TY  - JOUR
AB  - In biomedical monitoring, non-intrusive and continuous tracking of vital signs is a crucial yet challenging objective. Although accurate, traditional methods, such as electrocardiography (ECG) and photoplethysmography (PPG), necessitate direct contact with the patient, posing limitations for long-term and unobtrusive monitoring. To address this challenge, we introduce the EmRad system, an innovative solution harnessing the capabilities of continuous-wave (CW) radar technology for the contactless detection of vital signs, including heart rate and respiratory rate. EmRad discerns itself by emphasizing miniaturization, performance, scalability, and its ability to generate large-scale datasets in various environments. This article explains the system's design, focusing on signal processing strategies and motion artifact reduction to ensure precise vital sign extraction. The EmRad system's versatility is showcased through various case studies, highlighting its potential to transform vital sign monitoring in research and clinical contexts.
AU  - Albrecht, N.C.*
AU  - Langer, D.*
AU  - Krauss, D.*
AU  - Richer, R.*
AU  - Abel, L.*
AU  - Eskofier, B.M.
AU  - Rohleder, N.*
AU  - Koelpin, A.*
C1  - 71520
C2  - 56231
CY  - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa
SP  - 725-734
TI  - EmRad: Ubiquitous vital sign sensing using compact continuous-wave radars.
JO  - IEEE Open J. Eng. Med. Biol.
VL  - 5
PB  - Ieee-inst Electrical Electronics Engineers Inc
PY  - 2024
SN  - 2644-1276
ER  - 

TY  - JOUR
AB  - 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.
AU  - Krauss, D.*
AU  - Engel, L.*
AU  - Ott, T.*
AU  - Braunig, J.*
AU  - Richer, R.*
AU  - Gambietz, M.*
AU  - Albrecht, N.C.*
AU  - Hille, E.M.*
AU  - Ullmann, I.*
AU  - Braun, M.*
AU  - Dabrock, P.*
AU  - Kolpin, A.*
AU  - Koelewijn, A.D.*
AU  - Eskofier, B.M.
AU  - Vossiek, M.*
C1  - 71530
C2  - 56260
CY  - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa
SP  - 680-699
TI  - A review and tutorial on machine learning-enabled radar-based biomedical monitoring.
JO  - IEEE Open J. Eng. Med. Biol.
VL  - 5
PB  - Ieee-inst Electrical Electronics Engineers Inc
PY  - 2024
SN  - 2644-1276
ER  -