## 2 Related Work  
In recent years, there have been number studies focusing on human activity recognition for health in smart home. As the basic steps of analysis of human behavior pattern, the goal of activity recognition is to recognize human daily activities from the data collected from sensors deployed into rooms without disturbing the privacy of the inhabitants [8]. Many machine learning based approaches have been well researched in this area. For example, [PERSON] et al. summarized and compared the performance, features and application scenarios of many machine learning algorithms for activity recognition from the sensor data collecting from smart home [9]. [PERSON] et al. proposed a sliding window based SVM (Support Vector Machines) method leading to a real-time performance of activity recognition on streaming sensor data [10]. Some work focus on users' behaviors predication. For example, [PERSON] et al. establishes Markov chain-like models to predict the web resources users need [11]. Based on the study of activity recognition, some researchers have recently carried out many pioneer works on analysis of residents behavior patterns in smart home, especially for the elderly living alone. Some of these works focus on overall evaluation of residents' cognitive ability and life health status according to analysis of data over a period. For example, [PERSON] et al. used approximately two years of sensor data in 18 smart homes to train machine learning algorithms for predicting standard clinical assessment scores of residents [5]. [PERSON] et al. evaluated the health status of elderly people living independently at home based on long-term observations on the usage information of electrical devices during months or years [6]. Moreover, other works focus on anomaly detection from daily or weekly perspective based on the monitoring of a single activity or the combination of behaviors. For example, [PERSON] et al. proposed a novel one-class classification-based approach to detect errors in daily activities of older adults [12], [PERSON] et al. summarized sensor-based daily behaviors into three aspects, namely activity level, mobility level, and nonresponse interval, and developed a support vector data description method to detect abnormal behavioral patterns of elderly people living alone [13]. [PERSON] et al. used a novel computer-vision-based system consisting of inexpensive programmable depth sensors, wearable devices, and smart phones to ensure worker safety in the workplace without violating their privacy [14]. [PERSON] et al. proposed an indicator of predictability based on the cross-entropy measure for the detection of anomalies in elderly daily behavior [15]. [PERSON] et al. used a novel approach to identify appliance activities from smart meter data and extract the pattern of usage, which is used to monitor the health of the household's occupant [16]. As mentioned above, the machine learning method occupies an absolutely dominant position in the work of residents' behavior assessment and anomaly detection. Our approach aims to solve these problems by combining visual analysis with machine learning. By now, many studies have introduced visualization and visual analytics into anomaly detection in application domains, such as cyber security and financial crime. There are also some existing works about human abnormal behavior analysis supported by visualization or visual analytics. In terms of human online communication behaviors, [PERSON] et al. [17; 18] designed several novel visualizations to help users understand the analysis results of anomaly recognition algorithm based on machine learning and analyze the behavior patterns of anomalous persons who are potential threats to society. For increased situational awareness and decision making in emergency response, [PERSON] [19] and [PERSON] [20] visualized people's laws of daily activities in public areas and their movement in emergencies from the data captured by cameras and motion sensors in buildings. On the perspective of human urban life, [PERSON] et al.