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Data Science for Human Health Monitoring with Smart Sensors

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 1145

Special Issue Editors


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Guest Editor
División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/I.T. Orizaba, Orizaba, 94320 Veracruz, Mexico
Interests: databases; data mining; data science; big data

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Guest Editor
Universidad Autónoma del Estado de México, Centro Universitario UAEM Zumpango, Zumpango, 55600 Estado de México, Mexico
Interests: machine learning; data mining; data science

Special Issue Information

Dear Colleagues,

Thanks to the wide popularity of smart sensors, at present, large datasets from health monitoring systems are available; these devices, such as wearable and hydrogel sensors, allow us to track multiple vital and behavioral parameters of the human body, demonstrating their pertinence to preventive medicine. Smart sensors combined with data science techniques, e.g., machine learning and deep learning, completely automate health monitoring systems. For instance, deep learning is used to develop a smart insole system for foot care applications. Likewise, intelligent wearables utilize machine learning algorithms in processing big data and discovering precise knowledge in domains like health, cognition and sports.

This Special Issue on “Data Science for Human Health Monitoring with Smart Sensors” welcomes submissions related to data science techniques, machine learning, deep learning and other technologies applied to integrate, process, store, query, analyze, and visualize smart sensor data from human health monitoring systems.

Relevant topics include, but are not limited to, the following:

  • Data analytics for human health:
    • Data warehousing;
    • Data lakes;
    • Data Lakehouse;
    • Online analytics processing
    • ETL (extraction, transformation, and load) or ELT (extraction, load, and transformation) process design.
  • Data mining and health monitoring systems.
  • IA and human health.
    • Machine learning;
    • Deep learning;
    • Natural language processing;
  • Big data and human health applications
    • Storage;
    • Processing;
    • Analytics;
    • Visualization.

Prof. Dr. Lisbeth Rodríguez Mazahua
Prof. Dr. Asdrúbal López Chau
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. Applied Sciences 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 2400 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

  • big data
  • deep learning
  • machine learning
  • data science
  • smart sensors
  • wearable sensors
  • human health monitoring

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Published Papers (1 paper)

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Review

33 pages, 2346 KiB  
Review
Innovations and Technological Advances in Healthcare Remote Monitoring Systems for the Elderly and Vulnerable People: A Scoping Review
by Diana Lizet González-Baldovinos, Luis Pastor Sánchez-Fernández, Jose Luis Cano-Rosas, Asdrúbal López-Chau and Pedro Guevara-López
Appl. Sci. 2025, 15(6), 3200; https://doi.org/10.3390/app15063200 - 14 Mar 2025
Viewed by 395
Abstract
The ever-evolving landscape of healthcare demands innovative solutions, particularly in light of the global health crisis of 2020 and the aging global population. Technological advancements and new approaches in remote health monitoring systems have helped to bridge the gap for vulnerable individuals such [...] Read more.
The ever-evolving landscape of healthcare demands innovative solutions, particularly in light of the global health crisis of 2020 and the aging global population. Technological advancements and new approaches in remote health monitoring systems have helped to bridge the gap for vulnerable individuals such as older adults. This review explores methods for the analysis of physiological signals using remote and intelligent systems and mobile and web-based applications, mostly linked to wearable devices, focusing primarily on the elderly population. The main objective is to identify crucial advancements in the development or integration of technology applied to addressing challenges of this magnitude. The research is structured following the PRISMA-ScR guidelines. The search strategy was implemented in databases such as the ACM Digital Library, IEEE Xplore, PubMed, Science Direct, Scopus, and Springer Link. A total of 411 articles were collected, and inclusion and exclusion criteria were applied to focus on studies published between 2020 and 2024. Ultimately, 100 articles from 35 countries were selected for data extraction. The findings reveal significant progress in remote monitoring technologies but emphasize the need for rigorous validation to ensure accuracy and reliability across diverse populations. To develop robust systems that provide equitable and high-quality healthcare, it is essential to address critical challenges such as data privacy, security, accessibility, and ethical considerations. Full article
(This article belongs to the Special Issue Data Science for Human Health Monitoring with Smart Sensors)
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