Abstract: Continuous and accurate respiratory volume monitoring during sleep helps to infer the user’s sleep stage and provide clues about some chronic diseases. The existing works mainly focus on the detection and monitoring of respiratory frequency, and lack the means for continuous monitoring of respiratory volume. Therefore, a system named RF-SLEEP which uses commercial Radio Frequency IDentification (RFID) tags to wirelessly sense the respiratory volume during sleep was proposed. The phase value and timestamp data returned by the tag array attached to the chest surface was collected continuously by RF-SLEEP through the reader, and the displacement amounts of different points of the chest caused by breathing were calculated, then the model of relationship between the displacement amounts of different points of the chest and the respiratory volume was constructed by General Regression Neural Network (GRNN), so as to evaluate the respiratory volume of user during sleep. The errors in the calculation of chest displacement caused by the rollover of the user’s body during sleep were eliminated by RF-SLEEP through attaching the double reference tags to the user’s shoulders. The experimental results show that the average accuracy of RF-SLEEP for continuous monitoring of respiratory volume during sleep is 92.49% on average for different users.

Key words: Radio Frequency IDentification (RFID), respiratory volume, sleep, phase value, Generalized Regression Neural Network (GRNN)

摘要： 睡眠期间连续且准确的呼吸量监测有助于推断用户的睡眠阶段以及提供一些慢性疾病的线索。现有工作主要针对呼吸频率进行感知和监测，缺乏对呼吸量进行连续监测的手段。针对上述问题提出了一种基于商用无线射频识别(RFID)标签的无线感知用户睡眠期间呼吸量的系统——RF-SLEEP。RF-SLEEP通过阅读器连续收集附着在胸部表面的标签阵列返回的相位值及时间戳数据，计算出呼吸引起的胸部不同点的位移量，基于广义回归神经网络(GRNN)构建胸部不同点的位移量与呼吸量之间的关系模型，从而实现对用户睡眠期间呼吸量的评估。RF-SLEEP通过在用户肩膀处附着双参考标签，消除用户睡眠期间翻转身体对胸部位移计算造成的误差。实验结果表明，RF-SLEEP对不同用户睡眠期间的呼吸量连续监测的平均精确度为92.49%。

关键词: 无线射频识别, 呼吸量, 睡眠, 相位值, 广义回归神经网络