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

3 Citations (Scopus)


Wi-Fi channel state information (CSI)-based fall detection systems have a great potential compared with other alternatives since they are nonintrusive and nonspace 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 signal-to-noise-power ratio (SNR) 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 article, 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 pretrained convolutional neural network (CNN) optimized for binary classification of the spectrogram images of the fall and nonfall 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
Pages (from-to)17220-17234
Number of pages15
JournalIEEE Internet of Things Journal
Issue number18
Publication statusPublished - 2022 Sept 15


  • Activity recognition
  • WiFi
  • channel state information 28 (CSI)
  • device-free
  • fall detection
  • machine learning

ASJC Scopus subject areas

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


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