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Challenges and Trends in Multimodal Fall Detection for Healthcare

Part of the Studies in Systems, Decision and Control Book Series (volume 273)

Editors: Ponce, Hiram; Martínez-Villaseñor, Lourdes; Brieva, Jorge; Moya-Albor, Ernesto

DOI: 10.1007/978-3-030-38748-8

ISBN: 978-3-030-38747-1

Publisher: Springer International Publishing - Springer Journals

Jan 29, 2020

1 - 10 of 10 Chapters

LitStream

Open Source Implementation for Fall Classification and Fall Detection Systems

Ponce, Hiram; Martínez-Villaseñor, Lourdes; Núñez-Martínez, José; Moya-Albor, Ernesto; Brieva, Jorge

Pages: 3 –29

[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...
LitStream

Detecting Human Activities Based on a Multimodal Sensor Data Set Using a Bidirectional Long Short-Term Memory Model: A Case Study

de Assis Neto, Silvano Ramos; Santos, Guto Leoni; da Silva Rocha, Elisson; Bendechache, Malika; Rosati, Pierangelo; Lynn, Theo; Takako Endo, Patricia

Pages: 31 –51

[Human falls are one of the leading causes of fatal unintentional injuries worldwide. Falls result in a direct financial cost to health systems, and indirectly, to society’s productivity. Unsurprisingly, human fall detection and prevention is a major focus of health research. In this chapter, we...
LitStream

Intelligent Real-Time Multimodal Fall Detection in Fog Infrastructure Using Ensemble Learning

Divya, V.; Leena Sri, R.

Pages: 53 –79

[As the data and computing power increases with surge in sensors and IoT devices to making things smart, the research trend is moving in the direction to help the elderly as a part of the smart home infrastructure. The elderly people of age over 60 years are at a high risk level due to adverse...
LitStream

Wearable Sensors Data-Fusion and Machine-Learning Method for Fall Detection and Activity Recognition

Gjoreski, Hristijan; Stankoski, Simon; Kiprijanovska, Ivana; Nikolovska, Anastasija; Mladenovska, Natasha; Trajanoska, Marija; Velichkovska, Bojana; Gjoreski, Martin; Luštrek, Mitja; Gams, Matjaž

Pages: 81 –96

[Human falls are common source of injury among the elderly, because often the elderly person is injured and cannot call for help. In the literature this is addressed by various fall-detection systems, of which most common are the ones that use wearable sensors. This paper describes the winning...
LitStream

Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras

Espinosa, Ricardo; Ponce, Hiram; Gutiérrez, Sebastián; Martínez-Villaseñor, Lourdes; Brieva, Jorge; Moya-Albor, Ernesto

Pages: 97 –120

[Currently one of the most important research issue for artificial intelligence and computer vision tasks is the recognition of human falls. Due to the current exponential increase in the use of cameras is it common to use vision-based approach for fall detection and classification systems. On...
LitStream

Approaching Fall Classification Using the UP-Fall Detection Dataset: Analysis and Results from an International Competition

Ponce, Hiram; Martínez-Villaseñor, Lourdes

Pages: 121 –133

[This chapter presents the results of the Challenge UP – Multimodal Fall Detection competition that was held during the 2019 International Joint Conference on Neural Networks (IJCNN 2019). This competition lies on the fall classification problem, and it aims to classify eleven human activities...
LitStream

Classification of Daily Life Activities for Human Fall Detection: A Systematic Review of the Techniques and Approaches

Nizam, Yoosuf; Jamil, M. Mahadi Abdul

Pages: 137 –179

[Human fall detection systems are an important part of assistive technology, since daily living assistance are very often required for many people in today’s aging population. Human fall detection systems play an important role in our daily life, because falls are the main obstacle for elderly...
LitStream

An Interpretable Machine Learning Model for Human Fall Detection Systems Using Hybrid Intelligent Models

Souza, Paulo Vitor C.; Guimaraes, Augusto J.; Araujo, Vanessa S.; Batista, Lucas O.; Rezende, Thiago S.

Pages: 181 –205

[This chapter presents an assessment of falls and everyday situations in people by sensors dataset collected in fall simulation. This evaluation was performed through the use of intelligent techniques and models based on feature selection techniques and fuzzy neural networks. Therefore, this work...
LitStream

Multi-sensor System, Gamification, and Artificial Intelligence for Benefit Elderly People

Méndez, Juana Isabel; Mata, Omar; Ponce, Pedro; Meier, Alan; Peffer, Therese; Molina, Arturo

Pages: 207 –235

[Over the years, the elderly people population will become more than children population; besides since 2018 people over 65 years old outnumbered the population under 5 years old. Growing up involves biological, physical, social and psychological changes that may lead to social isolation and loss...
LitStream

A Novel Approach for Human Fall Detection and Fall Risk Assessment

Nizam, Yoosuf; Jamil, M. Mahadi Abdul

Pages: 237 –259

[There are several studies concerned in providing quality health care services to all people. Among them, human fall detection systems play an important role, because fall is the main obstacle for elderly people to live independently and it is also a major health concern due to aging population....

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