Human Activity Recognition System Using WiFi Sensing and Deep Learning

Authors

  • Nazmia Kurniawati Politeknik Negeri Jakarta
  • Shita Fitria Nurjihan Politeknik Negeri Jakarta

DOI:

https://doi.org/10.12928/biste.v5i4.9408

Keywords:

CSI, Deep Learning, Wi-Fi

Abstract

Human activity recognition systems can be used for various purposes such as monitoring, authentication, and telemedicine. In this research, a non-invasive, high privacy, easy to implement, and affordable human activity recognition system based on WiFi and deep learning is developed. Sixteen activities; including upper body, lower body, and whole body movement; were recognized by utilizing Channel State Information (CSI) contained in the WiFi signal. Measurements were carried out in an empty room with dimensions of 6*8 m with the distance between the transmitter and receiver being 1, 3 and 6 meters from the subject. Google Teachable Machine is used to recognize activities carried out. From the measurement result, the accuracy shows more than 97%. It is also evident that the further the measurement distance, the worse the recognition results. This is due to the increasing amount of noise in the radio channel as the distance increases.

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Published

2023-12-06

How to Cite

[1]
N. Kurniawati and S. F. Nurjihan, “Human Activity Recognition System Using WiFi Sensing and Deep Learning”, Buletin Ilmiah Sarjana Teknik Elektro, vol. 5, no. 4, pp. 498–504, Dec. 2023.

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