习（DL）分析这些数据，以区分咀嚼与其他面部活动。为考虑真实环境中咀嚼事件的时间依赖性，我们引入了隐马尔可夫模型（HMM）作为额外组件，以分析DL模型的输出。统计检验显示，涉及两个传感器（颊部和太阳穴）和三种面部活动（进食、紧咬和说话）的六组比较中，平均传感器激活值均存在统计学显著差异（P<.001），证明传感器数据的敏感性。此外，结合卷积和长短期记忆（LSTM）神经网络的卷积长短期记忆模型在咀嚼检测中表现最佳。在受控实验室环境中，该模型达到了0.91的F1-score，表现出强大的性能。在实际应用场景中，该系统在检测进食片段方面表现出高精度（0.95）和召回率（0.82）。在真实生活研究中评估的咀嚼频率和咀嚼次数与预期的日常进食行为保持一致。该研究在饮食监测和健康技术方面取得了实质性进展。通过提供一种可靠且非侵入性的进食行为追踪方法，有望彻底改变饮食数据的收集和应用方式，从而实现更有效的健康干预，更深入理解影响饮食习惯及其健康影响的因素。

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.