Acute physiological responses to a pyramidal exercise protocol and the associations with skin temperature variation in different body areas

Hillen, Barlo; López, Daniel; Marzano-Felisatti, Joaquín Martín; Sanchez-Jimenez, Jose Luis; Cibrián, Rosa; Nägele, Markus; Palmer, Rosario Salvador; Pérez-Soriano, Pedro; Schömer, Elmar; Simon, Perikles; Priego-Quesada, José Ignacio

doi:10.1016/j.jtherbio.2023.103605

Journal of Thermal Biology

2023

DOI: 10.1016/j.jtherbio.2023.103605

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Acute physiological responses to a pyramidal exercise protocol and the associations with skin temperature variation in different body areas

Barlo Hillen

Daniel López

Joaquín Martín Marzano-Felisatti

et al.

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2024

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Cited by 2 publications

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ThermoNet: advanced deep neural network-based thermogram processing pipeline for automatic time series analysis of specific skin areas in moving legs

Andrés López,

Hillen,

Nägele

et al. 2024

J Therm Anal Calorim

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Infrared thermography is an emerging technique in biomedical research, potentially providing diagnostic insights into psychological stress, physical strain, muscle fatigue, inflammation, tissue damage, and diseases with thermogenic effects. However, manual analysis strategies are frequently applied causing incomparable, non-reproducible results and hampering standardization. Moreover, widely applied manual analysis cannot recognize blood vessel-related thermal radiation patterns during physical exercise. Therefore, an enhanced processing pipeline, “ThermoNet”, has been developed to automatically process thermograms captured during running. For acquisition, an automatic temperature calibration technique has been introduced to obtain reliable pixel-temperature mapping. The thermograms are semantically segmented in the processing pipeline to extract the anatomical regions of interest (ROIs) by a state-of-the-art deep neural network rather than considering both legs as a single area. A second neural network further examines the ROIs to identify different venous and arterial (perforator) patterns. Within the segments, advanced statistical features are computed to provide time series data. Separate analysis of venous and perforator vessel patterns is carried out on individual connected components, resulting in the extraction of 276 features for each thermogram. The enhanced ROI extraction achieved a high accuracy for the left and right calf on the manually annotated test set. Each step of the ThermoNet pipeline represents a significant improvement over previous analysis methods. Finally, ThermoNet is a transferable pipeline for automatic, reproducible, and objective analysis of ROIs in thermal image sequences of moving test individuals.

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ThermoNet: advanced deep neural network-based thermogram processing pipeline for automatic time series analysis of specific skin areas in moving legs

Andrés López,

Hillen,

Nägele

et al. 2024

J Therm Anal Calorim

View full text Add to dashboard Cite

show abstract

Effect of spinal cord injury on the skin temperature of different regions of interest during a graded exercise test in a moderate temperature environment

Sanchez-Jimenez,

Cibrian-Ortiz de Anda,

Vazquez-Fariñas

et al. 2024

Journal of Thermal Biology

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Acute physiological responses to a pyramidal exercise protocol and the associations with skin temperature variation in different body areas

Cited by 2 publications

References 48 publications

ThermoNet: advanced deep neural network-based thermogram processing pipeline for automatic time series analysis of specific skin areas in moving legs

ThermoNet: advanced deep neural network-based thermogram processing pipeline for automatic time series analysis of specific skin areas in moving legs

Effect of spinal cord injury on the skin temperature of different regions of interest during a graded exercise test in a moderate temperature environment

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