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Adaptive and Intelligent Sensors for Mobile Health

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

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 15542

Special Issue Editors


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Guest Editor
Department of Computer Architecture and Technology, University of Seville, Sevilla, Spain
Interests: computer and communication systems architectures; mobile robotics; eHealth, eInclusion, and rehabilitation systems

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Guest Editor
Department of Electronic Technology, ETSI Informática, Universidad de Sevilla, Instituto de Ingeniería Informática de la Universidad de Sevilla, Sevilla, Spain
Interests: digital health products; services
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Information Processing Science, University of Oulu, Oulu, Finland
Interests: multiple sclerosis; mHealth; patient empowerment; user-centered design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mobile health (mhealth) entails the use of mobile devices and sensors to monitor and control health-related information and activities. When used together with artificial intelligence techniques, these mHealth devices become smart tools that can process and analyze the data collected, allowing more precise diagnostics and personalized health solutions. These solutions should be adapted to different users’ preferences, contexts, and condition progression. The combination of this participatory design approach with the implementation of smart personalization features results in amazing user experiences that ensure high adoption, increased adherence, and enhanced user engagement. In addition, healthcare costs are reduced by shifting healthcare from the traditional clinical environment to the patients’ daily living.

In this Special Issue, our aim is to publish novel research works on sensor-based mobile health solutions, particularly those including intelligent techniques to allow personalization and adaptation to the evolution of the users’ circumstances or contexts. Topics of interest include but are not limited to:

  • Sensors for smart mHealth devices;
  • Wearable and medical sensors;
  • IoT systems for healthcare and the elderly;
  • Machine/deep learning and artificial intelligence in mHealth;
  • Context-aware and adaptive systems for mHealth.

We encourage submissions of papers presenting original research findings; innovative personalized mHealth solutions, techniques, and tools; or novel reviews that could summarize the current state of the art on the topic. We hope that these contributions may facilitate future research and development efforts in this area.

Prof. Dr. Jose Luis Sevillano
Dr. Octavio Rivera
Dr. Manuel Dominguez-Morales
Dr. Guido Giunti
Guest Editor

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

  • mobile health
  • intelligent sensors
  • adaptive systems
  • participatory health informatics
  • deep learning

Published Papers (2 papers)

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Research

18 pages, 3735 KiB  
Article
New App-Based Dietary and Lifestyle Intervention on Weight Loss and Cardiovascular Health
by Alejandro Martínez-Rodríguez, María Martínez-Olcina, Juan Mora, Pau Navarro, Nuria Caturla and Jonathan Jones
Sensors 2022, 22(3), 768; https://doi.org/10.3390/s22030768 - 20 Jan 2022
Cited by 3 | Viewed by 2899
Abstract
Consumer digital technology is rapidly evolving, allowing users to manage their health in a simple, non-invasive manner. However, there are few studies revealing if using digital technology as part of an intervention really has an impact in consumer health compared with traditional strategies. [...] Read more.
Consumer digital technology is rapidly evolving, allowing users to manage their health in a simple, non-invasive manner. However, there are few studies revealing if using digital technology as part of an intervention really has an impact in consumer health compared with traditional strategies. The objective of the current study is to compare two groups (MTB; n = 18, 46.1 ± 10.4 years and MTBAPP; n = 19, 45.3 ± 6.40 years) of overweight, prehypertensive individuals in losing weight and lowering their blood pressure. Both were provided with nutritionist-guided recommendations, a wearable tracking device and a dietary supplement that has previously been proven to help lose body weight and lower blood pressure. In addition, one of the groups (MTBAPP) used a mobile app specifically designed for the intervention. Blood pressure, body composition, triglyceride level, peak expiratory flow, forced expiratory volume in the first second and maximum oxygen volume were measured at different time points. In addition, participants were monitored with an activity bracelet throughout the intervention. As a result, both groups significantly lost body weight, while the group using the app additionally improved blood pressure levels and lowered fat mass. Furthermore, the app users significantly increased the number of daily steps and decreased sedentary time. In conclusion, the addition of a mobile app with daily reminders to follow healthy lifestyle recommendations increased physical activity and overall improved blood pressure and fat mass levels when compared with a group performing the same intervention but in absence of the mobile application. Full article
(This article belongs to the Special Issue Adaptive and Intelligent Sensors for Mobile Health)
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16 pages, 7253 KiB  
Article
A Deep-Learning Based Posture Detection System for Preventing Telework-Related Musculoskeletal Disorders
by Enrique Piñero-Fuentes, Salvador Canas-Moreno, Antonio Rios-Navarro, Manuel Domínguez-Morales, José Luis Sevillano and Alejandro Linares-Barranco
Sensors 2021, 21(15), 5236; https://doi.org/10.3390/s21155236 - 02 Aug 2021
Cited by 15 | Viewed by 11047
Abstract
The change from face-to-face work to teleworking caused by the pandemic has induced multiple workers to spend more time than usual in front of a computer; in addition, the sudden installation of workstations in homes means that not all of them meet the [...] Read more.
The change from face-to-face work to teleworking caused by the pandemic has induced multiple workers to spend more time than usual in front of a computer; in addition, the sudden installation of workstations in homes means that not all of them meet the necessary characteristics for the worker to be able to position himself/herself comfortably with the correct posture in front of their computer. Furthermore, from the point of view of the medical personnel in charge of occupational risk prevention, an automated tool able to quantify the degree of incorrectness of a postural habit in a worker is needed. For this purpose, in this work, a system based on the postural detection of the worker is designed, implemented and tested, using a specialized hardware system that processes video in real time through convolutional neural networks. This system is capable of detecting the posture of the neck, shoulders and arms, providing recommendations to the worker in order to prevent possible health problems, due to poor posture. The results of the proposed system show that this video processing can be carried out in real time (up to 25 processed frames/sec) with a low power consumption (less than 10 watts) using specialized hardware, obtaining an accuracy of over 80% in terms of the pattern detected. Full article
(This article belongs to the Special Issue Adaptive and Intelligent Sensors for Mobile Health)
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