###### 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. s 2017 7 254 2017 7  
to live a more healthy and environment-friendly life. Furthermore, it can even help conduct health assessment and diagnosis of mobility impaired disease and Alzheimer's disease [3; 4]. Recently, detecting anomalous behaviors for smart health in smart home environment has already caught the eyes of some pioneers. Currently, methods of machine learning, frequently used to study these problems [5; 6; 7], usually include the following steps: (1) activity recognition; (2) classify these activities and select features; and (3) use the methods of machine learning to figure out the value of residents' health conditions. However, the existing methods have many problems. First, ground truth is generally difficult to obtain, so semi-supervised or supervised machine learning has to rely on a large number of manual annotations. Second, most existing methods are used for a few certain types of activity, which ignores the internal relations between humans and their various activities in smart home. Finally, more human intelligence should be invested, as it is required to comprehend unusual behaviors, analyze causes of anomalous behaviors, recognize, etc. In this paper, 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. First, we divide more than 30 activities that happen at home into categories, select different characteristics according to activity property, conduct local outlier factor (LOF) based anomaly detection, blend anomaly detection results of events in different categories, and create an overall anomaly appraisal and anomaly indication of separate items. Then, 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. In the case studies, we use several analyses of interesting anomalous events to show the effectiveness of this tool. The major contribution of this paper includes:  
1. Employing LOF algorithm to anomaly detection of residents' daily activities, which can detect anomalous activities rapidly and effectively. 2.