Wi-Fi-based Fall Detection Using Spectrogram Image of Channel State Information

Takashi Nakamura, Mondher Bouazizi, Kohei Yamamoto, Tomoaki Ohtsuki

Research output: Contribution to journalArticlepeer-review

Abstract

Wi-Fi CSI (Channel State Information) based fall detection systems have a great potential compared with other alternatives since they are non-intrusive and non-space limited. However, in the conventional work on Wi-Fi CSI based fall detection, a phenomenon is commonly observed: the classification performance degrades when data in different environments are used for learning and testing. Nonetheless, when the SNR (Signal to Noise power Ratio) is small, the conventional methods cannot capture features of motion and cannot segment signals accurately. Therefore, there is a need to address these problems in order to build a robust fall detection system. In this paper, we propose a spectrogram image-based fall detection using Wi-Fi CSI. Unlike the conventional method, CSI is segmented with a certain sliding time window, and then the classifier detects fall by using the spectrogram image generated from the segmented CSI. We use a pre-trained CNN (Convolutional Neural Network) optimized for binary classification of the spectrogram images of the fall and non-fall motions. We carried out experiments to evaluate the classification performance of our proposed method against the conventional one by using motion data in two different rooms for learning and testing. As a result, we confirmed that our proposed method outperforms the conventional one and reaches over 0.92 accuracy. In addition, compared with the conventional method, the fall detection performance of our method does not degrade even when using different environment data for learning and testing.

Original languageEnglish
JournalIEEE Internet of Things Journal
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Activity recognition
  • channel state information (CSI)
  • device-free
  • Fall detection
  • fall detection
  • Feature extraction
  • Hidden Markov models
  • Image segmentation
  • machine learning
  • Radar
  • Spectrogram
  • WiFi.
  • Wireless fidelity

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'Wi-Fi-based Fall Detection Using Spectrogram Image of Channel State Information'. Together they form a unique fingerprint.

Cite this