WiGId: Indoor Group Identification with CSI-Based Random Forest

Abstract

Human identity recognition has a wide range of application scenarios and a large number of application requirements. In recent years, the technology of collecting human biometrics through sensors for identification has become mature, but this kind of method needs additional equipment as assistance, which cannot be well applied to some scenarios. Using Wi-Fi for identity recognition has many advantages, such as no additional equipment as assistance, not affected by temperature, humidity, weather, light, and so on, so it has become a hot topic of research. The methods of individual identity recognition have been more mature; for example, gait information can be extracted as features. However, it is difficult to identify small-scale (2–5) group personnel at one time, and the tasks of fingerprint storage and classification are complex. In order to solve this problem, this paper proposed a method of using the random forest as a fingerprint database classifier. The method is divided into two stages: the offline stage trains the random forest classifier through the collected training data set. In the online phase, the real-time data collected are input into the classifier to get the results. When extracting channel state information (CSI) features, multiple people are regarded as a whole to reduce the difficulty of feature selection. The use of random forest classifier in classification can give full play to the advantages of random forest, which can deal with a large number of multi-dimensional data and is easy to generalize. Experiments showed that WiGId has good recognition performance in both LOS (line of sight) and N LOS (None line of sight) environments.

Recognizing and Counting Freehand Exercises Using Ubiquitous Cellular Signals

“…WiFi-based motion tracking relies on RSSI or the more detailed CSI to realize activity detection [ 13 , 27 , 28 , 29 , 30 , 31 ], gesture recognition [ 32 , 33 , 34 ] and tracking [ 35 , 36 , 37 , 38 , 39 , 40 ]. WiFit [ 41 ] enables people to practice three kinds of freehand exercises with the body on the line-of-sight between Wi-Fi transceivers.…”

SlideAugment: A Simple Data Processing Method to Enhance Human Activity Recognition Accuracy Based on WiFi

“…A study presented by Tsinghua University shows that in complex dynamic environment, the channel state information (CSI) from WiFi performs better than RSSI [ 6 ] in the field of indoor location. Since then, the channel state information from physical layer has been explored in activity recognition [ 7 ], indoor location [ 8 ], gesture recognition [ 9 , 10 ], and user identification [ 11 ]. The WiFi signals can be used to perform fall detection [ 12 ] and detect risky driving behavior [ 13 ].…”