Applied Machine Learning for Assisted Living (Record no. 172931)

MARC details
000 -LEADER
fixed length control field 05908nam a22005415i 4500
001 - CONTROL NUMBER
control field 978-3-031-11534-9
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240423125002.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783031115349
-- 978-3-031-11534-9
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-031-11534-9
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number R858-859.7
072 #7 - SUBJECT CATEGORY CODE
Subject category code MBG
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code UB
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code MED117000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UXT
Source thema
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 610.285
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Uddin, Zia.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
245 10 - TITLE STATEMENT
Title Applied Machine Learning for Assisted Living
Medium [electronic resource] /
Statement of responsibility, etc by Zia Uddin.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2022.
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2022.
300 ## - PHYSICAL DESCRIPTION
Extent XI, 131 p. 60 illus., 52 illus. in color.
Other physical details online resource.
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-- txt
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-- computer
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-- online resource
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-- text file
-- PDF
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505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 1.Assisted Living -- 1. 1. Introduction -- 1.2. Surveys on Assisted Living -- 1.3. Assisted Living Projects -- 1.4. Target Users -- 1.4.1. Indoor Observations -- 1.4.2. Outdoor Observations -- 1.5. Privacy and Data Protection -- 1.6. Conclusion -- References -- 2. Sensors and Features for Assisted Living Technologies -- 2.1. Sensors in User care -- 2.1.1. Wearable Sensors -- 2.1.2. Smart Daily Objects -- 2.1.3. Environmental Sensors -- 2.1.2. Wearables with Ambient Sensors -- 2.1.3. Ambient Sensors in Robotic Assisted Living -- 2.2. Feature Extraction -- 2.2.1. Feature Extraction Using PCA -- 2.2.2. Kernel Principal Component Analysis (KPCA) -- 2.2.3. Feature Extraction Using ICA -- 2.2.4. Linear Discriminant Analysis (LDA) -- 2.2.5. Generalized Discriminant Analysis (GDA) -- 2.3. Discussion -- 2.4. Conclusion -- References -- 3. Machine Learning -- 3.1 Shallow Machine Learning -- 3.1.1. Support Vector Machines -- vii -- 3.1.2. Random Forests -- 3.1.3. AdaBoost and Gradient Boosting -- 3.1.4. Nearest Neighbors -- 3.1.5. Examples -- 3.2. Deep Machine Learning -- 3.2.1. Deep Belief Networks (DBN) -- 3.2.2. Convolutional Neural Network -- 3.2.3. Recurrent Neural Networks -- 3.2.4. Neural Structured Learning -- 3.2.4. Pre-trained deep learning models -- 3.3. Explainable AI (XAI) -- 3.3.1. Local Explanations -- 3.3.2. Rule-based Explanations -- 3.3.3. Visual Explanations -- 3.3.4. Feature Relevance Explanations -- 3.4. Discussion -- 3.5. Conclusion -- References -- 4. Applications -- 4.1. Wearable Sensor-based Behavior Recognition -- 4.1.1. MHEALTH Dataset -- 4.1.2. Experimental Results on MHEALTH Dataset -- 4.1.3. PUC-Rio Dataset -- 4.1.4. Experimental Results on PUC-Rio Dataset -- 4.1.5. ARem Dataset -- 4.1.6. Experimental Results on AReM Dataset -- 4.3. Video Camera-based Behavior Recognition -- 4.3.1. Binary Silhouettes and Features -- 4.3.2. Depth Silhouettes and Features -- 4.3.3. 3-D Model-based HAR -- 4.4. Other Ambient Sensor-based Behavior Recognition -- 4.4.1. CASAS Dataset -- viii -- 4.4.2. Experimental Results -- 4.5. Conclusion -- References.
520 ## - SUMMARY, ETC.
Summary, etc User care at home is a matter of great concern since unforeseen circumstances might occur that affect people's well-being. Technologies that assist people in independent living are essential for enhancing care in a cost-effective and reliable manner. Assisted care applications often demand real-time observation of the environment and the resident’s activities using an event-driven system. As an emerging area of research and development, it is necessary to explore the approaches of the user care system in the literature to identify current practices for future research directions. Therefore, this book is aimed at a comprehensive review of data sources (e.g., sensors) with machine learning for various smart user care systems. To encourage the readers in the field, insights of practical essence of different machine learning algorithms with sensor data (e.g., publicly available datasets) are also discussed. Some code segments are also included to motivate the researchers ofthe related fields to practically implement the features and machine learning techniques. It is an effort to obtain knowledge of different types of sensor-based user monitoring technologies in-home environments. With the aim of adopting these technologies, research works, and their outcomes are reported. Besides, up to date references are included for the user monitoring technologies with the aim of facilitating independent living. Research that is related to the use of user monitoring technologies in assisted living is very widespread, but it is still consists mostly of limited-scale studies. Hence, user monitoring technology is a very promising field, especially for long-term care. However, monitoring of the users for smart assisted technologies should be taken to the next level with more detailed studies that evaluate and demonstrate their potential to contribute to prolonging the independent living of people. The target of this book is to contribute towards that direction.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Medical informatics.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element User interfaces (Computer systems).
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Human-computer interaction.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Health Informatics.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine Learning.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element User Interfaces and Human Computer Interaction.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer Nature eBook
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783031115332
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783031115356
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783031115363
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-3-031-11534-9">https://doi.org/10.1007/978-3-031-11534-9</a>
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks-CSE-Springer

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