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Smart Environments for Health and Well-Being

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

Deadline for manuscript submissions: 20 March 2024 | Viewed by 7507

Special Issue Editors

Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
Interests: data engineering; knowledge engineering; IT security; IoT; smart homes
Dr. Allal Tiberkak
E-Mail Website
Co-Guest Editor
Department of Mathematics and Computer Science, Faculty of Sciences, University of Medea, Medea 26000, Algeria
Interests: smart environments; digital health; multimedia on the Internet; security and privacy; web/internet of things
The Center for the Development of Advanced Technologies (CDTA), Algiers, Algeria
Interests: robotics; multi-agent systems; cyber-physical systems; human-robot interaction

Special Issue Information

Dear Colleagues,

A smart environment for health and well-being may be defined as a system of interconnected smart devices (sensors, actuators, displays, etc.) whose primary purpose is to make people's lives more comfortable and to improve their health and well-being.

The aim of this Special Issue is to contribute to the scientific knowledge of building smart environments for health and well-being.

The Special Issue invites authors to present original works describing their research results; theoretical, practical, or industrial solutions (prototype, formal modeling, augmented reality, machine learning, big data, web and Internet of things, system theory, optimization, robotics, etc.); and discussions of innovative ideas that have the potential to build human-centric smart environments for health and well-being.

Dr. Mehdi Adda
Dr. Allal Tiberkak
Dr. Abdelfetah Hentout
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

  • artificial intelligence
  • deep learning
  • smart devices
  • wearables
  • e-health
  • healthcare
  • smart health
  • machine learning
  • medical expert systems
  • pervasive computing
  • medical decision-making
  • medical data
  • security and privacy, smart environment
  • networks
  • distributed systems
  • virtual reality
  • augmented reality
  • blockchain
  • quality of life
  • primary health caregiver
  • activities of daily living
  • telemedicine
  • remote monitoring
  • human/smart environment interaction
  • physical human–robot interaction
  • ergonomics
  • localization and tracking
  • mobile computing
  • Internet of things
  • image, speech, and signal processing
  • fuzzy control
  • data metrics and analytics
  • trust models and trust policies
  • access control
  • legal aspects for medical data

Published Papers (3 papers)

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Research

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24 pages, 5438 KiB  
Article
Telehealth-Enabled In-Home Elbow Rehabilitation for Brachial Plexus Injuries Using Deep-Reinforcement-Learning-Assisted Telepresence Robots
Sensors 2024, 24(4), 1273; https://doi.org/10.3390/s24041273 - 17 Feb 2024
Viewed by 281
Abstract
Due to damage to the network of nerves that regulate the muscles and feeling in the shoulder, arm, and forearm, brachial plexus injuries (BPIs) are known to significantly reduce the function and quality of life of affected persons. According to the World Health [...] Read more.
Due to damage to the network of nerves that regulate the muscles and feeling in the shoulder, arm, and forearm, brachial plexus injuries (BPIs) are known to significantly reduce the function and quality of life of affected persons. According to the World Health Organization (WHO), a considerable share of global disability-adjusted life years (DALYs) is attributable to upper limb injuries, including BPIs. Telehealth can improve access concerns for patients with BPIs, particularly in lower-middle-income nations. This study used deep reinforcement learning (DRL)-assisted telepresence robots, specifically the deep deterministic policy gradient (DDPG) algorithm, to provide in-home elbow rehabilitation with elbow flexion exercises for BPI patients. The telepresence robots were used for a six-month deployment period, and DDPG drove the DRL architecture to maximize patient-centric exercises with its robotic arm. Compared to conventional rehabilitation techniques, patients demonstrated an average increase of 4.7% in force exertion and a 5.2% improvement in range of motion (ROM) with the assistance of the telepresence robot arm. According to the findings of this study, telepresence robots are a valuable and practical method for BPI patients’ at-home rehabilitation. This technology paves the way for further research and development in telerehabilitation and can be crucial in addressing broader physical rehabilitation challenges. Full article
(This article belongs to the Special Issue Smart Environments for Health and Well-Being)
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22 pages, 4927 KiB  
Article
Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns
Sensors 2022, 22(13), 4803; https://doi.org/10.3390/s22134803 - 25 Jun 2022
Cited by 3 | Viewed by 1686
Abstract
The monitoring of the daily life activities routine is beneficial, especially in old age. It can provide relevant information on the person’s health state and wellbeing and can help identify deviations that signal care deterioration or incidents that require intervention. Existing approaches consider [...] Read more.
The monitoring of the daily life activities routine is beneficial, especially in old age. It can provide relevant information on the person’s health state and wellbeing and can help identify deviations that signal care deterioration or incidents that require intervention. Existing approaches consider the daily routine as a rather strict sequence of activities which is not usually the case. In this paper, we propose a solution to identify flexible daily routines of older adults considering variations related to the order of activities and activities timespan. It combines the Gap-BIDE algorithm with a collaborative clustering technique. The Gap-BIDE algorithm is used to identify the most common patterns of behavior considering the elements of variations in activities sequence and the period of the day (i.e., night, morning, afternoon, and evening) for increased pattern mining flexibility. K-means and Hierarchical Clustering Agglomerative algorithms are collaboratively used to address the time-related elements of variability in daily routine like activities timespan vectors. A prototype was developed to monitor and detect the daily living activities based on smartwatch data using a deep learning architecture and the InceptionTime model, for which the highest accuracy was obtained. The results obtained are showing that the proposed solution can successfully identify the routines considering the aspects of flexibility such as activity sequences, optional and compulsory activities, timespan, and start and end time. The best results were obtained for the collaborative clustering solution that considers flexibility aspects in routine identification, providing coverage of monitored data of 89.63%. Full article
(This article belongs to the Special Issue Smart Environments for Health and Well-Being)
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Review

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39 pages, 1255 KiB  
Review
Reviewing Federated Machine Learning and Its Use in Diseases Prediction
Sensors 2023, 23(4), 2112; https://doi.org/10.3390/s23042112 - 13 Feb 2023
Cited by 15 | Viewed by 4208
Abstract
Machine learning (ML) has succeeded in improving our daily routines by enabling automation and improved decision making in a variety of industries such as healthcare, finance, and transportation, resulting in increased efficiency and production. However, the development and widespread use of this technology [...] Read more.
Machine learning (ML) has succeeded in improving our daily routines by enabling automation and improved decision making in a variety of industries such as healthcare, finance, and transportation, resulting in increased efficiency and production. However, the development and widespread use of this technology has been significantly hampered by concerns about data privacy, confidentiality, and sensitivity, particularly in healthcare and finance. The “data hunger” of ML describes how additional data can increase performance and accuracy, which is why this question arises. Federated learning (FL) has emerged as a technology that helps solve the privacy problem by eliminating the need to send data to a primary server and collect it where it is processed and the model is trained. To maintain privacy and improve model performance, FL shares parameters rather than data during training, in contrast to the typical ML practice of sending user data during model development. Although FL is still in its infancy, there are already applications in various industries such as healthcare, finance, transportation, and others. In addition, 32% of companies have implemented or plan to implement federated learning in the next 12–24 months, according to the latest figures from KPMG, which forecasts an increase in investment in this area from USD 107 million in 2020 to USD 538 million in 2025. In this context, this article reviews federated learning, describes it technically, differentiates it from other technologies, and discusses current FL aggregation algorithms. It also discusses the use of FL in the diagnosis of cardiovascular disease, diabetes, and cancer. Finally, the problems hindering progress in this area and future strategies to overcome these limitations are discussed in detail. Full article
(This article belongs to the Special Issue Smart Environments for Health and Well-Being)
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