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Krauss, D.* ; Engel, L.* ; Ott, T.* ; Braunig, J.* ; Richer, R.* ; Gambietz, M.* ; Albrecht, N.C.* ; Hille, E.M.* ; Ullmann, I.* ; Braun, M.* ; Dabrock, P.* ; Kolpin, A.* ; Koelewijn, A.D.* ; Eskofier, B.M. ; Vossiek, M.*

A review and tutorial on machine learning-enabled radar-based biomedical monitoring.

IEEE Open J. Eng. Med. Biol. 5, 680-699 (2024)

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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.

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1.065