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Author: search.filters.author.Pérez-Daniel, Karina RubySubject: search.filters.subject.Brain

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    Deep Learning for Multimodal Fall Detection
    (2019)
    Martinez-Villaseñor, Lourdes  
    ;
    Pérez-Daniel, Karina Ruby
    ;
    Ponce, Hiram  
    Fall detection systems can help providing quick assistance of the person diminishing the severity of the consequences of a fall. Real-time fall detection is important to decrease fear and time that a person remains laying on the floor after falling. In recent years, multimodal fall detection approaches are developed in order to gain more precision and robustness. In this work, we propose a multimodal fall detection system based on wearable sensors, ambient sensors and vision devices. We used long short-term memory networks (LSTM) and convolutional neural networks (CNN) for our analysis given that they are able to extract features from raw data, and are well suited for real-time detection. To test our proposal, we built a public multimodal dataset for fall detection. After experimentation, our proposed method reached 96.4% in accuracy, and it represented an improvement in precision, recall and F-{1}-score over using single LSTM or CNN networks for fall detection. © 2019 IEEE.
    Scopus© Citations 15  15  2