## 6 Case Study  
The data set consists of the information of an adult living in a smart domestic environment collected by various sensors. Based on the original data, we hope to use this system to analyze the daily behavior of the resident, and find out the date of anomaly behavior pattern and the reason for the anomaly.  
The system first analyzes 30 kinds of residents' activities and classifies activities according to the activity classification methods mentioned above, and then determines the strategy of calculating the anomaly for each activity. Furthermore, we can filter activities based on activity features to decide which activities can be used for the calculation of anomaly. Figure 1(1,2) shows the anomaly degree worked out using three methods of activity anomaly measurement and the weight of each activity used for calculations. Consequently, we can see that some daily activities with long duration and activities with strong regularity exert great influence on the measurement of anomaly. This regularity is in accord with the general knowledge of inhabitant's activity. It can be seen in Figure 1(3) that most dates are gathered together, indicating that inhabitant's activities of most dates correspond to their living habits, and only a few dates projected to the edge are judged as anomaly. The anomaly degree on 30 August, 2 August and 1 September arrives at the highest with the projective point at the remotest place from the cluster.  
The start time and duration of inhabitants' daily activities can be seen from the Gantt Chart of Figure 1(4). We choose 2 August, 30 August and 14 August for comparison and analysis. In Figure 1(5), we can see that the resident's sleeping activity was quite normal on 2 August. However, the resident lacked the activity of bathing; the time of watching TV was longer than the average value; he/she did  
Figure 6: Space Radar Map: One circle refers to one room, and each circle represents the three-day activity selected in the other views, from inside to outside. Sectors with different colors, besides the circle, refer to sensor-recorded duration triggered by different activity.  
not take medicine as usually, and did not take a rest. On 30 August, the resident slept too short, while he/she slept a long time on the sofa, and did not take medicine both in the morning and in the evening. Regarding 14 August, it was a relatively normal day, on which the behavior pattern of the resident had no obvious anomaly. It can be seen in Figure 5 that the resident's activity frequency showed great discrepancy with the average frequency on the radar map at the two days judged as anomaly. Especially, the resident had evident fluctuation in the times of going to toilet, leaving home, taking rest and medicine, and using phone on 30 August. The patient also presented such behavior phenomenon on 2 August, while the normal date of 14 August was nearer to the general activity routines, which accorded with the resident's daily behavioral habits.  
Figure 6 shows the duration of sensor at different places triggered by different activities, and the value does not refer to the activity duration of the resident at this place, as the resident would not trigger the sensor at that place after he/she slept in the bedroom. Triggering the sensor for many times will make the duration of triggering sensor longer, indicating the resident may have a poor sleep and get up many times. It can be concluded from the sleeping activity that the resident had a long sleep on 2 and 14 August, but the duration of triggering sensor was quite short, which proved a high quality of sleeping for the resident. The sleeping time on 30 August was quite short, but duration of triggering sensor is relatively long, which explained that the resident slept short and poor.