# Location Adaptive Motion Recognition Based on Wi-Fi Feature Enhancement
## 2 Related Work  
Currently, Internet of Things (IoT) has been applied to sensors, devices and software to enhance the performance. One of the most important components for IoT working is protocols and standards, and they can be divided into data protocols (WebSocket, Hyper Text Transfer Protocol and Direct Digital Frequency Synthesis) and network protocols(Wi-Fi, Bluetooth and ZigBee). Given that IoT system is vulnerable to attacks, some latest research [20,21] focuses on the security measures. Because of the limited capabilities of 5G, some research has put forward new advancement in next generation mobile wireless communication (6G), especially connected Intelligence [22,23]. Furthermore, indoor human motion recognition has become a popular field in recent research [24] combing IoT, smart devices, and AI. Meanwhile, with the coverage of optical cables and the popularity of indoor routers, Wi-Fi signals widely exist indoors. Furthermore, water accounts for about 70% of the human body. Thus, the human body has good reflectivity to indoor Wi-Fi signals, and action recognition can be realized by distinguishing the varying signals and corresponding them to human actions [25]. According to the different types of information collected, recognition technology using Wi-Fi signals can be divided into two categories: motion recognition based on RSSI and motion recognition based on CSI.  
Because of the high sensitivity to moving objects, some research works use RSSI information to image different moving objects [26] and RSSI for human motion recognition [27,28]. RSSI will no longer decrease monotonically with the increase in propagation distance due to small-scale shadow fading caused by signal multi-path propagation in an indoor environment, which limits the accuracy of measurement. CSI contains amplitude and phase information, so it has higher sensitivity to individual motion. At present, many achievements have been made in motion recognition based on CSI in Wi-Fi signals, such as e-eyes [29], CARM [19], WiGest [30], and WIMU [31]. These wireless motion recognition studies extract statistical features (such as histogram of signal amplitude [29]) or physical features (such as power distribution of Doppler shift [19]) from the CSI of Wi-Fi signal and associate the features with human motion. However, due to the lack of spatial resolution, the wireless signal also carries adverse environmental information irrelevant to the action, which limits the effect of recognition.  
Therefore, researchers began optimizing the recognition model's cross-domain generalization ability, such as using transfer learning and confrontational learning [32,33,34]. However, these methods need to add new data sets to the recognition model every time, which greatly increases the training workload. With the rise of deep learning, many attempts have been made to improve the generalization performance of Wi-Fi action recognition. For example, Widar 3.0 [35] mine the characteristics extracted by CSI in spatial dimension and time dimension, respectively, by using convolutional neural network (CNN) and recurrent neural network (RNN) to distinguish common human-computer interaction actions. STFNets [36] proposed a new neural network construction module: a short-time Fourier neural network. It directly learns the characteristics in the frequency domain of various sensor inputs. EI [32] designed a more complex network structure, defined a new loss function, and directly used the new model to learn the common expression of signal features in different environments based on making full use of unlabeled data. Therefore, it is necessary to use deep learning to improve generalization ability while avoiding more complex network structures from slowing down or even hindering training and consuming training data excessively [37].