Regions of interest selection and thermal imaging data analysis in sports and exercise science: a narrative review. (27th August 2021)

• Record Type: Journal Article
• Authors: Perpetuini, David; Formenti, Damiano; Cardone, Daniela; Filippini, Chiara; Merla, Arcangelo
• Is Part Of: Physiological measurement. Volume 42:Number 8(2021)
• Publication Date: 2021-08-27
• Subjects: infrared thermography -- morphing -- video tracking -- machine learning -- sport medicine -- thermal distribution; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/ac0fbd
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: Objective: Infrared thermography (IRT) is a non-invasive, contactless and low-cost technology that allows recording of the radiating energy that is released from a body, providing an estimate of its superficial temperature. Thanks to the improvement of infrared thermal detectors, this technique is widely used in the biomedical field to monitor the skin temperature for different purposes (e.g. assessing circulatory diseases, psychophysiological state, affective computing). Particularly, in sports and exercise science, thermography is extensively used to assess sports performance, to investigate superficial vascular changes induced by physical exercise, and to monitor injuries. However, the methods of analysis employed to treat IRT data are not standardized, and hence introduce variability in the results. Approach: This review focuses on the methods of analysis currently used for thermal imaging in sports and exercise science. Main Results: Firstly, the procedures employed for the selection of regions of interest (ROIs) from anatomical body districts are reviewed, paying attention also to the potentialities of morphing algorithms to increase the reproducibility of thermal results. Secondly, the statistical approaches utilized to characterize the temperature frequency and spatial distributions within ROIs are investigated, showing their strengths and weaknesses. Moreover, the importance of employing tracking methods to analyze the temporal thermal oscillations within ROIs is discussed. Thirdly, the capability of employing procedures of investigation based on machine learning frameworks on thermal imaging in sports science is examined. Significance: Finally, some proposals to improve the standardization and the reproducibility of IRT data analysis are provided, in order to facilitate the development of a common database of thermal images and to improve the effectiveness of IRT in sports science.