Title: A contactless sensing system for indoor fall recognition based on channel state information

Authors: Wei Zhuang; Yixian Shen; Jiefeng Zhang; Chunming Gao; Yi Chen; Dong Dai

Addresses: School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China; Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing, 210044, China ' School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China; Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing, 210044, China ' School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China; Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing, 210044, China ' School of Engineering and Technology, University of Washington Tacoma, Tacoma, WA 98402, USA ' School of Informatics, University of Edinburgh, Edinburgh, EH89AB, UK ' School of Cyber Science and Engineering, Southeast University, No. 2 Sipailou, Nanjing, 210096, China

Abstract: This paper introduces the designs and the implementation of a non-invasive indoor fall recognition system based on channel state information (CSI) in the Wi-Fi physical layer. We use a wireless router and a laptop computer equipped with an Intel Wi-Fi Link 5300 network card (802.11n) to setup a hardware platform. The platform receives and stores CSI data under various circumstances when a person in the Wi-Fi covered area stands up, sits down, walks, and falls. The CSI data are then processed and analysed using Matlab tools. Feature variables such as signal offset strength, period of motion, normalised standard deviation, median absolute deviation, interquartile range, and signal entropy are examined and best feature variables are chosen. Finally, cross validation algorithm and support vector machine (SVM) are used to establish the pattern recognition model. We tested the system in a laboratory environment and the experimental results showed that the fall incidents were effectively differentiated from other movements.

Keywords: CSI; channel state information; movement recognition; fall detection; contactless sensing; sensorless sensing; WiFi radar; WiFi physical layer; SVM; support vector machine.

DOI: 10.1504/IJSNET.2020.111237

International Journal of Sensor Networks, 2020 Vol.34 No.3, pp.188 - 200

Received: 02 Apr 2020
Accepted: 29 Apr 2020

Published online: 26 Oct 2020 *

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