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SFA-based ELM for remote detection of stationary objects

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Abstract

Ultra-wideband (UWB) bio-radar is a promising technology for the contactless detection and wireless monitoring of stationary living persons remotely. However the optional detection approach is fewer and the accuracy is less. Thus some other alternative methods for the detection of living persons are urgently required. Here, we investigated the detection of living persons by extracting human respiratory signals based on the machine learning under non-line of sight (NLOS) multipath environments. Extreme learning machine (ELM) was utilized to determine if one person is present in the detection environment or not based on the distributions of vital signs acquired from the statistics fingerprint analysis (SFA). Then, the life sign points of interest (LSPOI) can be defined to measure the frequency of human cardiopulmonary movement when the living persons in present in the environments. Meanwhile the amount of data can be reduced and the system efficiency can be enhanced which plays a key role in human detection. The detection results indicate the developed algorithm outperform the known references.

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Acknowledgements

This research was funded by National Key Research and Development Program (2018YFF01013002 and 2018YFF01013602); China Electronics Technology Group Corporation Connerstone Project (JSXC2019***004); Pre-research Project (3151***303) and Science and Technology on Electronic Test & Measurement Laboratory (6142001190101 and 6142001190102). The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the article.

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Authors and Affiliations

  1. Science and Technology on Electronic Test and Measurement Laboratory, The 41st Research Institute of CETC, Qingdao, 266555, China

    Xiaolin Liang, Weifeng Zhu, Jiawen Sun & Jianqin Deng

  2. China Electronics Technology Instruments Co Ltd, Qingdao, 266555, China

    Xiaolin Liang, Weifeng Zhu, Jiawen Sun & Jianqin Deng

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  1. Xiaolin Liang
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  2. Weifeng Zhu
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  3. Jiawen Sun
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  4. Jianqin Deng
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Correspondence to Jianqin Deng.

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Liang, X., Zhu, W., Sun, J. et al. SFA-based ELM for remote detection of stationary objects. J Ambient Intell Human Comput 13, 2963–2981 (2022). https://doi.org/10.1007/s12652-021-03131-x

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