Unconstrained detection of respiration rate and efficiency of sleep with pillow-based sensor array
Unconstrained detection of respiration rate and efficiency of sleep with pillow-based sensor array
复制标题
利用基于枕头的传感器阵列无限制地检测呼吸频率和睡眠效率
DOI：
10.1109/ecticon.2014.6839779
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
T. Kerdcharoen
中科院分区：
文献类型：
--
作者：
S. Lokavee;Worakot Suwansathit;Visasiri Tantrakul;T. Kerdcharoen
使用低成本睡眠监测系统开发了一种无约束实时方法来检测睡眠期间呼吸模式与睡眠效率 (%SE) 之间的关系。该系统由嵌入枕垫上的一系列力敏电阻 (FSR) 传感器、基于低成本 ZigBee 技术的无线网络设备（用于将压力数据数字化并传输到台式计算机或显示设备）、用于分析和分类身体运动的软件组成。典型的呼吸模式是通过胸部运动的升降而导致枕头上头部压力分布的变化来解释的。我们发现，个体在睡眠期间的身体运动频率是不同的。本文的目的是通过使用新开发的基于胸部运动的分类算法对夜间身体运动的频率进行分类来评估呼吸质量和睡眠效率。在这里，我们表明，睡眠质量可以通过呼吸频率的相关性来表征和区分，呼吸频率由胸部运动的提升和降低分开。该传感器系统和无线技术与计算机软件的集成可以使该医疗保健监控系统成为对护理点应用有价值的商业产品。
An unconstrained real-time method to detect the relationship between breathing patterns and sleep efficiency (%SE) during sleep was developed using a low-cost sleep monitoring system. This system consists of an array of force sensitive resistors (FSR) sensors embedded on a pillow-sheet, wireless network devices based on low-cost ZigBee technology to digitize and transmit the pressure data to desktop computer or display devices, software to analyze and classify body movements. The characteristic breathing patterns are explained by the change of the head pressure distribution on the pillow due to the lifting and lowing of the thorax movement. We have found that the body movement frequency is different among individuals during sleep. The aim of this paper was to evaluate the quality of breathing and sleep efficiency by using the frequency of body movement during the night that were classified using a newly developed classification algorithm based on thorax movement. Here we show that quality of sleep can be characterized and distinguished by correlations of breathing rates separated by lifting and lowing of the thorax motion. The integration of this sensor system and wireless technology with computer software could make this healthcare monitoring system a commercial product valuable for point-of-care applications.
影响因子：
4.6
作者：
Xin Zhu;Wenxi Chen;T. Nemoto;Y. Kanemitsu;K. Kitamura;K. Yamakoshi;D. Wei
通讯作者：
Xin Zhu;Wenxi Chen;T. Nemoto;Y. Kanemitsu;K. Kitamura;K. Yamakoshi;D. Wei