Respiration Signal Pattern Analysis for Doppler Radar Sensor with Passive Node and Its Application in Occupancy Sensing of a Stationary Subject

• Published in: Biosensors (Basel) Vol. 15; no. 5; p. 273
• Main Authors: Song, Chenyan, Yavari, Ehsan, Gao, Xiaomeng, Lubecke, Victor M., Boric-Lubecke, Olga
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.04.2025; MDPI
• Subjects: Accuracy; Adult; Algorithms; Biosensing Techniques; cardiorespiratory motion; Communication; Cost control; Doppler radar; Energy conservation; Energy consumption; Forecasts and trends; Humans; HVAC; Infrared cameras; Lighting; Male; Nodes; occupancy sensor; Pattern analysis; Photovoltaic cells; Physiology; Privacy; Quadratures; Radar; Radar detection; Radar meteorology; Radar systems; Receivers & amplifiers; Respiration; respiration signal; Respiratory Rate; Sensitivity; Sensors; Signal processing; Signal Processing, Computer-Assisted; Simulation methods; Smart buildings; Technology adoption; United States; Doppler radar; cardiorespiratory motion; occupancy sensor; respiration signal
• DOI: 10.3390/bios15050273
• ISSN: 2079-6374

Doppler radar node occupancy sensors are promising for applications in smart buildings due to their simple circuits and price advantage compared to quadrature radar sensors. However, single-channel sensitivity limitations may result in low sensitivity and misinterpreted motion rates if the detected subject is at or close to “null” points. We designed and tested a novel method to eliminate such limits, demonstrating that passive nodes can be used to detect a sedentary person regardless of position. This method is based on characteristics of chest motion due to respiration, found via both simulations and experiments based on a sinusoidal model and a more realistic model of cardiorespiratory motion. In addition, respiratory rate variability is considered to distinguish a true human presence from a mechanical target. Sensor node data were collected simultaneously with an infrared camera system, which provided a respiration signal reference, to test the algorithm with 19 human subjects and a mechanical target. The results indicate that a human presence was detected with 100% accuracy and successfully differentiated from a mechanical target in a controlled environment. The developed method can greatly improve the occupancy detection accuracy of single-channel radar-based occupancy sensors and facilitate their adoption in smart building applications.