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