School of Computer Science, Reichman University (IDC Herzliya), Herzliya 4610101, Israel.
Magic Lab, Department of Industrial Engineering and Management, Ben Gurion University of the Negev, P.O. Box 653, Be'er-Sheva 8410501, Israel.
Sensors (Basel). 2022 Aug 22;22(16):6306. doi: 10.3390/s22166306.
Analysing human physiological data allows access to the health state and the state of mind of the subject individual. Whenever a person is sick, having a panic attack, happy or scared, physiological signals will be different. In terms of physiological signals, we focus, in this manuscript, on monitoring breathing patterns. The scope can be extended to also address heart rate and other variables. We describe an analysis of breathing rate patterns during activities including resting, walking, running and watching a movie. We model normal breathing behaviours by statistically analysing signals, processed to represent quantities of interest. We consider moving maximum/minimum, the amplitude and the Fourier transform of the respiration signal, working with different window sizes. We then learn a statistical model for the basal behaviour, per individual, and detect outliers. When outliers are detected, a system that incorporates our approach would send a visible signal through a smart garment or through other means. We describe alert generation performance in two datasets-one literature dataset and one collected as a field study for this work. In particular, when learning personal rest distributions for the breathing signals of 14 subjects, we see alerts generated more often when the same individual is running than when they are tested in rest conditions.
分析人类生理数据可以了解个体的健康状况和心理状态。当一个人生病、恐慌发作、高兴或害怕时，生理信号会有所不同。在生理信号方面，我们在本文中专注于监测呼吸模式。范围可以扩展到心率和其他变量。我们描述了对休息、散步、跑步和看电影等活动期间呼吸率模式的分析。我们通过对代表感兴趣数量的信号进行统计分析来模拟正常的呼吸行为。我们考虑移动最大值/最小值、呼吸信号的幅度和傅里叶变换，使用不同的窗口大小。然后，我们为每个人学习基础行为的统计模型，并检测异常值。当检测到异常值时，包含我们方法的系统将通过智能服装或其他方式发出可见信号。我们在两个数据集（一个文献数据集和一个为这项工作收集的现场研究数据集）中描述了警报生成性能。特别是，当学习 14 个受试者的呼吸信号的个人休息分布时，我们发现当同一个人跑步时生成的警报比在休息条件下测试时更频繁。
