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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. 

Occupancy sensors are electronic devices used to detect the presence of people in monitored areas, and the output of these sensors can be used to optimize lighting control, heating and ventilation control, and real-estate utilization. Testing methods already exist for certain types of occupancy sensors (e.g., passive infrared) to evaluate their relative performance, allowing manufacturers to report coverage patterns for different types of motion. However, the existing published techniques are mostly tailored for passive-infrared sensors and therefore limited to evaluation of large motions, such as walking and hand movement. Here we define a characterization technique for a Doppler radar occupancy sensor based on detecting a small motion representing human breathing, using a well-defined readily reproducible target. The presented technique specifically provides a robust testing method for a single-channel continuous wave Doppler-radar based occupancy sensor, which has variation in sensitivity within each wavelength of range. By comparison with test data taken from a human subject, we demonstrate that the mobile target provides a reproducible alternative for a human target that better accounts for the impact of sensor placement. This characterization technique enables generation of coverage patterns for breathing motion for single-channel continuous wave Doppler radar-based occupancy sensors. 

Abstract Cognitive buildings use data on how occupants respond to the built environment to proactively make occupant-centric adjustments to lighting, temperature, ventilation, and other environmental parameters. However, sensors that unobtrusively and ubiquitously measure occupant responses are lacking. Here we show that Doppler-radar based sensors, which can sense small physiological motions, provide accurate occupancy detection and estimation of vital signs in challenging, realistic circumstances. Occupancy was differentiated from an empty room over 93% of the time in a 3.4 m × 8.5 m conference room with a single sensor in both wall and ceiling-mounted configurations. Occupancy was successfully detected while an occupant was under the table, visibly blocked from the sensor, a scenario where infrared, ultrasound, and video-based occupancy sensors would fail. Heart and respiratory rates were detected in all seats in the conference room with a single ceiling-mounted sensor. The occupancy sensor can be used to control HVAC and lighting with a short, 1–2 min delay and to provide information for space utilization optimization. Heart and respiratory rate sensing could provide additional feedback to future human-building interactive systems that use vital signs to determine how occupant comfort and wellness is changing with time.