# Capturing Features and Performing Human Detection from Human Gaits Using RFID  
[PERSON]  
1 School of Software, Xinjiang University, Uramqi 830091, China 11 School of Information Engineering, Shanghai Maritime University, Shanghai 201306, China 11 School of Software, Xinjiang University, Uramqi 830091,  
Recently, person detection systems based on radio frequency identification (RFID) have attracted a lot of research interest because of their passive and contact-free characteristics. This means that the user does not need to carry any device to carry out the person detection, and there is no risk of violating the user's privacy. Although RFID has shown considerable advantages in person detection, these identifications still have multiple limitations to some extent, which hinders the popularization of RFID in daily life. First of all, most systems collect human gait data by using the same walking pattern for a human in a normal state, but this is not always the case in real life. Even for the same user, due to changes in his mood or state, his walking style is always changing, and the data for a single state cannot be used as the standard, which leads to a failure to detect complete and stable gait data. Secondly, it takes a lot of manpower to obtain the gait data of each person, and a large amount of data is needed for training to support the expected results, which is very time consuming. Thirdly, most existing systems focus on personnel recognition, which can accurately identify the characteristics of each person, but also requires the user to walk in a certain posture and cannot accurately identify when multiple people pass through continuously. Most systems cannot be applied well in life. In order to solve the above problems, we propose the person detection system RF-Detection, which is based on RFID. Compared with traditional technologies, ultra-high-frequency (UHF) RFID tags are inexpensive, small, flexible, and can transmit energy without the need for additional equipment. As shown in Results, we place the tag group antenna face-to-face, and the user walks between the tag group and the antenna. RF-Detection will map the captured signal to the gait feature executed. There are three challenges in designing the RF-Detection system. Challenge 1: RF-Detection obtains unique characteristics of user gait data from RFID tags, which will often change with changes in users' emotions and walking patterns. Moreover, due to the multipath effect of the environment, additional noise interference will be caused to the collected data, making it difficult to carry out accurate and effective identification. Therefore, these characteristics need to be supported by a more robust system, and the data should be more stable. How can we detect users more accurately when they are walking? To tackle this challenge, we use multiple RFID tags to extract more robust user gait data by extending the walking distance. In order to prevent interference from upper body movements such as the hands, we repeatedly tested the tag position to make it highly uniform and reduce the influence on the received signal. Finally, in order to obtain stable gait features, we designed a neural network module for training. Challenge 2: How can we use limited data to detect new users without retraining? To tackle this challenge, we first divided the step length of the user group. From [4], we can see that the step length has a strong correlation with height. After that, the step characteristics of different populations were collected by experiments, which reduced the amount of data collection to a certain extent. Then, we used the method of data augmentation to further expand the data set, so as to reduce the data collection work. Finally, we fine tune the neural network to adapt the trained network to new user recognition. Challenge 3: How can we accurately detect the specific number of users when multiple users pass through in succession?