Fall Detection Analysis Using a Real Fall Dataset View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2019

AUTHORS

Samad Barri Khojasteh , José R. Villar , Enrique de la Cal , Víctor M. González , Javier Sedano

ABSTRACT

This study focuses on the performance of a fall detection method using data coming from real falls performed by relatively young people and the application of this technique in the case of an elder person. Although the vast majority of studies concerning fall detection place the sensory on the waist, in this research the wearable device must be placed on the wrist because it’s usability. A first pre-processing stage is carried out as stated in [1, 17]; this stage detects the most relevant points to label. This study analyzes the suitability of different models in solving this classification problem: a feed-forward Neural Network and a rule based system generated with the C5.0 algorithm. A discussion about the results and the deployment issues is included. More... »

PAGES

334-343

References to SciGraph publications

  • 2013. Human Activity Recognition and Feature Selection for Stroke Early Diagnosis in HYBRID ARTIFICIAL INTELLIGENT SYSTEMS
  • 2013-12. Challenges, issues and trends in fall detection systems in BIOMEDICAL ENGINEERING ONLINE
  • 2006. Fall Detection by Wearable Sensor and One-Class SVM Algorithm in INTELLIGENT COMPUTING IN SIGNAL PROCESSING AND PATTERN RECOGNITION
  • Book

    TITLE

    International Joint Conference SOCO’18-CISIS’18-ICEUTE’18

    ISBN

    978-3-319-94119-6
    978-3-319-94120-2

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-94120-2_32

    DOI

    http://dx.doi.org/10.1007/978-3-319-94120-2_32

    DIMENSIONS

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