In this paper, we propose an approach that uses deep learning to detect a human fall. The proposed approach automatically captures the intricate properties of the radar returns. In order to minimize false alarms, we fuse information from both the time-frequency and range domains. Experimental data is used to demonstrate the superiority of the deep learning based approach in comparison with the principal component analysis method and those methods incorporating predefined physically interpreted features.


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    Title :

    Fall Detection Using Deep Learning in Range-Doppler Radars


    Contributors:


    Publication date :

    2018-02-01


    Size :

    813135 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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