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

Takashi Nakamura, Mondher Bouazizi, Kohei Yamamoto, Tomoaki Ohtsuki

研究成果: Article査読

抄録

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.

本文言語English
ジャーナルIEEE Internet of Things Journal
DOI
出版ステータスAccepted/In press - 2022

ASJC Scopus subject areas

  • 信号処理
  • 情報システム
  • ハードウェアとアーキテクチャ
  • コンピュータ サイエンスの応用
  • コンピュータ ネットワークおよび通信

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