Song Chenyan, Yavari Ehsan, Gao Xiaomeng, Lubecke Victor M, Boric-Lubecke Olga
Adnoviv, Inc., Honolulu, HI 96822, USA.
Aptiv, Inc., Carmel, IN 46032, USA.
Biosensors (Basel). 2025 Apr 27;15(5):273. doi: 10.3390/bios15050273.
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.
与正交雷达传感器相比，多普勒雷达节点占用传感器因其电路简单和价格优势，在智能建筑应用中颇具前景。然而，如果检测对象位于或接近“零点”，单通道灵敏度限制可能导致灵敏度降低和运动速率误判。我们设计并测试了一种消除此类限制的新方法，证明了无源节点可用于检测久坐的人，而不论其位置如何。该方法基于呼吸引起的胸部运动特征，通过基于正弦模型和更真实心肺运动模型的模拟和实验得出。此外，考虑呼吸率变异性以区分真实的人体存在与机械目标。传感器节点数据与提供呼吸信号参考的红外相机系统同时收集，以对19名人类受试者和一个机械目标测试该算法。结果表明，在受控环境中，人体存在检测准确率达100%，并成功与机械目标区分开来。所开发的方法可大大提高基于单通道雷达的占用传感器的占用检测准确率，并促进其在智能建筑应用中的采用。
