Wireless Mobile Communication and Healthcare. 6th International Conference, MobiHealth 2016, Milan, Italy, November 14-16, 2016, Proceedings

Research Article

A Neural Network Model Based on Co-occurrence Matrix for Fall Prediction

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  • @INPROCEEDINGS{10.1007/978-3-319-58877-3_32,
        author={Masoud Hemmatpour and Renato Ferrero and Bartolomeo Montrucchio and Maurizio Rebaudengo},
        title={A Neural Network Model Based on Co-occurrence Matrix for Fall Prediction},
        proceedings={Wireless Mobile Communication and Healthcare. 6th International Conference, MobiHealth 2016, Milan, Italy, November 14-16, 2016, Proceedings},
        proceedings_a={MOBIHEALTH},
        year={2017},
        month={6},
        keywords={},
        doi={10.1007/978-3-319-58877-3_32}
    }
    
  • Masoud Hemmatpour
    Renato Ferrero
    Bartolomeo Montrucchio
    Maurizio Rebaudengo
    Year: 2017
    A Neural Network Model Based on Co-occurrence Matrix for Fall Prediction
    MOBIHEALTH
    Springer
    DOI: 10.1007/978-3-319-58877-3_32
Masoud Hemmatpour1,*, Renato Ferrero1,*, Bartolomeo Montrucchio1,*, Maurizio Rebaudengo1,*
  • 1: Politecnico di Torino
*Contact email: masoud.hemmatpour@polito.it, renato.ferrero@polito.it, bartolomeo.montrucchio@polito.it, maurizio.rebaudengo@polito.it

Abstract

Fall avoidance systems reduce injuries due to unintentional falls, but most of them are fall detections that activate an alarm after the fall occurrence. Since predicting a fall is the most promising approach to avoid a fall injury, this study proposes a method based on new features and multilayer perception that outperforms state-of-the-art approaches. Since accelerometer and gyroscope embedded in a smartphone are recognized to be precise enough to be used in fall avoidance systems, they have been exploited in an experimental analysis in order to compare the proposal with state-of-the-art approaches. The results have shown that the proposed approach improves the accuracy from 83% to 90%.