包到预测:一个不显眼的机制,用于识别粗粒度的睡眠模式与WiFi MAC层流量
Dheryta Jaisinghani1, Nishtha Phutela2
1Department of Computer Science, College of Humanities, Arts, and Sciences, University of Northern Iowa, Cedar Falls, IA 50613, USA.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究介绍了一种使用WiFi流量来检测大学生睡眠模式的非接触式方法. 包到预测 (P2P) 系统准确地识别睡眠和清醒期间,而不需要可穿戴传感器.
科学领域:
- 生物医学工程 生物医学工程
- 计算机科学 计算机科学
- 睡眠科学 睡眠科学
背景情况:
- 充足的睡眠对认知功能至关重要.
- 三分之一的美国成年人经历了严重的睡眠剥夺.
- 现有的睡眠监测方法往往是侵入性的或昂贵的.
研究的目的:
- 为大学生开发一种非接触式,不引人注目的睡眠模式检测系统.
- 为了利用WiFi的MAC层流量来预测睡眠和清醒期.
- 评估机器学习模型在自动化睡眠模式识别中的有效性.
主要方法:
- 包到预测 (P2P) 系统旨在分析WiFi MAC层流量.
- 手动验证证实了从这些数据中提取睡眠模式的可行性.
- 训练了六个机器学习模型,并对性能进行了比较.
主要成果:
- K-最近邻近模型实现了最高的准确性.
- 在训练数据上达到87%的准确性,在测试数据上达到83%.
- 展示了WiFi流量分析用于睡眠监控的潜力.
结论:
- 该P2P系统提供了一种新的,无传感器的睡眠模式检测方法.
- 机器学习,特别是K-最近的邻居,有效地从WiFi数据中预测睡眠/清醒状态.
- 这项技术对在学术环境中可访问的睡眠研究和监测有影响.
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