国家杰出青年科学基金(62125203); 国家自然科学基金(61932013, 62172236, 61972201); 江苏省重点研发计划重大科技示范项目(BE2022798); 江苏省优秀青年基金(BK20220105)
睡眠过程中的人体呼吸波形检测对于智慧康养和医疗保健应用至关重要, 结合不同的呼吸波形模式可以实现睡眠质量分析和呼吸系统疾病检测. 传统基于接触式设备的呼吸感知方法会给用户带来诸多不便, 与其相比, 非接触式感知方法更适合进行连续性监测. 然而, 在睡眠过程中由于设备部署、睡眠姿态以及人体运动都具有随机性, 严重限制了非接触呼吸感知方案在日常生活中的使用. 为此, 提出一种基于脉冲超宽带(impulse radio-ultra wide band, IR-UWB)的睡眠状态下人体呼吸波形检测方法. 所提方法以睡眠状态下人体呼吸时其胸腔起伏导致无线脉冲信号传播路径的周期性变化为基础, 进而生成细粒度的人体呼吸波形, 实现呼吸波形的实时输出以及呼吸速率的高精度估计. 首先, 为了从接收无线射频信号中获取人体呼吸时的胸腔位置, 提出一个基于IR-UWB信号的呼吸能量比指标来实现目标位置估计. 然后, 通过提出基于I/Q复平面的向量投影方法和基于呼吸向量圆周位置的投影信号选择方法, 从反射信号中提取到人体呼吸特征波形. 最后, 结合变分编码器-解码器网络来实现睡眠状态下细粒度的呼吸波形恢复. 通过在不同条件下进行大量实验测试, 结果表明所提方法在睡眠状态下监测的人体呼吸波形与商用呼吸带获得的真实波形高度相似, 其呼吸速率的平均估计误差为0.229 bpm, 可实现高精度的睡眠状态下人体呼吸波形检测.
The detection of the human respiration waveform in the sleep state is crucial for applications in intelligent health care as well as medical and healthcare in that different respiration waveform patterns can be examined to analyze sleep quality and monitor respiratory diseases. Traditional respiration sensing methods based on contact devices cause various inconveniences to users. In contrast, contactless sensing methods are more suitable for continuous monitoring. However, the randomness of the device deployment, sleep posture, and human motion during sleep severely restrict the application of contactless respiration sensing solutions in daily life. For this reason, the study proposes a detection method for the human respiration waveform in the sleep state based on impulse radio-ultra wide band (IR-UWB). On the basis of the periodic changes in the propagation path of the wireless pulse signal caused by the undulation of the human chest during respiration in the sleep state, the proposed method generates a fine-grained human respiration waveform and thereby achieves the real-time output of the respiration waveform and high-precision respiratory rate estimation. Specifically, to obtain the position of the human chest during respiration from the received wireless radio-frequency (RF) signals, this study proposes the indicator respiration energy ratio based on IR-UWB signals to estimate the target position. Then, it puts forward a vector projection method based on the in-phase/quadrature (I/Q) complex plane and a method of projection signal selection based on the circumferential position of the respiration vector to extract the characteristic human respiration waveform from the reflected signal. Finally, a variational encoder-decoder network is leveraged to achieve the fine-grained recovery of the respiratory waveform in the sleep state. Extensive experiments and tests are conducted under different conditions, and the results show that the human respiration waveforms monitored by the proposed method in the sleep state are highly similar to the actual waveforms captured by commercial respiratory belts. The average error of the proposed method in estimating the human respiratory rate is 0.229 bpm, indicating that the method can achieve high-precision detection of the human respiration waveform in the sleep state.