Subdirección de Investigación, Centro de Enseñanza Técnica Industrial, C. Nueva Escocia 1885, Guadalajara 44638, Mexico.
Posgrado en Ingeniería y Tecnología Aplicada, Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico.
Sensors (Basel). 2024 Mar 18;24(6):1944. doi: 10.3390/s24061944.
The remote monitoring of vital signs and healthcare provision has become an urgent necessity due to the impact of the COVID-19 pandemic on the world. Blood oxygen level, heart rate, and body temperature data are crucial for managing the disease and ensuring timely medical care. This study proposes a low-cost wearable device employing non-contact sensors to monitor, process, and visualize critical variables, focusing on body temperature measurement as a key health indicator. The wearable device developed offers a non-invasive and continuous method to gather wrist and forehead temperature data. However, since there is a discrepancy between wrist and actual forehead temperature, this study incorporates statistical methods and machine learning to estimate the core forehead temperature from the wrist. This research collects 2130 samples from 30 volunteers, and both the statistical least squares method and machine learning via linear regression are applied to analyze these data. It is observed that all models achieve a significant fit, but the third-degree polynomial model stands out in both approaches. It achieves an R value of 0.9769 in the statistical analysis and 0.9791 in machine learning.
由于 COVID-19 大流行对世界的影响，生命体征的远程监测和医疗保健已经成为当务之急。血氧水平、心率和体温数据对于管理疾病和确保及时医疗至关重要。本研究提出了一种使用非接触式传感器的低成本可穿戴设备，用于监测、处理和可视化关键变量，重点是体温测量作为关键健康指标。所开发的可穿戴设备提供了一种非侵入性和连续的方法来收集手腕和额头温度数据。然而，由于手腕和实际额头温度之间存在差异，本研究采用统计方法和机器学习从手腕估算核心额头温度。本研究从 30 名志愿者中收集了 2130 个样本，并应用统计最小二乘法和通过线性回归的机器学习来分析这些数据。结果表明，所有模型都达到了显著的拟合度，但三次多项式模型在两种方法中都很突出。它在统计分析中达到了 0.9769 的 R 值，在机器学习中达到了 0.9791。
