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Challenges and Trends in Multimodal Fall Detection for Healthcare de Hiram Ponce

Challenges and Trends in Multimodal Fall Detection for Healthcare: Studies in Systems, Decision and Control, cartea 273

Editat de Hiram Ponce, Lourdes Martínez-Villaseñor, Jorge Brieva, Ernesto Moya-Albor
en Limba Engleză Paperback – 29 ian 2021
This book focuses on novel implementations of sensor technologies, artificial intelligence, machine learning, computer vision and statistics for automated, human fall recognition systems and related topics using data fusion.

It includes theory and coding implementations to help readers quickly grasp the concepts and to highlight the applicability of this technology. For convenience, it is divided into two parts. The first part reviews the state of the art in human fall and activity recognition systems, while the second part describes a public dataset especially curated for multimodal fall detection. It also gathers contributions demonstrating the use of this dataset and showing examples.
 
This book is useful for anyone who is interested in fall detection systems, as well as for those interested in solving challenging, signal recognition, vision and machine learning problems. Potential applications include health care, robotics, sports, human–machine interaction, among others.

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Specificații

ISBN-13: 9783030387501
ISBN-10: 303038750X
Ilustrații: XIII, 259 p.
Dimensiuni: 155 x 235 mm
Greutate: 0.39 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Systems, Decision and Control

Locul publicării:Cham, Switzerland

Cuprins

Challenges and Solutions on Human Fall Detection and Classification.- Open Source Implementation for Fall Classification and Fall Detection Systems.- Detecting Human Activities based on a Multimodal Sensor Data Set using a Bidirectional Long Short-Term Memory Model: A Case Study.- Approaching Fall Classification using the UP-Fall Detection Dataset: Analysis and Results from an International Competition.- Reviews and Trends on Multimodal Healthcare.- A Novel Approach for Human Fall Detection and Fall Risk Assessment.

Textul de pe ultima copertă

This book focuses on novel implementations of sensor technologies, artificial intelligence, machine learning, computer vision and statistics for automated, human fall recognition systems and related topics using data fusion.
 
It includes theory and coding implementations to help readers quickly grasp the concepts and to highlight the applicability of this technology. For convenience, it is divided into two parts. The first part reviews the state of the art in human fall and activity recognition systems, while the second part describes a public dataset especially curated for multimodal fall detection. It also gathers contributions demonstrating the use of this dataset and showing examples.
 
This book is useful for anyone who is interested in fall detection systems, as well as for those interested in solving challenging, signal recognition, vision and machine learning problems. Potential applications include health care, robotics, sports, human–machine interaction, among others.


Caracteristici

Covers challenging issues and current trends for designing fall detection systems using a multimodal approach Provides novel implementations of sensor technologies, artificial intelligence, machine learning, and statistics for fall detection systems Describes and discusses a common, public dataset, especially gathered for multimodal fall detection