• Amplitude Modulation (AM) is used for signal transmission and reception due to its simplicity and effectiveness in detecting respiratory motion. As the transmitted signal reflects off the patient’s chest, respiration-induced movements modulate its amplitude. These variations correspond to the respiratory pattern, enabling real-time, non-contact monitoring with minimal computational complexity. • The ADALM-Pluto SDR enables cost-effective RF signal transmission with a flexible frequency range suitable for respiratory rate monitoring. It transmits signals that reflect off the chest, capturing amplitude variations caused by respiration. These variations are analyzed to accurately calculate the respiration rate in real time. • We used frame-to-frame subtraction for efficient clutter mitigation by isolating chest movement and VMD for precise spectral analysis, ensuring adaptability to moving objects. This enhances robustness in real-time, non-contact respiratory monitoring. • The effectiveness of the proposed method is confirmed through extensive experimental evaluation, which reports average root mean square error (RMSE) and mean absolute error (MAE) values of 2.03 BPM and 1.69 BPM, respectively, and an overall accuracy of 90.00% across all subjects • The achieved performance highlights the effectiveness of the proposed framework in capturing respiratory dynamics in a non-invasive manner, making it suitable for continuous monitoring in clinical and remote healthcare settings. Monitoring of vital signs, such as respiration, is crucial for assessing a person’s health. Traditionally, contact-based methods have been used for this purpose. Although recent advancements in this field have provided soft, skin-like sensors, and flexible wearable devices have improved comfort, these approaches may still impose certain constraints on continuous monitoring, thereby motivating the development of contactless solutions. To overcome these problems, the recent research focus has been shifted toward contactless monitoring techniques, including camera-based, video-based, and waveform-based methods. In camera and video-based methods, the privacy of the subject is at risk. So, in this study, we propose a radar-based contactless technique named amplitude modulation continuous wave radar for vital estimation (AMCWAVE) for monitoring respiratory signals. This technique involves generating and receiving waveforms using amplitude modulation, which is simpler and less complex compared to alternative approaches. Also, the system model is designed using ADALM-Pluto, MATLAB, and Simulink. The proposed approach is non-contact, remote-operable, cost-effective, less complex, and easily configurable to meet user requirements for respiratory pattern detection. Comprehensive experimental evaluation demonstrates the effectiveness of the proposed method, achieving average root mean square error (RMSE) and mean absolute error (MAE) values of 2.03 and 1.69 breaths per minute (BPM), respectively, along with an average accuracy of 90.00% across all subjects. These results confirm a strong agreement between the estimated and reference respiratory rates under varying conditions.
Hari et al. (Wed,) studied this question.