Department of Industrial Engineering and Mathematical Sciences, Polytechnic University of Marche, 60131 Ancona, Italy.
Sensors (Basel). 2019 Aug 31;19(17):3773. doi: 10.3390/s19173773.
A simple sleep monitoring measurement method is presented in this paper, based on a simple, non-invasive motion sensor, the Passive InfraRed (PIR) motion sensor. The easy measurement set-up proposed is presented and its performances are compared with the ones provided by a commercial, ballistocardiographic bed sensor, used as reference tool. Testing was conducted on 25 nocturnal acquisitions with a voluntary, healthy subject, using the PIR-based proposed method and the reference sensor, simultaneously. A dedicated algorithm was developed to correlate the bed sensor outputs with the PIR signal to extract sleep-related features: sleep latency (SL), sleep interruptions (INT), and time to wake (TTW). Such sleep parameters were automatically identified by the algorithm, and then correlated to the ones computed by the reference bed sensor. The identification of these sleep parameters allowed the computation of an important, global sleep quality parameter: the sleep efficiency (SE). It was calculated for each nocturnal acquisition and then correlated to the SE values provided by the reference sensor. Results show the correlation between the SE values monitored with the PIR and the bed sensor with a robust statistic confidence of 4.7% for the measurement of SE (coverage parameter k = 2), indicating the validity of the proposed, unobstructive approach, based on a simple, small, and low-cost sensor, for the assessment of important sleep-related parameters.
本文提出了一种简单的睡眠监测测量方法,该方法基于简单的非侵入式运动传感器,即被动式红外(PIR)运动传感器。本文提出了一种简单的测量设置,并将其性能与作为参考工具的商用心动球传感器的性能进行了比较。使用基于 PIR 的建议方法和参考传感器,对 25 名自愿的健康受试者进行了 25 次夜间采集测试。开发了一种专用算法,将床传感器的输出与 PIR 信号相关联,以提取与睡眠相关的特征:睡眠潜伏期 (SL)、睡眠中断 (INT) 和醒来时间 (TTW)。该算法自动识别这些睡眠参数,然后将其与参考床传感器计算的参数相关联。这些睡眠参数的识别允许计算一个重要的整体睡眠质量参数:睡眠效率 (SE)。为每个夜间采集计算 SE,并将其与参考传感器提供的 SE 值相关联。结果表明,PIR 和床传感器监测的 SE 值之间存在相关性,测量 SE 的稳健统计置信度为 4.7%(覆盖参数 k = 2),这表明基于简单、小巧、低成本传感器的非侵入式方法的有效性,用于评估重要的睡眠相关参数。