Principal component analysis for dynamic thermal video analysis

Abstract

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.