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    Early Fall Prediction Using Hybrid Recurrent Neural Network and Long Short-Term Memory

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    Author
    Lytras, Miltiadis cc
    Kwok Tai Chui
    Ryan Wen Liu
    Mingbo Zhao
    Miguel Torres Ruiz
    Subject
    Fall Prediction
    Predictive Model
    Recurrent Neural Network
    Date
    2022-10-21
    
    Metadata
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    Abstract
    Falls are unintentionally events that may occur in all age groups, particularly for elderly. Negative impacts include severe injuries and deaths. Although numerous machine learning models were proposed for fall detection, the formulations of the models are limited to prevent the occurrence of falls. Recently, the emerging research area namely early fall prediction receives an increasing attention. The major challenges of fall prediction are the long period of unseen future data and the nature of uncertainty in the time of occurrence of fall events. To extend the predictability (from 0.5 to 5 s) of the early fall prediction model, we propose a particle swarm optimization-based recurrent neural network and long short-term memory (RNN-LSTM). Results and analysis show that the algorithm yields accuracies of 89.8–98.2%, 88.4–97.1%, and 89.3–97.6% in three benchmark datasets UP Fall dataset, MOBIFALL dataset, and UR Fall dataset, respectively.
    Department
    Computer Science
    Publisher
    Springer International Publishing
    Collections
    Conference Papers

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