A Review and Tutorial on Machine Learning- Enabled Radar-Based Biomedical Monitoring
Krauß D, Engel L, Ott T, Bräunig J, Richer R, Gambietz M, Albrecht N, Hille EM, Ullmann I, Braun M, Dabrock P, Kölpin A, Koelewijn A, Eskofier B, Vossiek M (2024)
Publication Type: Journal article
Publication year: 2024
Journal
Pages Range: 1-22
DOI: 10.1109/OJEMB.2024.3397208
Abstract
- Radio detection and ranging-based (radar) sensing offers unique opportunities for biomedical monitoring and can help overcome the limitations of currently established solutions. Due to its contactless and unobtrusive measurement principle, it can facilitate the longitudinal recording of human physiology and can help to bridge the gap from laboratory to real-world assessments. However, radar sensors typically yield complex and multidimensional data that are hard to interpret without domain expertise. Machine learning (ML) algorithms can be trained to extract meaningful information from radar data for medical experts, enhancing not only diagnostic capabilities but also contributing to advancements in disease prevention and treatment. However, until now, the two aspects of radar-based data acquisition and ML-based data processing have mostly been addressed individually and not as part of a holistic and end-to-end data analysis pipeline. For this reason, we present a tutorial on radar-based ML applications for biomedical monitoring that equally emphasizes both dimensions. We highlight the fundamentals of radar and ML theory, data acquisition and representation and outline categories of clinical relevance. Since the contactless and unobtrusive nature of radar-based sensing also raises novel ethical concerns regarding biomedical monitoring, we additionally present a discussion that carefully addresses the ethical aspects of this novel technology, particularly regarding data privacy, ownership, and potential biases in ML algorithms.
Authors with CRIS profile
Daniel Krauß
Machine Learning and Data Analytics Lab
Lukas Engel
Lehrstuhl für Hochfrequenztechnik
Tabea Ott
Lehrstuhl für Systematische Theologie II (Ethik)
Johanna Bräunig
Lehrstuhl für Hochfrequenztechnik
Robert Richer
Machine Learning and Data Analytics Lab
Markus Gambietz
Ingrid Ullmann Lehrstuhl für Hochfrequenztechnik Peter Dabrock Lehrstuhl für Systematische Theologie II (Ethik) Anne Koelewijn
Björn Eskofier Machine Learning and Data Analytics Lab Martin Vossiek Lehrstuhl für Hochfrequenztechnik
Ingrid Ullmann Lehrstuhl für Hochfrequenztechnik Peter Dabrock Lehrstuhl für Systematische Theologie II (Ethik) Anne Koelewijn
Björn Eskofier Machine Learning and Data Analytics Lab Martin Vossiek Lehrstuhl für Hochfrequenztechnik
Involved external institutions
Rheinische Friedrich-Wilhelms-Universität Bonn
Germany (DE)
Technische Universität Hamburg-Harburg (TUHH)
Germany (DE)
Germany (DE)
Technische Universität Hamburg-Harburg (TUHH)
Germany (DE)
How to cite
APA:
Krauß, D., Engel, L., Ott, T., Bräunig, J., Richer, R., Gambietz, M.,... Vossiek, M. (2024). A Review and Tutorial on Machine Learning- Enabled Radar-Based Biomedical Monitoring. IEEE Open Journal of Engineering in Medicine and Biology, 1-22. https://doi.org/10.1109/OJEMB.2024.3397208
MLA:
Krauß, Daniel, et al. "A Review and Tutorial on Machine Learning- Enabled Radar-Based Biomedical Monitoring." IEEE Open Journal of Engineering in Medicine and Biology (2024): 1-22.
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