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Challenges and Trends in Multimodal Fall Detection for Healthcare

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Cover of 'Challenges and Trends in Multimodal Fall Detection for Healthcare'

Table of Contents

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    Book Overview
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    Chapter 1 Open Source Implementation for Fall Classification and Fall Detection Systems
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    Chapter 2 Detecting Human Activities Based on a Multimodal Sensor Data Set Using a Bidirectional Long Short-Term Memory Model: A Case Study
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    Chapter 3 Intelligent Real-Time Multimodal Fall Detection in Fog Infrastructure Using Ensemble Learning
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    Chapter 4 Wearable Sensors Data-Fusion and Machine-Learning Method for Fall Detection and Activity Recognition
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    Chapter 6 Approaching Fall Classification Using the UP-Fall Detection Dataset: Analysis and Results from an International Competition
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    Chapter 7 Classification of Daily Life Activities for Human Fall Detection: A Systematic Review of the Techniques and Approaches
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    Chapter 8 An Interpretable Machine Learning Model for Human Fall Detection Systems Using Hybrid Intelligent Models
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    Chapter 10 A Novel Approach for Human Fall Detection and Fall Risk Assessment
Attention for Chapter 2: Detecting Human Activities Based on a Multimodal Sensor Data Set Using a Bidirectional Long Short-Term Memory Model: A Case Study
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Chapter title
Detecting Human Activities Based on a Multimodal Sensor Data Set Using a Bidirectional Long Short-Term Memory Model: A Case Study
Chapter number 2
Book title
Challenges and Trends in Multimodal Fall Detection for Healthcare
Published by
Springer, Cham, January 2020
DOI 10.1007/978-3-030-38748-8_2
Book ISBNs
978-3-03-038747-1, 978-3-03-038748-8
Authors

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

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 17%
Researcher 2 17%
Lecturer 1 8%
Student > Master 1 8%
Lecturer > Senior Lecturer 1 8%
Other 0 0%
Unknown 5 42%
Readers by discipline Count As %
Computer Science 2 17%
Business, Management and Accounting 1 8%
Chemical Engineering 1 8%
Psychology 1 8%
Engineering 1 8%
Other 0 0%
Unknown 6 50%

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