Laboratory of Ergonomics and Physiology, Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, National Institute for Insurance against Accidents at Work (INAIL), Monte Porzio Catone, Italy.
Unit of Advanced Robotics and Human-Centred Technologies, Campus Bio-Medico University of Rome, Rome, Italy.
Front Public Health. 2024 Mar 11;12:1219595. doi: 10.3389/fpubh.2024.1219595. eCollection 2024.
Early identification of hypothermia or hyperthermia is of vital importance, and real-time monitoring of core temperature () of the workers exposed to thermal environments is an extremely valuable tool. From the existing literature studies, the model developed by Buller et al. in their study of 2013 that generates real-time estimates of from heart rate () measurements using the Kalman filter (KF) shows good potential for occupational application. However, some aspects could be improved to reliably handle the existing very wide range of workers and work activities. This study presents a real-time estimation model, called the Biphasic Kalman filter-based (BKFB) model, based on measurement, with characteristics suited to application in the occupational field.
Thirteen healthy subjects (six female and seven male) were included in the study to perform three consecutive tasks simulating work activities. During each test, an ingestible sensor was used to measure and a sensor to measure . The KF methodology was used to develop the BKFB model.
An algorithm with a biphasic structure was developed using two different models for the increasing and decreasing phases of , with the ability to switch between the two based on an threshold. estimates were compared with measurements, and with respect to overall root mean square error (RMSE), the BKFB model achieved a sizeable reduction (0.28 ± 0.12°C) compared to the Buller et al. model (0.34 ± 0.16°C).
The BKFB model introduced some modifications over the Buller et al. model for a more effective application in the occupational field. It was developed using data collected from a sample of workers (heavily weighted toward middle-aged, not very fit, and with a considerable fraction of female workers), and it also included two different modeling of (for the up- and down-phases), which allowed for better behavioral modeling in the two different stages. The BKFB model provides estimates reasonably in comparison to the measured intra-abdominal temperature values in both the activity and recovery phases but is more practical and easier to use for a real-time monitoring system of the workers' thermal states.
及早发现体温过低或过高至关重要,实时监测暴露于热环境中的工人的核心体温()是一种极其有价值的工具。从现有文献研究来看,Buller 等人在 2013 年的研究中开发的模型,通过使用卡尔曼滤波器(KF)从心率()测量值生成的实时估计值,显示出在职业应用方面有很大的潜力。然而,为了能够可靠地处理现有的广泛的工人和工作活动,仍有一些方面需要改进。本研究提出了一种实时估计模型,称为基于双相卡尔曼滤波器的(BKFB)模型,该模型基于测量,具有适合在职业领域应用的特点。
本研究纳入了 13 名健康受试者(6 名女性和 7 名男性),进行了三项连续的模拟工作活动的任务。在每次测试中,使用可摄入的传感器来测量和使用传感器来测量。使用卡尔曼滤波方法来开发 BKFB 模型。
使用两种不同的模型开发了一种具有双相结构的算法,用于和的上升和下降阶段,能够根据阈值在两者之间切换。将估计值与测量值进行比较,并根据总体均方根误差(RMSE),与 Buller 等人的模型(0.34±0.16°C)相比,BKFB 模型实现了相当大的降低(0.28±0.12°C)。
BKFB 模型在职业领域的更有效应用方面对 Buller 等人的模型进行了一些修改。它是使用从工人样本(以中年、不太健康且女性工人比例相当大的工人为重点)中收集的数据开发的,它还包括对(上升和下降阶段)的两种不同建模,这允许在两个不同阶段进行更好的行为建模。BKFB 模型在活动和恢复阶段与测量的腹腔内温度值相比,提供了相当合理的估计值,但对于工人热状态的实时监测系统来说,它更实用且更易于使用。