Hayes Tamara L, Riley Thomas, Pavel Misha, Kaye Jeffrey A
Biomedical Engineering Department (BME) and the Oregon Center for Aging and Technology (ORCATECH), Oregon Health & Science University (OHSU), 3303 SW Bond Avenue, Portland, OR 97239, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:2147-50. doi: 10.1109/IEMBS.2010.5628022.
Disrupted sleep patterns are a significant problem in the elderly, leading to increased cognitive dysfunction and risk of nursing home placement. A cost-effective and unobtrusive way to remotely monitor changing sleep patterns over time would enable improved management of this important health problem. We have developed an algorithm to derive sleep parameters such as bed time, rise time, sleep latency, and nap time from passive infrared sensors distributed around the home. We evaluated this algorithm using 404 days of data collected in the homes of 8 elderly community-dwelling elders. Data from this algorithm were highly correlated to ground truth measures (bed mats) and were surprisingly robust to variability in sensor layout and sleep habits.
睡眠模式紊乱是老年人面临的一个重大问题，会导致认知功能障碍加剧以及入住养老院的风险增加。一种经济高效且不引人注意的方法，用于随时间远程监测睡眠模式的变化，将有助于更好地管理这一重要的健康问题。我们开发了一种算法，可从分布在家庭周围的被动红外传感器中得出诸如就寝时间、起床时间、入睡潜伏期和午睡时间等睡眠参数。我们使用在8位居住在社区的老年人家中收集的404天数据对该算法进行了评估。该算法得出的数据与地面真值测量（床垫）高度相关，并且令人惊讶的是，对传感器布局和睡眠习惯的变化具有很强的鲁棒性。
