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Author: search.filters.author.Nuñez Martínez, José Pablo

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    Item type:Publication,
    Open Source Implementation for Fall Classification and Fall Detection Systems
    (2020)
    Ponce, Hiram  
    ;
    Martinez-Villaseñor, Lourdes  
    ;
    Nuñez Martínez, José Pablo
    ;
    Moya-Albor, Ernesto  
    ;
    Brieva, Jorge  
    Distributed social coding has created many benefits for software developers. Open source code and publicly available datasets can leverage the development of fall detection and fall classification systems. These systems can help to improve the time in which a person receives help after a fall occurs. Many of the simulated falls datasets consider different types of fall however, very few fall detection systems actually identify and discriminate between each category of falls. In this chapter, we present an open source implementation for fall classification and detection systems using the public UP-Fall Detection dataset. This implementation comprises a set of open codes stored in a GitHub repository for full access and provides a tutorial for using the codes and a concise example for their application. © 2020, Springer Nature Switzerland AG.
    Scopus© Citations 2  54  2
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    Artificial hydrocarbon networks for freezing of gait detection in Parkinson’s disease
    (2020)
    Martinez-Villaseñor, Lourdes  
    ;
    Ponce, Hiram  
    ;
    Nuñez Martínez, José Pablo
    Freezing of gait (FoG) is one of the most impairing phenomenon experienced by Parkinson's disease (PD) patients. This phenomenon is associated with falls and is an important factor that limits autonomy and impairs quality of life of PD patients. Pharmacological treatment is difficult and do not always help to deal with this problem. Robust FoG detection systems can help monitoring and identifying when a patient needs aid providing external cueing to deal with FoG episodes. In this paper, we describe a comparative analysis of traditional machine learning techniques against Artificial Hydrocarbon Networks (AHN) for FoG detection. We compared four supervised machine learning classifiers and AHN for FoG event detection using a publicly available dataset, obtaining 88% of F-score metric with AHN. We prove that AHN are suitable for FoG detection. © 2020 IEEE.
    Scopus© Citations 2  11  1