sensors-logo

Journal Browser

Journal Browser

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: closed (30 September 2022) | Viewed by 10804

Special Issue Editor

Dr. Ivan Miguel Serrano Pires
E-Mail Website
Guest Editor
1. Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
2. Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
Interests: ambient assisted living technologies; health; sensor-based systems; machine learning; mobile innovative technologies
Special Issues, Collections and Topics 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 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 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

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

Published Papers (10 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Article
PATROL: Participatory Activity Tracking and Risk Assessment for Anonymous Elderly Monitoring
Sensors 2022, 22(18), 6965; https://doi.org/10.3390/s22186965 - 14 Sep 2022
Viewed by 340
Abstract
There has been a subsequent increase in the number of elderly people living alone, with contribution from advancement in medicine and technology. However, hospitals and nursing homes are crowded, expensive, and uncomfortable, while personal caretakers are expensive and few in number. Home monitoring [...] Read more.
There has been a subsequent increase in the number of elderly people living alone, with contribution from advancement in medicine and technology. However, hospitals and nursing homes are crowded, expensive, and uncomfortable, while personal caretakers are expensive and few in number. Home monitoring technologies are therefore on the rise. In this study, we propose an anonymous elderly monitoring system to track potential risks in everyday activities such as sleep, medication, shower, and food intake using a smartphone application. We design and implement an activity visualization and notification strategy method to identify risks easily and quickly. For evaluation, we added risky situations in an activity dataset from a real-life experiment with the elderly and conducted a user study using the proposed method and two other methods varying in visualization and notification techniques. With our proposed method, 75.2% of the risks were successfully identified, while 68.5% and 65.8% were identified with other methods. The average time taken to respond to notification was 176.46 min with the proposed method, compared to 201.42 and 176.9 min with other methods. Moreover, the interface analyzing and reporting time was also lower (28 s) in the proposed method compared to 38 and 54 s in other methods. Full article
(This article belongs to the Special Issue Mobile Health Technologies for Ambient Assisted Living and Healthcare)
Show Figures

Figure 1

Article
Empowering People with a User-Friendly Wearable Platform for Unobtrusive Monitoring of Vital Physiological Parameters
Sensors 2022, 22(14), 5226; https://doi.org/10.3390/s22145226 - 13 Jul 2022
Viewed by 421
Abstract
Elderly people feel vulnerable especially after they are dismissed from health care facilities and return home. The purpose of this work was to alleviate this sense of vulnerability and empower these people by giving them the opportunity to unobtrusively record their vital physiological [...] Read more.
Elderly people feel vulnerable especially after they are dismissed from health care facilities and return home. The purpose of this work was to alleviate this sense of vulnerability and empower these people by giving them the opportunity to unobtrusively record their vital physiological parameters. Bearing in mind all the parameters involved, we developed a user-friendly wrist-wearable device combined with a web-based application, to adequately address this need. The proposed compilation obtains the photoplethysmogram (PPG) from the subject’s wrist and simultaneously extracts, in real time, the physiological parameters of heart rate (HR), blood oxygen saturation (SpO2) and respiratory rate (RR), based on algorithms embedded on the wearable device. The described process is conducted solely within the device, favoring the optimal use of the available resources. The aggregated data are transmitted via Wi-Fi to a cloud environment and stored in a database. A corresponding web-based application serves as a visualization and analytics tool, allowing the individuals to catch a glimpse of their physiological parameters on a screen and share their digital information with health professionals who can perform further processing and obtain valuable health information. Full article
(This article belongs to the Special Issue Mobile Health Technologies for Ambient Assisted Living and Healthcare)
Show Figures

Figure 1

Article
Multi-Device Nutrition Control
Sensors 2022, 22(7), 2617; https://doi.org/10.3390/s22072617 - 29 Mar 2022
Cited by 1 | Viewed by 745
Abstract
Precision nutrition is a popular eHealth topic among several groups, such as athletes, people with dementia, rare diseases, diabetes, and overweight. Its implementation demands tight nutrition control, starting with nutritionists who build up food plans for specific groups or individuals. Each person then [...] Read more.
Precision nutrition is a popular eHealth topic among several groups, such as athletes, people with dementia, rare diseases, diabetes, and overweight. Its implementation demands tight nutrition control, starting with nutritionists who build up food plans for specific groups or individuals. Each person then follows the food plan by preparing meals and logging all food and water intake. However, the discipline demanded to follow food plans and log food intake results in high dropout rates. This article presents the concepts, requirements, and architecture of a solution that assists the nutritionist in building up and revising food plans and the user following them. It does so by minimizing human–computer interaction by integrating the nutritionist and user systems and introducing off-the-shelf IoT devices in the system, such as temperature sensors, smartwatches, smartphones, and smart bottles. An interaction time analysis using the keystroke-level model provides a baseline for comparison in future work addressing both the use of machine learning and IoT devices to reduce the interaction effort of users. Full article
(This article belongs to the Special Issue Mobile Health Technologies for Ambient Assisted Living and Healthcare)
Show Figures

Figure 1

Article
Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device
Sensors 2022, 22(4), 1449; https://doi.org/10.3390/s22041449 - 14 Feb 2022
Cited by 1 | Viewed by 774
Abstract
Glaucoma is a silent disease that leads to vision loss or irreversible blindness. Current deep learning methods can help glaucoma screening by extending it to larger populations using retinal images. Low-cost lenses attached to mobile devices can increase the frequency of screening and [...] Read more.
Glaucoma is a silent disease that leads to vision loss or irreversible blindness. Current deep learning methods can help glaucoma screening by extending it to larger populations using retinal images. Low-cost lenses attached to mobile devices can increase the frequency of screening and alert patients earlier for a more thorough evaluation. This work explored and compared the performance of classification and segmentation methods for glaucoma screening with retinal images acquired by both retinography and mobile devices. The goal was to verify the results of these methods and see if similar results could be achieved using images captured by mobile devices. The used classification methods were the Xception, ResNet152 V2 and the Inception ResNet V2 models. The models’ activation maps were produced and analysed to support glaucoma classifier predictions. In clinical practice, glaucoma assessment is commonly based on the cup-to-disc ratio (CDR) criterion, a frequent indicator used by specialists. For this reason, additionally, the U-Net architecture was used with the Inception ResNet V2 and Inception V3 models as the backbone to segment and estimate CDR. For both tasks, the performance of the models reached close to that of state-of-the-art methods, and the classification method applied to a low-quality private dataset illustrates the advantage of using cheaper lenses. Full article
(This article belongs to the Special Issue Mobile Health Technologies for Ambient Assisted Living and Healthcare)
Show Figures

Figure 1

Article
A CSI-Based Human Activity Recognition Using Deep Learning
Sensors 2021, 21(21), 7225; https://doi.org/10.3390/s21217225 - 30 Oct 2021
Cited by 5 | Viewed by 1363
Abstract
The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at [...] Read more.
The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users’ inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics of WiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities. Full article
(This article belongs to the Special Issue Mobile Health Technologies for Ambient Assisted Living and Healthcare)
Show Figures

Figure 1

Article
Usability, User Experience, and Acceptance Evaluation of CAPACITY: A Technological Ecosystem for Remote Follow-Up of Frailty
Sensors 2021, 21(19), 6458; https://doi.org/10.3390/s21196458 - 27 Sep 2021
Viewed by 862
Abstract
Frailty predisposes older persons to adverse events, and information and communication technologies can play a crucial role to prevent them. CAPACITY provides a means to remotely monitor variables with high predictive power for adverse events, enabling preventative personalized early interventions. This study aims [...] Read more.
Frailty predisposes older persons to adverse events, and information and communication technologies can play a crucial role to prevent them. CAPACITY provides a means to remotely monitor variables with high predictive power for adverse events, enabling preventative personalized early interventions. This study aims at evaluating the usability, user experience, and acceptance of a novel mobile system to prevent disability. Usability was assessed using the system usability scale (SUS); user experience using the user experience questionnaire (UEQ); and acceptance with the technology acceptance model (TAM) and a customized quantitative questionnaire. Data were collected at baseline (recruitment), and after three and six months of use. Forty-six participants used CAPACITY for six months; nine dropped out, leaving a final sample of 37 subjects. SUS reached a maximum averaged value of 83.68 after six months of use; no statistically significant values have been found to demonstrate that usability improves with use, probably because of a ceiling effect. UEQ, obtained averages scores higher or very close to 2 in all categories. TAM reached a maximum of 51.54 points, showing an improvement trend. Results indicate the success of the participatory methodology, and support user centered design as a key methodology to design technologies for frail older persons. Involving potential end users and giving them voice during the design stage maximizes usability and acceptance. Full article
(This article belongs to the Special Issue Mobile Health Technologies for Ambient Assisted Living and Healthcare)
Show Figures

Figure 1

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
Cited by 2 | Viewed by 905
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)
Show Figures

Figure 1

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 8 | Viewed by 1468
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)
Show Figures

Figure 1

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 6 | Viewed by 2116
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)
Show Figures

Figure 1

Review

Jump to: Research

Review
Can the Eight Hop Test Be Measured with Sensors? A Systematic Review
Sensors 2022, 22(9), 3582; https://doi.org/10.3390/s22093582 - 08 May 2022
Viewed by 710
Abstract
Rehabilitation aims to increase the independence and physical function after injury, surgery, or other trauma, so that patients can recover to their previous ability as much as possible. To be able to measure the degree of recovery and impact of the treatment, various [...] Read more.
Rehabilitation aims to increase the independence and physical function after injury, surgery, or other trauma, so that patients can recover to their previous ability as much as possible. To be able to measure the degree of recovery and impact of the treatment, various functional performance tests are used. The Eight Hop Test is a hop exercise that is directly linked to the rehabilitation of people suffering from tendon and ligament injuries on the lower limb. This paper presents a systematic review on the use of sensors for measuring functional movements during the execution of the Eight Hop Test, focusing primarily on the use of sensors, related diseases, and different methods implemented. Firstly, an automated search was performed on the publication databases: PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Secondly, the publications related to the Eight-Hop Test and sensors were filtered according to several search criteria and 15 papers were finally selected to be analyzed in detail. Our analysis found that the Eight Hop Test measurements can be performed with motion, force, and imaging sensors. Full article
(This article belongs to the Special Issue Mobile Health Technologies for Ambient Assisted Living and Healthcare)
Show Figures

Figure 1

Back to TopTop