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Participative Urban Health and Healthy Ageing in the Age of AI...
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Participative Urban Health and Healthy Ageing in the Age of AI – "Selected Papers from International Conference on Smart Living and Public Health

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 8751

Special Issue Editors

Dr. Hamdi Aloulou

E-Mail Website
Guest Editor
Digital Research Center of Sfax, ReDCAD, Sfax, Tunisia
Interests: internet of things and context awareness applied for the development of intelligent spaces for public health, ageing, and wellbeing
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. Antoine De Marassé-Enouf

E-Mail Website
Guest Editor
Agence du Numérique en Santé (ANS, French eHealth Agency), 75015 Paris, France
Interests: E-health; embedded systems
Prof. Dr. Mounir Mokhtari

E-Mail Website
Guest Editor
Institut Mines–Telecom, 91120 Palaiseau, France
Interests: human–machine interaction; ambient assisted living; semantic reasoning; wearable sensors; human behavior monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The International Conference on Smart Living and Public Health (ICOST, https://www.icost-society.org) provides a premier venue for the presentation and discussion of research on the design, development, deployment, and evaluation of AI for healthcare, smart urban environments, assistive technologies, chronic disease management, and coaching and health telematics systems. The conference has brought together global communities for over almost 20 years to raise awareness of frail and dependent people's quality of life.

The ICOST focuses on the impact of ICTs on public health and the wellbeing of citizens all over the world. The 19th edition of the conference will explore the theme “Participative Urban Health and Ageing Well in the Age of AI".

Authors whose papers were selected for the conference will be invited to submit extended versions of their original papers and contributions (a 50% extension of the contents of the conference paper is required).

Topics of interest include: internet of things and artificial intelligence solutions for e-health; deep learning for health and wellbeing; big data analytics for public health; decision support systems for healthcare; biomedical and health informatics; preventive and predictive healthcare systems; virtual personal assistant for e-health; smart cities; human-centered design; and human–computer interaction

Dr. Hamdi Aloulou
Prof. Dr. Bessam Abdulrazak
Dr. Antoine De Marassé-Enouf
Prof. Dr. Mounir Mokhtari
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.

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Published Papers (3 papers)

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Research

16 pages, 4018 KiB  
Article
Data-Driven Smart Living Lab to Promote Participation in Rehabilitation Exercises and Sports Programs for People with Disabilities in Local Communities
by Seung Bok Lee, Yim Taek Oh, Seung Wan Yang and Jong Bae Kim
Sensors 2023, 23(5), 2761; https://doi.org/10.3390/s23052761 - 2 Mar 2023
Cited by 1 | Viewed by 2159
Abstract
Patients discharged from hospitals after an inpatient course of medical treatment for any ailment or traumatic injury that results in disabling conditions and are rendered mobility impaired require ongoing systematic sports and exercise programs to maintain healthy lifestyles. Under such circumstances, a rehabilitation [...] Read more.
Patients discharged from hospitals after an inpatient course of medical treatment for any ailment or traumatic injury that results in disabling conditions and are rendered mobility impaired require ongoing systematic sports and exercise programs to maintain healthy lifestyles. Under such circumstances, a rehabilitation exercise and sports center, accessible throughout local communities, is critical for promoting beneficial living and community participation for these individuals with disabilities. An innovative data-driven system equipped with state-of-the-art smart and digital equipment, set up in architecturally barrier-free infrastructures, is essential for these individuals to promote health maintenance and overcome secondary medical complications following an acute inpatient hospitalization or suboptimal rehabilitation. A federally funded collaborative research and development (R&D) program proposes to build a multi-ministerial data-driven system of exercise programs using a smart digital living lab as a platform to provide pilot services in physical education and counseling with exercise and sports programs for this patient population. We describe the social and critical aspects of rehabilitating such a population of patients by presenting a full study protocol. A modified sub-dataset of the previously generated 280-item full dataset is applied using a data-collecting system—“The Elephant”—as an example of how data acquisition will be achieved to assess the effects of lifestyle rehabilitative exercise programs for people with disabilities. Full article
(This article belongs to the Special Issue Participative Urban Health and Healthy Ageing in the Age of AI – "Selected Papers from International Conference on Smart Living and Public Health)
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17 pages, 1068 KiB  
Article
Interaction with a Virtual Coach for Active and Healthy Ageing
by Michael McTear, Kristiina Jokinen, Mirza Mohtashim Alam, Qasid Saleem, Giulio Napolitano, Florian Szczepaniak, Mossaab Hariz, Gérard Chollet, Christophe Lohr, Jérôme Boudy, Zohre Azimi, Sonja Dana Roelen and Rainer Wieching
Sensors 2023, 23(5), 2748; https://doi.org/10.3390/s23052748 - 2 Mar 2023
Cited by 4 | Viewed by 2388
Abstract
Since life expectancy has increased significantly over the past century, society is being forced to discover innovative ways to support active aging and elderly care. The e-VITA project, which receives funding from both the European Union and Japan, is built on a cutting [...] Read more.
Since life expectancy has increased significantly over the past century, society is being forced to discover innovative ways to support active aging and elderly care. The e-VITA project, which receives funding from both the European Union and Japan, is built on a cutting edge method of virtual coaching that focuses on the key areas of active and healthy aging. The requirements for the virtual coach were ascertained through a process of participatory design in workshops, focus groups, and living laboratories in Germany, France, Italy, and Japan. Several use cases were then chosen for development utilising the open-source Rasa framework. The system uses common representations such as Knowledge Bases and Knowledge Graphs to enable the integration of context, subject expertise, and multimodal data, and is available in English, German, French, Italian, and Japanese. Full article
(This article belongs to the Special Issue Participative Urban Health and Healthy Ageing in the Age of AI – "Selected Papers from International Conference on Smart Living and Public Health)
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19 pages, 2649 KiB  
Article
Stress Estimation Using Biometric and Activity Indicators to Improve QoL of the Elderly
by Kanta Matsumoto, Tomokazu Matsui, Hirohiko Suwa and Keiichi Yasumoto
Sensors 2023, 23(1), 535; https://doi.org/10.3390/s23010535 - 3 Jan 2023
Cited by 1 | Viewed by 3076
Abstract
It is essential to estimate the stress state of the elderly to improve their QoL. Stress states change every day and hour, depending on the activities performed and the duration/intensity. However, most existing studies estimate stress states using only biometric information or specific [...] Read more.
It is essential to estimate the stress state of the elderly to improve their QoL. Stress states change every day and hour, depending on the activities performed and the duration/intensity. However, most existing studies estimate stress states using only biometric information or specific activities (e.g., sleep duration, exercise duration/amount, etc.) as explanatory variables and do not consider all daily living activities. It is necessary to link various daily living activities and biometric information in order to estimate the stress state more accurately. Specifically, we construct a stress estimation model using machine learning with the answers to a stress status questionnaire obtained every morning and evening as the ground truth and the biometric data during each of the performed activities and the new proposed indicator including biological and activity perspectives as the features. We used the following methods: Baseline Method 1, in which the RRI variance and Lorenz plot area for 4 h after waking and 24 h before the questionnaire were used as features; Baseline Method 2, in which sleep time was added as a feature to Baseline Method 1; the proposed method, in which the Lorenz plot area per activity and total time per activity were added. We compared the results with the proposed method, which added the new indicators as the features. The results of the evaluation experiments using the one-month data collected from five elderly households showed that the proposed method had an average estimation accuracy of 59%, 7% better than Baseline Method 1 (52%) and 4% better than Baseline Method 2 (55%). Full article
(This article belongs to the Special Issue Participative Urban Health and Healthy Ageing in the Age of AI – "Selected Papers from International Conference on Smart Living and Public Health)
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