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.
Highlights
With the development of Internet of things, various powerful sensors are introduced in household devices frequently, which create a safer, more convenient and comfortable smart home environment [1,2].At the same time, massive data related to residents’ living are continuously captured by the sensors, providing opportunities for researchers to explore residents’ behaviors
We design a smart home visual analytics system (SHVis) in which original data and anomaly detection results are blended in an interactive visual analysis interface with multiple views
There have been number studies focusing on human activity recognition for health in smart home
Summary
With the development of Internet of things, various powerful sensors are introduced in household devices frequently, which create a safer, more convenient and comfortable smart home environment [1,2]. Visual analytics technology is introduced to detect and analyze residents’ unusual behaviors for health in smart home, which better combines machine intelligence and artificial intelligence. We design a smart home visual analytics system (SHVis) in which original data and anomaly detection results are blended in an interactive visual analysis interface with multiple views This tool supports interactive exploration of anomaly tendency, analysis of spatial and temporal distribution of daily activities and multi-periods comparative analysis for smart health. It can effectively help analysts analyze, contrast and interpret users’ daily activities and anomalous events to predict resident health information. Behaviors of different dates, and observe detailed information of anomalous events
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