The project “DeepSpoMed – Novel evaluation of thermographic camera data using neural networks” is part of the cooperation project “Innovative sports medicine performance diagnostics based on a novel evaluation of thermographic camera data using neural networks – InnoSpoMed”.

The project is a collaboration between

Geheimnisvolle Muster – Infrarot-Thermografie für die sportmedizinische Leistungsdiagnostik – Videointerview @ MEDICA

The project is funded by the Federal Ministry for Economic Affairs and Energy (BMWi) as part of the “Central Innovation for SMEs” (ZIM) funding program.

Intra-university research funding level I – Johannes Gutenberg University Mainz.

Focus: Adaptation of the methodology to bicycle stress tests with the help of preliminary work.

Intra-university research funding level I – Johannes Gutenberg University Mainz.

Focus: Automatic data labeling with the help of a multimodal stereo system.

Federal Ministry for Economic Affairs and Energy (BMWi) (“Zentrale Innovation Mittelstand”, ZF4211603GR9)

Focus: Development of a thermogram processing pipeline and creation of AI models to recognize body parts and blood vessel structures.

Intra-university research funding level I – Johannes Gutenberg University Mainz.

Focus: Evaluation of existing computer vision methods and proof of concept for the detection of blood vessel structures in thermograms.

2025

Andrés López, D., Weber, V., Zentgraf, S., et al. (2025). ThermoCycleNet: Stereo-based Thermogram Labeling for Model Transition to Cycling. In Zenodo; Arxiv. DOI Author/Publisher URL
Andrés López, D., Weber, V., Zentgraf, S., et al. (2025). Presentation | ThermoCycleNet: Stereo-based Thermogram Labeling for Model Transition to Cycling. In A. Catalá, G. Joya, and I. Rojas (eds.), Book of Abstracts (pp. 24-24). Author/Publisher URL

2024

Lopez, D. A., Hillen, B., Naegele, M., et al. (2024). ThermoNet: advanced deep neural network-based thermogram processing pipeline for automatic time series analysis of specific skin areas in moving legs. JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY, 149(19), 11337-11348. DOI Author/Publisher URL
Lopez, D. A., Hillen, B., Naegele, M., et al. (2024). StereoThermoLegs: label propagation with multimodal stereo cameras for automated annotation of posterior legs during running at different velocities. JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY. Published. DOI Author/Publisher URL
Andrés López, D., Hillen, B., Naegele, M., et al. (2024). StereoThermoLegs Dataset. DOI
Andrés López, D. (2024). ThermoNet: deep neural network thermogram analysis of human calves during physical exercise [PhD Thesis]. DOI Author/Publisher URL

2023

Hillen, B., Zentgraf, S., Weber, V., et al. (2023, July 7). Poster | Deep neural network-driven time series analysis of posterior legs surface radiation during cardiopulmonary exercise testing and associations with core temperature and internal load. 28th Annual Congress of the European College of Sport Science (ECSS), Paris.
Hillen, B., Lopez, D. A., Marzano-Felisatti, J. M., et al. (2023). Acute physiological responses to a pyramidal exercise protocol and the associations with skin temperature variation in different body areas. JOURNAL OF THERMAL BIOLOGY, 115. DOI Author/Publisher URL
Hillen, B., Andrés López, D., Marzano Felisatti, J. M., et al. (2023, June 27). Poster | Skin thermal radiation variation during a pyramidal load cycling protocol and the association with individual load and thermoregulation. Sports, Medicine and Health Summit, CCH Hamburg.
Andrés López, D., Hillen, B., Simon, P., Schömer, E. (2023). Presentation | Improved deep neural network-driven automatic segmentation of skin’s thermal radiation in moving posterior legs during cardiopulmonary exercise testing for time series data analysis (A. Kiadó, ed.).
Hillen, B., Lopez, D. A., Pfirrmann, D., et al. (2023). An exploratory, intra- and interindividual comparison of the deep neural network automatically measured calf surface radiation temperature during cardiopulmonary running and cycling exercise testing: A preliminary study. JOURNAL OF THERMAL BIOLOGY, 113. DOI Author/Publisher URL

2022

Hillen, B., Lopez, D. A., Schömer, E., et al. (2022). Towards Exercise Radiomics: Deep Neural Network-Based Automatic Analysis of Thermal Images Captured During Exercise. IEEE Journal of Biomedical and Health Informatics, 1-11. DOI

2020

Andrés López, D., Hillen, B., Simon, P., and Schömer, E. (2020). Presentation | Deep learning based segmentation of uncovered body parts in thermal images during dynamic exercise. Book of Abstracts, 24-25. Author/Publisher URL
Andrés López, D., Hillen, B., Simon, P., and Schömer, E. (2020). Presentation | Thermonet: Automatic Segmentation of Blood Vessel Patterns detected by Infrared Thermography during dynamic Exercise (F. Dela, E. Müller, and E. Tsolakidis, eds.; Issue Book of Abstracts, pp. 127-127). Author/Publisher URL