Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Oct 30;21(21):7225.
doi: 10.3390/s21217225.

A CSI-Based Human Activity Recognition Using Deep Learning

Parisa Fard Moshiri  1 , Reza Shahbazian  2 , Mohammad Nabati  1 , Seyed Ali Ghorashi  3
Affiliations

A CSI-Based Human Activity Recognition Using Deep Learning

Parisa Fard Moshiri et al. Sensors (Basel). .

Abstract

The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users' inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics of WiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities.

Keywords: Internet of Things; activity recognition; channel state information; deep learning; smart house.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
2D-CNN structure used in this paper.
Figure 2
Figure 2
1D-CNN structure used in this paper.
Figure 3
Figure 3
LSTM structure used in this paper.
Figure 4
Figure 4
BLSTM structure used in this paper.
Figure 5
Figure 5
Configuration for CSI collection.
Figure 6
Figure 6
Generated RGB images: (a) walk; (b) run; (c) fall; (d) lie down; (e) sit down; (f) stand up; (g) bend.
Figure 7
Figure 7
Experimental environment.
Figure 8
Figure 8
Accuracy of different methods implemented on the dataset.
Figure 9
Figure 9
Confusion matrices of proposed methods: (a) LSTM; (b) 1D-CNN; (c) BLSTM; (d) 2D-CNN.
See this image and copyright information in PMC

References

    1. Hassan Q.F. Internet of Things A to Z: Technologies and Applications. 1st ed. Wiley; Hoboken, NJ, USA: 2018. pp. 5–45.
    1. Dey N., Hassanien A.E., Bhatt C., Ashour A.S., Satapathy S.C. Internet of Things and Big Data Analytics toward Next-Generation Intelligence. 1st ed. Volume 30. Springer; New York, NY, USA: 2018. pp. 199–243.
    1. Perera C., Liu C.H., Jayawardena S. The Emerging Internet of Things Marketplace from an Industrial Perspective: A Survey. IEEE Trans. Emerg. Top. Comput. 2015;3:585–598. doi: 10.1109/TETC.2015.2390034. - DOI
    1. Wang F., Feng J., Zhao Y., Zhang X., Zhang S., Han J. Joint Activity Recognition and Indoor Localization with WiFi Fingerprints. IEEE Access. 2019;7:80058–80068. doi: 10.1109/ACCESS.2019.2923743. - DOI
    1. Vlachostergiou A., Stratogiannis G., Caridakis G., Siolas G., Mylonas P. Smart Home Context Awareness Based on Smart and Innovative Cities. Association for Computing Machinery; New York, NY, USA: 2015.

LinkOut - more resources