# An Adaptive Ensemble Approach to Ambient Intelligence Assisted People Search
## 5 Real-World Experiments
Figure 5 shows the system structure of our experimental environment. People were required to wear the iTag when they were working in this environment. Each iTag saved the personal name and face photo of its owner in its memory. WSN nodes were distributed in the physical world. The signal of each node was not strong enough to cover the whole environment, due to their limited wireless communication range. Each WSN node took care of a small area in its vicinity, so that the physical world was sectioned into smaller areas that were under the control of different WSN nodes. The WSN nodes were responsible for searching for the existence, approach, and departure of iTags in its vicinity. Each node scanned nearby iTags through Bluetooth communication continuously. Once it found a new iTag signal, it downloaded the personal name and face photo from iTag. It trained and maintained a local classifier to recognize a small number of nearby people. One person could be detected by multiple WSN nodes at the same time, so that people may be used in different classifiers. Figure 6 shows the processing flow of the robot. When a robot received a command from an operator to search for a person in our experiment environment, the iTag ID or the name of the target was given. The robot enquired to the WSN about the details of this target by supplying this iTag ID or name. The enquiry was broadcasted through the entire WSN by multi-hop routing. If the target was seen by the WSN nodes, they would respond to the robot with a face photo of the target. The robot then exchanged messages with those WSN nodes about the position of target if the target was still in their vicinity. The robot could receive multiple replies if the target was in the range of multiple nodes. Figure 5: System structure of our experimental environment. Figure 6: People search task processing flowcharts: (**a**) Wireless sensor network (WSN) node task; and (**b**) robot process. If the robot did not find the target from a distance, it went to the next region covered by a different WSN node that replied its enquiry. If the target was identified in the view of the robot's camera and verified by second-round recognition at a closer distance, the robot ended the search task and sent an indication to the operator and to the target people. If the robot found that the target did not match the given information, then it ended the task and informed the operator with a failure message. A global map was constructed using a grid-based simultaneous localization and mapping (SLAM) algorithm with Rao-Blackwellized particle filters [29; 30] implemented in an open source software ROS gmapping package. The map was constructed in advance and it was stored on the robot. The coordinates of each WSN node were also marked on the map as anchor nodes for preliminary target localization. The robot used a sonar and laser ranger to scan for environmental information for map building, as well as for locating itself on the map. The robot visited the WSN nodes one by one if it received responses from multiple nodes. When the robot moved into a region that was controlled by different WSN nodes, it collected local lightweight classifiers from the node and combined them with the global classifiers stored in the robot for facial recognition. After the robot reached the control region of a target node, it used its camera to scan the area. The facial recognition process was a two-phase process. After the first round of facial recognition, it moved towards a closer position to take another snapshot of the face and then it performed a second round of recognition to reconfirm the result. In a real-world experiment, we stored the YaleB database and our researchers' face photos, respectively, on 30 iTags. The robot was started from different distances to compare the performance of our method with the single pre-trained SVM classifier. Ten local classifiers and 10 global classifiers were combined; the parameters of the classifiers were the same as in Section 4.1. The experiment results can be found below.