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Wi-Fi based non-invasive detection of indoor wandering using LSTM model

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Abstract

Wandering is a significant indicator in the clinical diagnosis of dementia and other related diseases for elders. Reliable monitoring of long-term continuous movement in indoor setting for detection of wandering movement is challenging because most elders are prone to forget to carry or wear sensors that collect motion information daily due to their declining memory. Wi-Fi as an emerging sensing modality has been widely used to monitor human indoor movement in a non-invasive manner. In order to continuously monitor individuals’ indoor motion and reliably identify wandering movement in a non-invasive manner, in this work, we develop a LSTM-based deep classification method that is able to differentiate the wandering-caused Wi-Fi signal change from the others. Specifically, we first use the off-the-shelf Wi-Fi devices to capture a resident’s indoor motion information, enabling to collect a group of Wi-Fi signal streams, which will be split into variable-size segments. Second, the deep network LSTM is adopted to develop wandering detection method that is able to classify every variable-size segment of Wi-Fi signals into categories according to the well-known wandering spatiotemporal patterns. Last, experimental evaluation conducted on a group of real-world Wi-Fi signal streams shows that our proposed LSTM-based detection method is workable and effective to identify indoor wandering behavior, obtaining an average value of 0.9286, 0.9618, 0.9634 and 0.9619 for accuracy, precision, recall and F-1 score, respectively.

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Acknowledgements

This work was funded by the Fundamental Research Funds for the Central Universities (31920210013), the National Natural Science Foundation of China (Grant No. 61562075), the Natural Science Foundation of Gansu Province (20JR5RA511, 1506RJZA269), the Gansu Provincial First-class Discipline Program of Northwest Minzu University (11080305), and the Program for Innovative Research Team of SEAC ([2018] 98).

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Authors and Affiliations

  1. School of mathematics and computer science, Northwest Minzu University, Lanzhou, 730030, China

    Qiang Lin, Yusheng Hao & Caihong Liu

  2. Key Laboratory of Streaming Data Computing and Application, Northwest Minzu University, Lanzhou, 730124, China

    Qiang Lin, Yusheng Hao & Caihong Liu

Authors
  1. Qiang Lin
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  2. Yusheng Hao
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  3. Caihong Liu
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Correspondence to Qiang Lin.

Additional information

Qiang Lin received the PhD degree in computer science and technology from Northwestern Polytechnical University, China in 2014. He is currently an associate professor at School of Mathematics and Computer Science, Northwest Minzu University, China. His research interest includes medical image computing, pervasive computing, intelligent information processing and human behavior sensing.

Yusheng Hao received the MS degree in computer system architecture from Lanshou Jiaotong University, China in 2014. He is currently an senior lecturer at School of Mathematics and Computer Science, Northwest Minzu University, China. His research interest includes human behavior sensing, pervasive computing, intelligent information processing and human behavior sensing.

Caihong Liu received the MS degree in computer application technique from Lanzhou University of Technology, China in 2006. She is currently associate professor at School of Mathematics and Computer Science, Northwest Minzu University, China. Her research interest includes human behavior sensing, wireless senosr networks, and data mining.

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Lin, Q., Hao, Y. & Liu, C. Wi-Fi based non-invasive detection of indoor wandering using LSTM model. Front. Comput. Sci. 15, 156505 (2021). https://doi.org/10.1007/s11704-020-0270-z

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  • DOI: https://doi.org/10.1007/s11704-020-0270-z

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