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Special Issue "Mobile Health Technologies for Ambient Assisted Living and Healthcare"

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

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editor

Dr. Ivan Miguel Serrano Pires
E-Mail Website
Guest Editor
1. Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal
2. Polytechnic Institute of Viseu, Viseu, Portugal
Interests: ambient assisted living technologies; health; sensor-based systems; machine learning; mobile innovative technologies
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, the use of telemedicine and mobile devices is expanding in realms where sensors may help in the development of innovative solutions. The development of these solutions is important for the monitoring of elderly people, tracking lifestyles, healthcare treatments and others.

The motivation for this Special Issue is to bring together researchers and practitioners interested in the application of information and communication technologies (ICT) to healthcare and medicine in general and to the support of persons with special needs. This research field is closely related to the development of assistive technologies for different types of people, for the monitoring of sports and others. The development of these solutions for medical purposes should be validated according to the EU General Data Protection Regulation (GDPR) and the privacy of the data is important. Other accepted research studies may be related to the security and privacy of the information for its acceptance. In the case of medical rehabilitation and assistive technology, research in and applications of ICT have contributed greatly to the enhancement of quality of life and full integration of all citizens into society. Databases, networking, graphical interfaces, data mining, machine learning, intelligent decision support systems and specialized programming languages are just a few of the technologies and research areas currently contributing to medical informatics.

Previously, we started the promotion of the creation of m-Health and e-Health solutions for healthcare professionals, because the mobile devices are commonly widely used for the different daily tasks, and they include sensors that allow the monitoring of different physical and physiological parameters. There are different solutions currently under development related to this field and, as it contributes to improve the quality of life, it can interact in the development of technologies for social help.

This Special Issue aims to collect original research papers or review papers on advances in the technologies for the design of solutions for Ambient Assisted Living and Healthcare. Topics include but are not limited to:

  • Health care information systems interoperability, security and efficiency
  • Ambient intelligence for wellbeing and e-health applications, supported by RFID technology and Wireless Sensor Networks
  • Mobile applications and ubiquitous devices in Healthcare and lifestyle training
  • Robotic systems and devices for health care and medicine
  • Technologies to promote a healthy and secure society
  • Big Data Analytics for e-health
  • Assessment of Acceptance/Adoption models
  • Cultural Evaluation of e-health
  • Acceptance e-health and economic growth factors affecting e-health adoption
  • Machine learning for healthcare
  • Intelligent systems for young and elderly people using mobile devices
  • Activities of daily living
  • Human factors, efficient cost control and management in society
  • Intelligent decision support and data systems in health care, medicine and society
  • Innovation in people supporting activities (e.g., health care, schooling and services)
  • Embedded systems for healthcare
  • IT Acceptance Models Acceptance of e-health services
  • Major barriers and facilitators for e-health
  • Biosignal Acquisition, Analysis and Processing
  • Semantic Technologies and Cognition
  • Neural Networks
  • Physiological Computing in Mobile Devices
  • Telemedicine
  • Physiological Computing in Mobile Devices
  • Augmented Reality in Healthcare using wearable devices
  • Sensors and Actuators
  • ICT for development
  • Cloud computing for healthcare
  • Mobile application concepts and technologies for different mobile platforms

Dr. Ivan Miguel Serrano Pires
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 papers will be 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 2200 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

  • Ambient assisted living
  • Health technologies
  • Mobile devices
  • Data processing
  • Data acquisition
  • Sensors
  • Artificial Intelligence

Published Papers (3 papers)

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Research

Article
Using Direct Acyclic Graphs to Enhance Skeleton-Based Action Recognition with a Linear-Map Convolution Neural Network
Sensors 2021, 21(9), 3112; https://doi.org/10.3390/s21093112 - 29 Apr 2021
Viewed by 428
Abstract
Research on the human activity recognition could be utilized for the monitoring of elderly people living alone to reduce the cost of home care. Video sensors can be easily deployed in the different zones of houses to achieve monitoring. The goal of this [...] Read more.
Research on the human activity recognition could be utilized for the monitoring of elderly people living alone to reduce the cost of home care. Video sensors can be easily deployed in the different zones of houses to achieve monitoring. The goal of this study is to employ a linear-map convolutional neural network (CNN) to perform action recognition with RGB videos. To reduce the amount of the training data, the posture information is represented by skeleton data extracted from the 300 frames of one film. The two-stream method was applied to increase the accuracy of recognition by using the spatial and motion features of skeleton sequences. The relations of adjacent skeletal joints were employed to build the direct acyclic graph (DAG) matrices, source matrix, and target matrix. Two features were transferred by DAG matrices and expanded as color texture images. The linear-map CNN had a two-dimensional linear map at the beginning of each layer to adjust the number of channels. A two-dimensional CNN was used to recognize the actions. We applied the RGB videos from the action recognition datasets of the NTU RGB+D database, which was established by the Rapid-Rich Object Search Lab, to execute model training and performance evaluation. The experimental results show that the obtained precision, recall, specificity, F1-score, and accuracy were 86.9%, 86.1%, 99.9%, 86.3%, and 99.5%, respectively, in the cross-subject source, and 94.8%, 94.7%, 99.9%, 94.7%, and 99.9%, respectively, in the cross-view source. An important contribution of this work is that by using the skeleton sequences to produce the spatial and motion features and the DAG matrix to enhance the relation of adjacent skeletal joints, the computation speed was faster than the traditional schemes that utilize single frame image convolution. Therefore, this work exhibits the practical potential of real-life action recognition. Full article
(This article belongs to the Special Issue Mobile Health Technologies for Ambient Assisted Living and Healthcare)
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Article
An Experimental Study on the Validity and Reliability of a Smartphone Application to Acquire Temporal Variables during the Single Sit-to-Stand Test with Older Adults
Sensors 2021, 21(6), 2050; https://doi.org/10.3390/s21062050 - 15 Mar 2021
Cited by 1 | Viewed by 623
Abstract
Smartphone sensors have often been proposed as pervasive measurement systems to assess mobility in older adults due to their ease of use and low-cost. This study analyzes a smartphone-based application’s validity and reliability to quantify temporal variables during the single sit-to-stand test with [...] Read more.
Smartphone sensors have often been proposed as pervasive measurement systems to assess mobility in older adults due to their ease of use and low-cost. This study analyzes a smartphone-based application’s validity and reliability to quantify temporal variables during the single sit-to-stand test with institutionalized older adults. Forty older adults (20 women and 20 men; 78.9 ± 8.6 years) volunteered to participate in this study. All participants performed the single sit-to-stand test. Each sit-to-stand repetition was performed after an acoustic signal was emitted by the smartphone app. All data were acquired simultaneously with a smartphone and a digital video camera. The measured temporal variables were stand-up time and total time. The relative reliability and systematic bias inter-device were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots. In contrast, absolute reliability was assessed using the standard error of measurement and coefficient of variation (CV). Inter-device concurrent validity was assessed through correlation analysis. The absolute percent error (APE) and the accuracy were also calculated. The results showed excellent reliability (ICC = 0.92–0.97; CV = 1.85–3.03) and very strong relationships inter-devices for the stand-up time (r = 0.94) and the total time (r = 0.98). The APE was lower than 6%, and the accuracy was higher than 94%. Based on our data, the findings suggest that the smartphone application is valid and reliable to collect the stand-up time and total time during the single sit-to-stand test with older adults. Full article
(This article belongs to the Special Issue Mobile Health Technologies for Ambient Assisted Living and Healthcare)
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Article
A Case Study on the Development of a Data Privacy Management Solution Based on Patient Information
Sensors 2020, 20(21), 6030; https://doi.org/10.3390/s20216030 - 23 Oct 2020
Cited by 4 | Viewed by 1051
Abstract
Data on diagnosis of infection in the general population are strategic for different applications in the public and private spheres. Among them, the data related to symptoms and people displacement stand out, mainly considering highly contagious diseases. This data is sensitive and requires [...] Read more.
Data on diagnosis of infection in the general population are strategic for different applications in the public and private spheres. Among them, the data related to symptoms and people displacement stand out, mainly considering highly contagious diseases. This data is sensitive and requires data privacy initiatives to enable its large-scale use. The search for population-monitoring strategies aims at social tracking, supporting the surveillance of contagions to respond to the confrontation with Coronavirus 2 (COVID-19). There are several data privacy issues in environments where IoT devices are used for monitoring hospital processes. In this research, we compare works related to the subject of privacy in the health area. To this end, this research proposes a taxonomy to support the requirements necessary to control patient data privacy in a hospital environment. According to the tests and comparisons made between the variables compared, the application obtained results that contribute to the scenarios applied. In this sense, we modeled and implemented an application. By the end, a mobile application was developed to analyze the privacy and security constraints with COVID-19. Full article
(This article belongs to the Special Issue Mobile Health Technologies for Ambient Assisted Living and Healthcare)
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