Objective: Current methods for the analysis of dynamic thermal video data from human participants, such as the estimation of mean temperatures from manually selected regions of interest, are rudimentary and provide very limited insight on temperature dynamics. This work proposes a method for the decomposition and analysis of dynamic thermal data to identify different sources of temporal temperature changes in the data. Methods: Principal component analysis (PCA) was applied to thermal video data to identify different sources of changes in temperature. The implemented algorithms were applied on dynamic thermal data of a thermally passive, inanimate object as well as thermal video data of the plantar aspect of human feet. Results: Different sources of temperature variations, consisting of a combination of passive surface cooling, environmental processes and physiological processes were identified. The passive cooling of the skin, typically observed during acclimatization, was noted to last over 60 min, much longer than the five to 20 min durations suggested in the literature. The decomposition that results from the proposed method uncovers underlying temperature dynamics that would typically not emerge from conventional analysis approaches since these would be overshadowed by this passive cooling component. Significance: This method of decomposition of the temporal changes in dynamic thermal data can provide a deeper understanding of the processes driving the temperature changes. The code for the developed algorithms has been made available online in the form of Python and Matlab functions, together with the results presented in this paper, and can be accessed from https://github.com/gaucijean/ThermoSuite.

中文翻译：

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动态热视频分析的主成分分析

目标：当前用于分析来自人类参与者的动态热视频数据的方法（例如从手动选择的感兴趣区域估计平均温度）是基本的，并且对温度动态的了解非常有限。这项工作提出了一种分解和分析动态热数据的方法，以识别数据中时间温度变化的不同来源。方法：将主成分分析 (PCA) 应用于热视频数据，以识别温度变化的不同来源。实施的算法应用于热被动、无生命物体的动态热数据以及人脚足底方面的热视频数据。结果：温度变化的不同来源，包括被动表面冷却、确定了环境过程和生理过程。通常在适应过程中观察到的皮肤被动冷却持续超过 60 分钟，比文献中建议的 5 到 20 分钟持续时间长得多。由所提出的方法产生的分解揭示了通常不会从传统分析方法中出现的潜在温度动态，因为这些会被这种被动冷却组件所掩盖。意义：这种分解动态热数据时间变化的方法可以更深入地了解驱动温度变化的过程。所开发算法的代码已以 Python 和 Matlab 函数的形式在线提供，以及本文中提供的结果，并可从 https 访问：