With the aging population, the demand for healthcare and related services is increasing and, for this reason, technologies for remote patient monitoring are developing, aiming at indoor scenarios. Remote patient monitoring can help capture the clinical data of patients at home, which can save time and money, specifically reducing the need for hospitalization by potentially detecting health-related issues before they become too serious.
The non-contact radar-based technology can be applied in the remote patient monitoring system for detecting vital signs. Radars are suitable for applications at home because they are non-invasive, robust in changing lighting and temperature, and suitable for patients with skin irritation.
Heartbeat and respiration are critical clinical data for the diagnosis of the disease. The study of respiration frequency estimation was explored by previous work, such as the MSc thesis in \cite{Maxthesis}. Building on that work, this project proposes a pipeline to measure the heartbeat frequency and cancel the random body movement. The impact of different orientations is also studied. The phase history difference of the chest displacement due to vital signs is extracted, and the wavelet transform is used to separate heartbeat and respiration signals. Different methods are tested to calculate the heartbeat frequency in the time and frequency domain. The RBM is detected by the energy threshold of the phase difference, and the intervals with the RBM are discarded.
The simulation and experimental results indicate that the proposed processing pipeline can work on the radar data.