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  4. Open Source Implementation for Fall Classification and Fall Detection Systems
 
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Open Source Implementation for Fall Classification and Fall Detection Systems

Journal
Challenges and Trends in Multimodal Fall Detection for Healthcare
Studies in Systems, Decision and Control
ISSN
2198-4182
2198-4190
Date Issued
2020
Author(s)
Ponce, Hiram  
Facultad de Ingeniería - CampCM  
Martinez-Villaseñor, Lourdes  
Facultad de Ingeniería - CampCM  
Nuñez Martínez, José Pablo
Facultad de Ingeniería - CampCM  
Moya-Albor, Ernesto  
Facultad de Ingeniería - CampCM  
Brieva, Jorge  
Facultad de Ingeniería - CampCM  
Type
Resource Types::text::book::book part
DOI
10.1007/978-3-030-38748-8_1
URL
https://scripta.up.edu.mx/handle/123456789/4122
Abstract
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.
Subjects

Ambient assisted livi...

Human activity recogn...

Human fall detection

Machine learning

Open coding


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