Human fall detection from acceleration measurements using a Recurrent Neural Network

Abstract:


This work presented a fall detection method based on Recurrent Neural Networks. It leverages the ability of recurrent networks to process sequential data, such as acceleration measurements from body-worn devices, as well as data augmentation in the form of random rotations of the input acceleration signal. Τhe proposed method was able to find all but one fall event, while at the same time producing no false alarms when tested on the publicly available URFD dataset. Proceedings/Precision Medicine Powered by pHealth and Connected Health    


  • T. Theodoridis, V. Solachidis, N. Vretos, P. Daras, "Human fall detection from acceleration measurements using a Recurrent Neural Network", International Conference on Biomedical and Health Informatics Thessaloniki, Greece, 18-21 November 2017

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    Contact Information

    Dr. Petros Daras, Principal Researcher Grade Α
    1st km Thermi – Panorama, 57001, Thessaloniki, Greece
    P.O.Box: 60361
    Tel.: +30 2310 464160 (ext. 156)
    Fax: +30 2310 464164
    Email: daras@iti.gr