A Visual Analytics Approach for Detecting and Understanding Anomalous Resident Behaviors in Smart Healthcare
AbstractWith 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
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
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.
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. Applied Sciences. 2017; 7(3):254.Chicago/Turabian Style
Liao, Zhifang; Kong, Lingyuan; Wang, Xiao; Zhao, Ying; Zhou, Fangfang; Liao, Zhining; Fan, Xiaoping. 2017. "A Visual Analytics Approach for Detecting and Understanding Anomalous Resident Behaviors in Smart Healthcare." Appl. Sci. 7, no. 3: 254.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.