Sensor-Generated Time Series Events: A Definition Language

Sensors

Aurea Anguera²  Juan A. Lara¹  David Lizcano¹  Maria Aurora Martínez¹ 

[1] Madrid Open University, Camino de la Fonda 20, Collado Villalba, Madrid 28400, Spain; E-Mails:;Technical School of Computer Science, Universidad Politécnica de Madrid, Carretera de Valencia km 7, Madrid 28031, Spain; E-Mail:

关键词: sensors;    time series;    data mining;    event;    event definition language;    stabilometry;   

DOI  :  10.3390/s120911811

来源: mdpi

【 摘 要 】

There are now a great many domains where information is recorded by sensors over a limited time period or on a permanent basis. This data flow leads to sequences of data known as time series. In many domains, like seismography or medicine, time series analysis focuses on particular regions of interest, known as events, whereas the remainder of the time series contains hardly any useful information. In these domains, there is a need for mechanisms to identify and locate such events. In this paper, we propose an events definition language that is general enough to be used to easily and naturally define events in time series recorded by sensors in any domain. The proposed language has been applied to the definition of time series events generated within the branch of medicine dealing with balance-related functions in human beings. A device, called posturograph, is used to study balance-related functions. The platform has four sensors that record the pressure intensity being exerted on the platform, generating four interrelated time series. As opposed to the existing ad hoc proposals, the results confirm that the proposed language is valid, that is generally applicable and accurate, for identifying the events contained in the time series.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.