Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA.
Sensors (Basel). 2024 Aug 17;24(16):5331. doi: 10.3390/s24165331.
The rising incidence of type 2 diabetes underscores the need for technological innovations aimed at enhancing diabetes management by aiding individuals in monitoring their dietary intake. This has resulted in the development of technologies capable of tracking the timing and content of an individual's meals. However, the ability to use non-invasive wearables to estimate or classify the carbohydrate content of the food an individual has just consumed remains a relatively unexplored area. This study investigates carbohydrate content classification using postprandial heart rate responses from non-invasive wearables. We designed and developed timeStampr, an iOS application for collecting timestamps essential for data labeling and establishing ground truth. We then conducted a pilot study in controlled, yet naturalistic settings. Data were collected from 23 participants using an Empatica E4 device worn on the upper arm, while each participant consumed either low-carbohydrate or carbohydrate-rich foods. Due to sensor irregularities with dark skin tones and non-compliance with the study's health criteria, we excluded data from three participants. Finally, we configured and trained a Light Gradient Boosting Machine (LGBM) model for carbohydrate content classification. Our classifiers demonstrated robust performance, with the carbohydrate content classification model consistently achieving at least 84% in accuracy, precision, recall, and AUCROC within a 60 s window. The results of this study demonstrate the potential of postprandial heart rate responses from non-invasive wearables in carbohydrate content classification.
2 型糖尿病发病率的上升突显出需要进行技术创新，以帮助个人监测饮食摄入，从而加强糖尿病管理。这导致了能够跟踪个人用餐时间和内容的技术的发展。然而，使用非侵入性可穿戴设备来估计或分类个人刚刚摄入的食物中的碳水化合物含量仍然是一个相对未被探索的领域。本研究调查了使用非侵入性可穿戴设备的餐后心率反应进行碳水化合物含量分类。我们设计并开发了 timeStampr，这是一个用于收集数据标记和建立基准所需的时间戳的 iOS 应用程序。然后，我们在受控但自然的环境中进行了一项试点研究。使用佩戴在上臂的 Empatica E4 设备从 23 名参与者中收集数据，而每位参与者都食用了低碳水化合物或富含碳水化合物的食物。由于深色皮肤色调的传感器不规则和不符合研究健康标准，我们排除了三名参与者的数据。最后，我们为碳水化合物含量分类配置和训练了一个轻量级梯度提升机 (LGBM) 模型。我们的分类器表现出强大的性能，在 60 秒的窗口内，碳水化合物含量分类模型的准确性、精度、召回率和 AUCROC 始终至少达到 84%。这项研究的结果表明，非侵入性可穿戴设备的餐后心率反应在碳水化合物含量分类中具有潜力。
