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Selected Papers from the 17th International Conference on Smart Living...
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Selected Papers from the 17th International Conference on Smart Living and Public Health (ICOST 2019)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (29 February 2020) | Viewed by 13902

Special Issue Editors

Prof. Dr. Mounir Mokhtari

E-Mail Website
Guest Editor
Professor at Institut Mines-Télécom, Paris, France. Director of CNRS IPAL Lab, Singapore
Interests: human–machine interaction; ambient assisted living; rehabilitation robotics; health telematics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bessam Abdulrazak

E-Mail Website
Guest Editor
Département d'informatique Faculté des sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
Interests: ubiquitous and pervasive computing; ambient-intelligence; smart-environments; IoT; assistive technologies and rehabilitation robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Hamdi Aloulou

E-Mail Website
Guest Editor
Associate Professor, University of Monastir, Tunisia Senior Scientist Digital Research Centre of Sfax, Tunisia Research Associate Institut Mines-Telecom, Paris, France
Interests: ambient intelligence; Internet of Things; e-health; pervasive computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jose Pagan

E-Mail Website
Guest Editor
New York University and the New York Academy of Medicine, USA
Interests: applied economics; health economics; population health; public health policy
Dr. Maria Fernanda Cabrera-Umpiérrez

E-Mail Website
Guest Editor
LifeSTech Group, Tecnología Fotónica y Bioingeniería Dep, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: ehealth; einclusion; human computer interaction; accessibility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 17th International Conference on Smart Living and Public Health (ICOST 2019) will be held from 14–16 October 2019 (http://www.icost-society.org/). The ICOST 2019 edition will be hosted by New York University (NYU) and the New York Academy of Medicine (NYAM), in New York City, USA. Our theme for this year will be “How does AI impact urban living and public health?”

ICOST provides a premier venue for the presentation and discussion of research in the design, development, deployment and evaluation of artificial intelligence for health, smart urban environments, assistive technologies, chronic disease management, coaching and health telematics systems. This year, ICOST aims at understanding and assessing how research impacts public health policies in facing emerging social and economic challenges. ICOST brings together stakeholders from clinical, PHOs (public health organizations), academics and industrial perspectives along with end users and family caregivers to explore how to utilize technologies to foster health prevention, independent living, and offer an enhanced quality of life. The conference features a dynamic program incorporating a range of technical, clinical, and industry keynote speakers, and oral and poster presentations along with demonstrations and technical exhibits.

Authors of selected high-qualified papers from the conference will be invited to submit extended versions of their original papers (50% extensions of the contents of the conference paper) and contributions under the following conference topics:

  • Public health and urban data collection and processing
  • Policy and guidelines for Public Health 2.0 and quality of life in smart cities
  • Health in all policies (HiAP)
  • Health technology assessment and impact analysis
  • Artificial intelligence for participatory and personalized health
  • Deep learning for health and wellbeing
  • Big data analytics
  • Human–machine interfaces
  • Social acceptance/privacy/data protection/security issues
  • Smart urban spaces and new assistive living space concepts in the smart city
  • Urban design for wellbeing and e-health
  • Participatory telehealth systems
  • Design and future urban cities for wellbeing
  • Preventive medicine
  • Predictive medicine
  • Translational medicine for wellbeing
  • E-health and chronic disease management
  • Assistive technology for ageing and frail people
  • E-health and therapeutic education
  • Coaching and urban gaming for e-health
  • Virtual personal assistants for e-Health
  • Internet of Things (IoT) for smart living and e-health
  • Persuasive and supportive mobile e-health
  • Personal robotics and smart wheelchairs
  • Collaborative robotics (Cobots)
  • Trusted systems and verification for health and wellness
  • Human factors, ethics and usability for persons with cognitive impairments
  • Modeling of physical and conceptual information in smart environments
  • Cognitive technologies across the lifespan and functional abilities
  • Tele-assistance and tele-rehabilitation
  • Protective systems/devices against common and emerging infectious diseases.
  • Smart homes/home networks/residential gateways
  • Middleware support for smart home and health telematic services
  • Wearable sensors/integrated micro/nano systems/home health monitoring
  • Context awareness/autonomous computing

Prof. Dr. Mounir Mokhtari
Prof. Dr. Bessam Abdulrazak
Dr. Hamdi Aloulou
Prof. Dr. Jose Pagan
Prof. Dr. Maria Fernanda Cabrera
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Public Health
  • E-health
  • Assistive Technologies
  • Artificial Intelligence
  • Machine Learning
  • Internet of Things

Published Papers (4 papers)

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Research

11 pages, 1274 KiB  
Article
ICT-Based Health Care Services for Individuals with Spinal Cord Injuries: A Feasibility Study
by Wan-ho Jang, Seung-bok Lee, Dong-wan Kim, Yun-hwan Lee, Yun-jeong Uhm, Seung-wan Yang, Jeong-hyun Kim and Jong-bae Kim
Sensors 2020, 20(9), 2491; https://doi.org/10.3390/s20092491 - 28 Apr 2020
Cited by 2 | Viewed by 2937
Abstract
In the Republic of Korea, 90.5% of those living with spinal cord injury (SCI) are faced with medical complications that require chronic care. Some of the more common ones include urinary tract infections, pressure sores, and pain symptomatology. These and other morbidities have [...] Read more.
In the Republic of Korea, 90.5% of those living with spinal cord injury (SCI) are faced with medical complications that require chronic care. Some of the more common ones include urinary tract infections, pressure sores, and pain symptomatology. These and other morbidities have been recognized to deteriorate the individual’s health, eventually restricting their community participation. Telerehabilitation, using information and communication technology, has propelled a modern-day movement in providing comprehensive medical services to patients who have difficulty in mobilizing themselves to medical care facilities. This study aims to verify the effectiveness of health care and management in the SCI population by providing ICT-based health care services. We visited eight individuals living with chronic SCI in the community, and provided ICT-based health management services. After using respiratory and urinary care devices with the provision of home visit occupational therapy, data acquisition was achieved and subsequently entered into a smart device. The entered information was readily accessible to the necessary clinicians and researchers. The clients were notified if there were any concerning results from the acquired data. Subsequently, they were advised to follow up with their providers for any immediate medical care requirements. Digital hand-bike ergometers and specialized seating system cushions are currently in development. The ICT-based health care management service for individuals with SCI resulted in a favorable expected level of outcome. Based on the results of this study, we have proposed and are now in preparation for a randomized clinical trial. Full article
(This article belongs to the Special Issue Selected Papers from the 17th International Conference on Smart Living and Public Health (ICOST 2019))
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13 pages, 2370 KiB  
Article
Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models
by Meriem Zerkouk and Belkacem Chikhaoui
Sensors 2020, 20(8), 2359; https://doi.org/10.3390/s20082359 - 21 Apr 2020
Cited by 34 | Viewed by 4472
Abstract
The ability to identify and accurately predict abnormal behavior is important for health monitoring systems in smart environments. Specifically, for elderly persons wishing to maintain their independence and comfort in their living spaces, abnormal behaviors observed during activities of daily living are a [...] Read more.
The ability to identify and accurately predict abnormal behavior is important for health monitoring systems in smart environments. Specifically, for elderly persons wishing to maintain their independence and comfort in their living spaces, abnormal behaviors observed during activities of daily living are a good indicator that the person is more likely to have health and behavioral problems that need intervention and assistance. In this paper, we investigate a variety of deep learning models such as Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), CNN-LSTM and Autoencoder-CNN-LSTM for identifying and accurately predicting the abnormal behaviors of elderly people. The temporal information and spatial sequences collected over time are used to generate models, which can be fitted to the training data and the fitted model can be used to make a prediction. We present an experimental evaluation of these models performance in identifying and predicting elderly persons abnormal behaviors in smart homes, via extensive testing on two public data sets, taking into account different models architectures and tuning the hyperparameters for each model. The performance evaluation is focused on accuracy measure. Full article
(This article belongs to the Special Issue Selected Papers from the 17th International Conference on Smart Living and Public Health (ICOST 2019))
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22 pages, 8879 KiB  
Article
Deep Learning to Unveil Correlations between Urban Landscape and Population Health
by Daniele Pala, Alessandro Aldo Caldarone, Marica Franzini, Alberto Malovini, Cristiana Larizza, Vittorio Casella and Riccardo Bellazzi
Sensors 2020, 20(7), 2105; https://doi.org/10.3390/s20072105 - 08 Apr 2020
Cited by 6 | Viewed by 3022
Abstract
The global healthcare landscape is continuously changing throughout the world as technology advances, leading to a gradual change in lifestyle. Several diseases such as asthma and cardiovascular conditions are becoming more diffuse, due to a rise in pollution exposure and a more sedentary [...] Read more.
The global healthcare landscape is continuously changing throughout the world as technology advances, leading to a gradual change in lifestyle. Several diseases such as asthma and cardiovascular conditions are becoming more diffuse, due to a rise in pollution exposure and a more sedentary lifestyle. Healthcare providers deal with increasing new challenges, and thanks to fast-developing big data technologies, they can be faced with systems that provide direct support to citizens. In this context, within the EU-funded Participatory Urban Living for Sustainable Environments (PULSE) project, we are implementing a data analytic platform designed to provide public health decision makers with advanced approaches, to jointly analyze maps and geospatial information with healthcare and air pollution data. In this paper we describe a component of such platforms, which couples deep learning analysis of urban geospatial images with healthcare indexes collected by the 500 Cities project. By applying a pre-learned deep Neural Network architecture, satellite images of New York City are analyzed and latent feature variables are extracted. These features are used to derive clusters, which are correlated with healthcare indicators by means of a multivariate classification model. Thanks to this pipeline, it is possible to show that, in New York City, health care indexes are significantly correlated to the urban landscape. This pipeline can serve as a basis to ease urban planning, since the same interventions can be organized on similar areas, even if geographically distant. Full article
(This article belongs to the Special Issue Selected Papers from the 17th International Conference on Smart Living and Public Health (ICOST 2019))
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16 pages, 3565 KiB  
Article
Pilot Site Deployment of an IoT Solution for Older Adults’ Early Behavior Change Detection
by Hamdi Aloulou, Mounir Mokhtari and Bessam Abdulrazak
Sensors 2020, 20(7), 1888; https://doi.org/10.3390/s20071888 - 29 Mar 2020
Cited by 11 | Viewed by 2887
Abstract
The world demography is continuously changing. During the last decade, we noticed a regular variation in the world demography leading to a nearly balanced society share between the young and aging population. This increasing older adult population is facing many problems. In fact, [...] Read more.
The world demography is continuously changing. During the last decade, we noticed a regular variation in the world demography leading to a nearly balanced society share between the young and aging population. This increasing older adult population is facing many problems. In fact, the transition to the aging period is associated with physical, psychological, cognitive, and societal changes. Negative behavior changes are considered as indicators of older adults’ frailty. This is why it is important to detect such behavior changes early in order to prevent isolation, sedentary lifestyle, and even diseases, and therefore delay the frailty period. This paper exhibits a proof-of-concept pilot site deployment of an Internet of Thing (IoT) solution for the continuous monitoring and detection of older adults’ behavior changes. The objective is to help geriatricians detect sedentary lifestyle and health-related problems at an early stage. Full article
(This article belongs to the Special Issue Selected Papers from the 17th International Conference on Smart Living and Public Health (ICOST 2019))
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