Continuous-wave radar and motion-derived biomarkers for non-contact vital status classification in end-of-life care: A clinically validated machine learning approach

In palliative care, effective communication about anticipated death is critical for aligning therapeutic goals, managing family expectations, and ensuring dignified care. However, prognostic uncertainty - particularly regarding the time of death - remains a challenge due to the limited reliability of current methods. This study explores the potential of radar-derived motion biomarkers as a novel approach to distinguish between living and deceased patients, addressing the need for objective decision-support tools in palliative care. Using continuous-wave radar, we recorded the torso displacement (distance signal) of 16 palliative care patients during their dying phase and derived ground-truth annotations from electronic health records (EHR). Machine learning (ML) algorithms processed 5-minute segments of radar-derived motion signals for binary vital status classification. We evaluated the results with balanced accuracy, Gini gain, and SHAP values. Palliative care specialists provided qualitative feedback to ensure clinical relevance. The ML models achieved balanced accuracy of 0.92-0.98 in distinguishing vital states, demonstrating radar technology's potential as an objective monitoring tool. This study is the first to investigate continuous motion biomarkers in end-of-life patients under real-world clinical conditions, capturing physiological changes during this critical phase. Limitations include the challenges in EHR-derived annotation accuracy, as well as the inherent complexity of physiological variability near death. Our findings highlight radar technology's viability for complementary vital status monitoring in palliative care settings. By providing objective data, this approach could reduce prognostic uncertainty while maintaining patient dignity. This work bridges technological innovation with palliative care's humanistic ethos, offering new possibilities for evidence-based end-of-life management.

Content

Subjects

Machine Learning in Healthcare

Non-invasive Patient Monitoring

Palliative Care Monitoring

Radar Technology

Vital Status Classification

DDC Class

600: Technology