A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical Monitoring

Area of Science:

• Biomedical Engineering
• Signal Processing
• Machine Learning

Background:

• Radar sensing offers contactless, unobtrusive physiological monitoring, overcoming limitations of current methods.
• Radar data is complex and requires specialized interpretation for clinical applications.
• Integrating radar data acquisition with ML processing is crucial for a complete biomedical monitoring solution.

Purpose of the Study:

• To provide a tutorial on radar-based ML applications for biomedical monitoring.
• To emphasize a holistic, end-to-end data analysis pipeline for radar sensing.
• To address the ethical considerations of radar-based biomedical monitoring.

Main Methods:

• Fundamentals of radar and ML theory are explained.
• Data acquisition and representation techniques for radar signals are outlined.
• Categories of clinical relevance for radar-based monitoring are discussed.

Main Results:

• A comprehensive overview of radar sensing and ML for biomedical applications.
• Guidance on creating an integrated data analysis pipeline.
• Identification of ethical concerns including data privacy, ownership, and algorithmic bias.

Conclusions:

• Radar sensing combined with ML presents a powerful approach for advanced biomedical monitoring.
• An end-to-end pipeline is essential for effective interpretation of radar data.
• Addressing ethical implications is vital for responsible deployment of this technology.