In this study, we present a non-contact respiratory anomaly detection method using incoherent light-wave signals reflected from the chest of a mechanical robot that can breathe like human beings. In comparison to existing radar and camera-based sensing systems for vitals monitoring, this technology uses only a low-cost ubiquitous light source (e.g., infrared light emitting diode) and sensor (e.g., photodetector). This light-wave sensing (LWS) system recognizes different breathing anomalies from the variations of light intensity reflected from the chest of the robot within a 0.5m-1.5m range. The anomaly detection model demonstrates up to 96.6% average accuracy in classifying 7 different types of breathing data using machine learning. The model can also detect faulty data collected by the system that does not contain breathing information. The developed system can be utilized at home or healthcare facilities as a smart, non-contact and discreet respiration monitoring method.

本研究提出了一种利用不连续光波信号从机械机器人胸部反射出来的方法来检测非接触式呼吸异常，与现有的用于监测生命体征的雷达和摄像头感应系统相比，该技术仅使用低成本的常见光源（如红外线发光二极管）和传感器（如光电二极管），该光波感应系统能够识别机器人胸部反射光强的变化中的不同呼吸异常。呼吸异常检测模型使用机器学习方法在分类7种不同呼吸数据时达到了96.6%的平均准确率，该模型还能够检测系统收集的不包含呼吸信息的错误数据。这个开发的系统可以作为一个智能、非接触和隐蔽的呼吸监测方法在家庭或医疗设施中使用。

利用反射红外光波信号进行呼吸异常检测