- Soft Computing Drug Administration in the Ambient Assisted Living Environment

2015 ◽  
pp. 576-593
Keyword(s):  
Soft Computing ◽  
Drug Administration ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Living Environment

Forecasting Trends in an Ambient Assisted Living Environment Using Deep Learning

2021 ◽  
Author(s):  
Guillaume Gingras ◽  
Mehdi Adda ◽  
Abdenour Bouzouane ◽  
Hussein Ibrahim ◽  
Clemence Dallaire
Keyword(s):  
Deep Learning ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Living Environment

A Multi-Disciplinary Approach to Ambient Assisted Living

2011 ◽  
pp. 76-93 ◽  
Author(s):  
Martina Ziefle ◽  
Carsten Röcker ◽  
Wiktoria Wilkowska ◽  
Kai Kasugai ◽  
Lars Klack ◽  
...  
Keyword(s):  
Technology Acceptance ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Medical Engineering ◽  
Interdisciplinary Approach ◽  
Healthcare Systems ◽  
Living Environment ◽  
Smart Healthcare ◽  
Interdisciplinary Research Project ◽  
Independent Life

This chapter illustrates the different disciplinary design challenges of smart healthcare systems and presents an interdisciplinary approach toward the development of an integrative Ambient Assisted Living environment. Within the last years a variety of new healthcare concepts for supporting and assisting users in technology-enhanced environments emerged. While such smart healthcare systems can help to minimize hospital stays and in so doing enable patients an independent life in a domestic environment, the complexity of such systems raises fundamental questions of behavior, communication and technology acceptance. The first part of the chapter describes the research challenges encountered in the fields of medical engineering, computer science, psychology, communication science, and architecture as well as their consequences for the design, use and acceptance of smart healthcare systems. The second part of the chapter shows how these disciplinary challenges were addressed within the eHealth project, an interdisciplinary research project at RWTH Aachen University.

A pattern analysis and visualisation system for sleep monitoring in ambient assisted living environment

2012 ◽  
Vol 1 (4) ◽  
pp. 320 ◽  
Author(s):  
Huiru Zheng ◽  
Haiying Wang ◽  
Hoda Nikamalfard ◽  
Maurice Mulvenna ◽  
Paul McCullagh ◽  
...  
Keyword(s):  
Assisted Living ◽  
Pattern Analysis ◽  
Ambient Assisted Living ◽  
Living Environment ◽  
Sleep Monitoring

scholarly journals Behavioral Biometrics in Assisted Living: A Methodology for Emotion Recognition

2016 ◽  
Vol 6 (4) ◽  
pp. 1035-1044
Author(s):  
S. Xefteris ◽  
N. Doulamis ◽  
V. Andronikou ◽  
T. Varvarigou ◽  
G. Cambourakis
Keyword(s):  
Risk Assessment ◽  
Neurodegenerative Diseases ◽  
Facial Expressions ◽  
Assisted Living ◽  
Service Oriented Architecture ◽  
Ambient Assisted Living ◽  
Living Environment ◽  
Emotional Status ◽  
Behavioral Biometrics ◽  
Dementia Patients

Behavioral biometrics aim at providing algorithms for the automatic recognition of individual behavioral traits, stemming from a person’s actions, attitude, expressions and conduct. In the field of ambient assisted living, behavioral biometrics find an important niche. Individuals suffering from the early stages of neurodegenerative diseases (MCI, Alzheimer’s, dementia) need supervision in their daily activities. In this context, an unobtrusive system to monitor subjects and alert formal and informal carers providing information on both physical and emotional status is of great importance and positively affects multiple stakeholders. The primary aim of this paper is to describe a methodology for recognizing the emotional status of a subject using facial expressions and to identify its uses, in conjunction with pre-existing risk-assessment methodologies, for its integration into the context of a smart monitoring system for subjects suffering from neurodegenerative diseases. Paul Ekman’s research provided the background on the universality of facial expressions as indicators of underlying emotions. The methodology then makes use of computational geometry, image processing and graph theory algorithms for the detection of regions of interest and then a neural network is used for the final classification. Findings are coupled with previous published work for risk assessment and alert generation in the context of an ambient assisted living environment based on Service oriented architecture principles, aimed at remote web-based estimation of the cognitive and physical status of MCI and dementia patients.

scholarly journals Ambient Assisted Living: A Research on Human Activity Recognition and Vital Health Sign Monitoring using Deep Learning Approaches

2019 ◽  
Vol 8 (6S4) ◽  
pp. 531-540
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Assisted Living ◽  
Independent Living ◽  
Ambient Assisted Living ◽  
The Elderly ◽  
Human Activity Recognition ◽  
Living Environment ◽  
Learning Approaches

The rise in life expectancy rate and dwindled birth rate in new age society has led to the phenomenon of population ageing which is being witnessed across the world from past few decades. India is also a part of this demographic transition which will have the direct impact on the societal and economic conditions of the country. In order to effectively deal with the prevailing phenomenon, stakeholders involved are coming up with the Information and Communication Technology (ICT) based ecosystem to address the needs of elderly people such as independent living, activity recognition, vital health sign monitoring, prevention from social isolation etc. Ambient Assisted Living (AAL) is one such ecosystem which is capable of providing safe and secured living environment for the elderly and disabled people. In this paper we will focus on reviewing the sensor based Human Activity Recognition (HAR) and Vital Health Sign Monitoring (VHSM) which is applicable for AAL environments. At first we generally describe the AAL environment. Next we present brief insights into sensor modalities and different deep learning architectures. Later, we survey the existing literature for HAR and VHSM based on sensor modality and deep learning approach used.

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