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Appl. Sci. 2017, 7(3), 254; doi:10.3390/app7030254

A Visual Analytics Approach for Detecting and Understanding Anomalous Resident Behaviors in Smart Healthcare

1
School of Software Engineering, Central South University, Changsha 410075, China
2
Data Center Consolidation (Beijing), Industrial and Commercial Bank of China, Beijing 100000, China
3
School of Information Science and Engineering, Central South University, Changsha 410075, China
4
Division of Health & Social Care Research, Faculty of Life Sciences &Medicine, King’s College London, London WC2R 2LS, UK
5
Information Management Department, Hunan University of Finance and Economics, Changsha 410083, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Wenbing Zhao
Received: 31 December 2016 / Revised: 25 February 2017 / Accepted: 27 February 2017 / Published: 7 March 2017
(This article belongs to the Special Issue Smart Healthcare)
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Abstract

With the development of science and technology, it is possible to analyze residents’ daily behaviors for the purpose of smart healthcare in the smart home environment. Many researchers have begun to detect residents’ anomalous behaviors and assess their physical condition, but these approaches used by the researchers are often caught in plight caused by a lack of ground truth, one-sided analysis of behavior, and difficulty of understanding behaviors. In this paper, we put forward a smart home visual analysis system (SHVis) to help analysts detect and comprehend unusual behaviors of residents, and predict the health information intelligently. Firstly, the system classifies daily activities recorded by sensor devices in smart home environment into different categories, and discovers unusual behavior patterns of residents living in this environment by using various characteristics extracted from those activities and appropriate unsupervised anomaly detection algorithm. Secondly, on the basis of figuring out the residents’ anomaly degree of every date, we explore the daily behavior patterns and details with the help of several visualization views, and compare and analyze residents’ activities of various dates to find the reasons why residents act unusually. In the case study of this paper, we analyze residents’ behaviors that happened over two months and find unusual indoor behaviors and give health advice to the residents. View Full-Text
Keywords: smart healthcare; user behaviors; anomaly detection; visual analytics smart healthcare; user behaviors; anomaly detection; visual analytics
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MDPI and ACS Style

Liao, Z.; Kong, L.; Wang, X.; Zhao, Y.; Zhou, F.; Liao, Z.; Fan, X. A Visual Analytics Approach for Detecting and Understanding Anomalous Resident Behaviors in Smart Healthcare. Appl. Sci. 2017, 7, 254.

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