WiGId: Indoor Group Identification with CSI-Based Random Forest

Author:

Dang Xiaochao,Cao YuanORCID,Hao Zhanjun,Liu YangORCID

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.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference33 articles.

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. RETRACTED: WIFI based human activity recognition using multi-head adaptive attention mechanism;Journal of Intelligent & Fuzzy Systems;2024-04-26

2. Efficient Residual Neural Network for Human Activity Recognition using WiFi CSI Signals;Proceedings of the 2024 9th International Conference on Information and Education Innovations;2024-04-12

3. Attention-Based Hybrid Deep Learning Network for Human Activity Recognition Using WiFi Channel State Information;Applied Sciences;2023-08-01

4. Gestive: Evaluation of Multi-Class Classification Methods for Gesture Recognition to Improve Presentation Experience;Procedia Computer Science;2023

5. WiImg: Pushing the Limit of WiFi Sensing with Low Transmission Rates;2022 19th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON);2022-09-20

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