Demo abstract: Demonstration of the FABER system for fine-grained recognition of abnormal behaviors

2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) Pub Date : 2015-03-23 DOI:10.1109/PERCOMW.2015.7134021

Gabriele Civitarese, Z. H. Janjua, Daniele Riboni, C. Bettini

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Abstract

The life expectancy is rapidly growing in many countries. According to the United Nations, the percentage of elderly population will rise from 5% in 2013 to 11% in 2050. The increasing aging of the population implies an increase of age-related diseases, and an increase in terms of health-care costs. The innovations introduced by pervasive computing, and in particular by sensor-based activity monitoring methods, can be exploited to early detect the onset of health issues. For this reason, we devised a novel method to recognize anomalies that a senior performs during the execution of activities of daily living, based on data acquired from unobtrusive sensors deployed at home. The objective is to support the clinicians in the early diagnosis of neurodegenerative diseases, providing them with fine-grained information about abnormal behaviors. In this paper, we present a demonstration of the method, based on a graphical tool that simulates the execution of activities and abnormal behaviors of an elderly person in a sensor-rich smart home.

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演示摘要:展示了FABER系统对异常行为的细粒度识别

许多国家的预期寿命正在迅速增长。根据联合国的数据，老年人口的比例将从2013年的5%上升到2050年的11%。人口日益老龄化意味着与年龄有关的疾病增加，保健费用增加。普及计算带来的创新，特别是基于传感器的活动监测方法带来的创新，可用于及早发现健康问题的发生。基于这个原因，我们设计了一种新的方法来识别老年人在执行日常生活活动时的异常行为，该方法基于部署在家中的不显眼的传感器获得的数据。目的是支持临床医生对神经退行性疾病的早期诊断，为他们提供有关异常行为的细粒度信息。在本文中，我们基于图形工具展示了该方法的演示，该工具模拟了老年人在传感器丰富的智能家居中的活动执行和异常行为。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)

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