The challenge of dealing with patients suffering from chronic diseases and an aging population requires evolving from traditional hospital-based healthcare systems into a person-centered approach, where patients can be monitored remotely via modern technologies by cost-effective and reliable solutions based on emerging technologies in the healthcare domain.

Due to its contactless capabilities, radio-frequency technologies can lead to proactive monitoring of conditions directly related to health statuses. These technologies can include the tracking and monitoring of vital signs or the identification of abnormalities and critical life-threatening events, such as strokes or falls, in order to react before more complex scenarios and non-treatable conditions can appear over time.

This thesis project explores developing, evaluating, and verifying a processing pipeline based on radar sensing technology, jointly exploring human activity recognition and breathing frequency estimation, two of the most immediate capabilities to detect and monitor the general health conditions of a human being.

Through Doppler-Time and Range-Time data domains, the differentiation between translational and in-place activities, namely walking and sitting, is addressed, aiming to successfully identify and locate the segments where the test subject is not moving. This then triggers a proposed pipeline for the continuous estimation of the breathing frequency for the in-place scenario based on a sequential estimator, specifically the extended Kalman filter.