Suppr超能文献

一种使用称重传感器来确定静息-活动模式的受试者状态检测方法。

A subject state detection approach to determine rest-activity patterns using load cells.

作者信息

Adami Adriana M, Adami Andre G, Schwarz Gilmar, Beattie Zachary T, Hayes Tamara L

机构信息

University of Caxias do sul, Caxias, RS 95070-560, Brasil.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:204-7. doi: 10.1109/IEMBS.2010.5627935.

Abstract

A patient's sleep/wake schedule is an important step underlying clinical evaluation of sleep-related complaints. Aspects related to timing of a person's sleep routine provide important clues regarding diagnosis and treatments. Solutions for sleep complaints may sometimes rely solely on changes in habits and life style, based on what is learned from daily rest-activity patterns. This paper describes an approach for determining two states, in-bed and out-of-bed, using load cells under the bed. These states are important because they can help characterize rest-activity patterns at nighttime or detect bed exits in hospitals or nursing homes. The information derived from the load cells is valuable as an objective and continuous measure of daily patterns, and it is particularly valuable in sleep studies in populations who would not be able to remember specific hours to complete sleep diaries. The approach is evaluated on data collected in a laboratory experiment, in a sleep clinic, and also on data collected from residents of an assisted-living facility.

摘要

患者的睡眠/觉醒时间表是对与睡眠相关的主诉进行临床评估的重要基础步骤。与个人睡眠习惯时间相关的方面为诊断和治疗提供了重要线索。基于从日常休息-活动模式中了解到的情况,睡眠主诉的解决方案有时可能仅依赖于习惯和生活方式的改变。本文描述了一种使用床下称重传感器来确定两种状态(在床上和不在床上)的方法。这些状态很重要,因为它们有助于刻画夜间的休息-活动模式,或检测医院或养老院中的患者离床情况。从称重传感器获得的信息作为日常模式的客观且连续的度量是很有价值的,并且在那些无法记住特定时间来完成睡眠日记的人群的睡眠研究中尤其有价值。该方法在实验室实验、睡眠诊所收集的数据以及从辅助生活设施的居民那里收集的数据上进行了评估。

相似文献

2
Detection of movement in bed using unobtrusive load cell sensors.使用非侵入式称重传感器检测床上的活动。
IEEE Trans Inf Technol Biomed. 2010 Mar;14(2):481-90. doi: 10.1109/TITB.2008.2010701. Epub 2009 Jan 20.

引用本文的文献

1
A Gaussian model for movement detection during sleep.一种用于睡眠期间运动检测的高斯模型。
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:2263-6. doi: 10.1109/EMBC.2012.6346413.
2
Classification of lying position using load cells under the bed.利用床下称重传感器对卧位进行分类。
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:474-7. doi: 10.1109/IEMBS.2011.6090068.

本文引用的文献

1
Classification of breathing events using load cells under the bed.使用床下称重传感器对呼吸事件进行分类。
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:3921-4. doi: 10.1109/IEMBS.2009.5333548.
2
Detection of movement in bed using unobtrusive load cell sensors.使用非侵入式称重传感器检测床上的活动。
IEEE Trans Inf Technol Biomed. 2010 Mar;14(2):481-90. doi: 10.1109/TITB.2008.2010701. Epub 2009 Jan 20.
6
The role of actigraphy in sleep medicine.活动记录仪在睡眠医学中的作用。
Sleep Med Rev. 2002 Apr;6(2):113-24. doi: 10.1053/smrv.2001.0182.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验