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Kategorie szczegółowe BISAC

Smart Assisted Living: Toward an Open Smart-Home Infrastructure

ISBN-13: 9783030255923 / Angielski / Miękka / 2020 / 342 str.

Feng Chen;Rebeca I Garcia-Betances;Liming Chen
Smart Assisted Living: Toward an Open Smart-Home Infrastructure Feng Chen Rebeca I Garcia-Betances Liming Chen 9783030255923 Springer - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

Smart Assisted Living: Toward an Open Smart-Home Infrastructure

ISBN-13: 9783030255923 / Angielski / Miękka / 2020 / 342 str.

Feng Chen;Rebeca I Garcia-Betances;Liming Chen
cena 363,12 zł
(netto: 345,83 VAT:  5%)

Najniższa cena z 30 dni: 346,96 zł
Termin realizacji zamówienia:
ok. 22 dni roboczych
Bez gwarancji dostawy przed świętami

Darmowa dostawa!
inne wydania
Kategorie:
Informatyka, Internet
Kategorie BISAC:
Computers > Networking - Hardware
Computers > Artificial Intelligence - Computer Vision & Pattern Recognition
Technology & Engineering > Engineering (General)
Wydawca:
Springer
Język:
Angielski
ISBN-13:
9783030255923
Rok wydania:
2020
Wydanie:
2020
Numer serii:
000210210
Ilość stron:
342
Waga:
0.49 kg
Wymiary:
23.39 x 15.6 x 1.88
Oprawa:
Miękka
Wolumenów:
01
Dodatkowe informacje:
Wydanie ilustrowane

Part I: Sensing and Activity Monitoring

Multi-Resident Activity Monitoring in Smart Homes Through Non-Wearable Non-Intrusive Sensors
Son N. Tran and Qing Zhang and Vanessa Smallbon and Mohan Karunanithi

Where Am I? Comparing CNN and LSTM for Location Classification in Egocentric Videos
Georgios Kapidis, Ronald W. Poppe, Elsbeth A. van Dam, Remco C. Veltkamp, and Lucas P. J. J. Noldus

A Privacy-Preserving Wearable Camera Setup for Dietary Event Spotting in Free-Living
Giovanni Schiboni, Fabio Wasner, and Oliver Amft

Saving Energy on EMG-Monitoring Eyeglasses for Free-Living Eating Event Spotting Using Adaptive Duty-Cycling
Giovanni Schiboni and Oliver Amft

Indoor Localisation with WiFi Fingerprinting Based on a Convolutional Neural Network
Zumin Wang

Unobtrusive Sensing to Assist with Post-Stroke Rehabilitation
Chris Nugent

Part II: Activity Recognition and Behaviour Analysis

Energy-Based Decision Engine for Household Human Activity Recognition
Anastasios Vafeiadis, Thanasis Vafeiadis, Stelios Zikos, Stelios Krinidis, Konstantinos Votis, Dimitrios Giakoumis, Dimosthenis Ioannidis, Dimitrios Tzovaras, Liming Chen, and Raouf Hamzaoui

Distributed Context Recognition, a Systematic Review
Umar Ahmad and Luis Lopera

Exercise Type Recognition Using Transfer Learning
Hossein Malekmohamadi

Meta-Intelligence for Behaviour Recognition
Xiaodong Liu and Qi Liu

Part III: User Needs and Personalisation

A Conceptual Framework for Adaptive User Interfaces for Older Adults
Eduardo Machado, Deepika Singhy, Federico Cruciani, Liming Chen, Sten Hankey, Fernando Salvago, Johannes Kropf, and Andreas Holzinger

Studying the Technological Barriers and Needs of People with Dementia: A Quantitative Study
Nikolaos Liappas, Rebeca Isabel García-Betances, José Gabriel Teriús-Padrón, and María Fernanda Cabrera-Umpiérrez

Adaptive Service Robot Behaviours Based on User Mood: Towards Better Personalized Support of MCI Patients at Home
Dimitrios Giakoumis, Georgia Peleka, Manolis Vasileiadis, Ioannis Kostavelis, and Dimitrios Tzovaras

Part IV: Ambient Assisted Living Solutions

Towards Cognitive Assisted Living
Claudia Steinberger and Judith Michael

Towards Self-Management of Chronic Diseases in Smart Homes
José G. Teriús-Padrón, Georgios Kapidis, Sarah Fallmann, Erinc Merdivan, Sten Hanke, Rebeca I. García-Betances, and María Fernanda Cabrera-Umpiérrez

A Deep Learning Approach for Privacy Preservation in Assisted Living
Ismini Psychoula, Erinc Merdivany, Deepika Singhy, Liming Chen, Feng Chen, Sten Hankey, Johannes Kropfy, Andreas Holzingerx, and Matthieu Geist

Towards Socially Assistive Robots for the Elderly: An End-to-End Object Search Framework
Mohammad Reza Loghmani, Timothy Patten and Markus Vincze

Modelling Activities of Daily Living with Petri Nets
Matias Garcia-Constantino, Alexandros Konios and Chris Nugent

Calculus of Context-Aware Ambients for Assisted Living System Modelling
Francois Siewe

Dr Feng Chen is a Senior Lecturer at the School of Computer Science and Informatics, De Montfort University.

Dr Rebeca I García-Betances is a Senior Researcher in the Life Supporting Technologies Group at the Technical University of Madrid.

Prof María Fernanda Cabrera-Umpiérrez is an Associate Professor at the Telecommunication School, Technical University of Madrid.

Prof Liming Chen is a Chair Professor of Computer Science at De Montfort University, where he leads the Context, Intelligence and Interaction Research Group (CIIRG).

Prof Chris Nugent is a Professor of Biomedical Engineering and Head of the School of Computing at Ulster University, where he also leads the Pervasive Computing Research Group and is Co-Principal Investigator at the Connected Health Innovation Centre.


Smart Homes (SH) offer a promising approach to assisted living for the ageing population. Yet the main obstacle to the rapid development and deployment of Smart Home (SH) solutions essentially arises from the nature of the SH field, which is multidisciplinary and involves diverse applications and various stakeholders. Accordingly, an alternative to a one-size-fits-all approach is needed in order to advance the state of the art towards an open SH infrastructure.

This book makes a valuable and critical contribution to smart assisted living research through the development of new effective, integrated, and interoperable SH solutions. It focuses on four underlying aspects: (1) Sensing and Monitoring Technologies; (2) Context Interference and Behaviour Analysis; (3) Personalisation and Adaptive Interaction, and (4) Open Smart Home and Service Infrastructures, demonstrating how fundamental theories, models and algorithms can be exploited to solve real-world problems.

This comprehensive and timely book offers a unique and essential reference guide for policymakers, funding bodies, researchers, technology developers and managers, end users, carers, clinicians, healthcare service providers, educators and students, helping them adopt and implement smart assisted living systems.



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