Joint Activity Recognition and Indoor Localization With WiFi Fingerprints

Recent years have witnessed the rapid development in the research topic of WiFi sensing that automatically senses human with commercial WiFi devices. Past work falls into two major categories, i.e., activity recognition and the indoor localization. The former work utilizes WiFi devices to recognize...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.80058-80068
Main Authors: Wang, Fei, Feng, Jianwei, Zhao, Yinliang, Zhang, Xiaobin, Zhang, Shiyuan, Han, Jinsong
Format: Article
Language:English
Subjects:
1D convolutional neural networks
Ablation
Activity recognition
Artificial neural networks
Clocks
Convolutional neural networks
CSI fingerprints
Fingerprint recognition
Fingerprints
human–computer interaction
indoor localization
Indoor navigation
Localization
Location based services
Machine learning
Protocol (computers)
R&D
Research & development
Task analysis
Walking
Wireless fidelity
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Summary:Recent years have witnessed the rapid development in the research topic of WiFi sensing that automatically senses human with commercial WiFi devices. Past work falls into two major categories, i.e., activity recognition and the indoor localization. The former work utilizes WiFi devices to recognize human daily activities such as smoking, walking, and dancing. The latter one, indoor localization, can be used for indoor navigation, location-based services, and through-wall surveillance. The key rationale behind WiFi sensing is that people behaviors can influence the WiFi signal propagation and introduce specific patterns into WiFi signals, called WiFi fingerprints, which can be further explored to identify human activities and locations. In this paper, we propose a novel deep learning framework for joint activity recognition and indoor localization task using WiFi channel state information (CSI) fingerprints. More precisely, we develop a system running standard IEEE 802.11n WiFi protocol and collect more than 1400 CSI fingerprints on 6 activities at 16 indoor locations. Then we propose a dual-task convolutional neural network with one-dimensional convolutional layers for the joint task of activity recognition and indoor localization. The experimental results and ablation study show that our approach achieves good performances in this joint WiFi sensing task. Data and code have been made publicly available at https://github.com/geekfeiw/apl .
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2923743