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Special Issue "Sensor Technologies for Caring People with Disabilities"

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

Deadline for manuscript submissions: closed (28 February 2019)

Special Issue Editors

Guest Editor
Prof. Dr. Francisco José José García-Peñalvo

Department of Computer Science, University of Salamanca, Salamanca 37008, Spain
Website | E-Mail
Interests: information systems, human factors in computing; project management in information-systems development; global and distributed software-engineering; systems, services, and software process improvement and innovation; management information systems; business software; innovation in IT
Guest Editor
Dr. Manuel Franco-Martín

Department of Psychiatry, Zamora Hospital, Zamora, Spain
Website | E-Mail
Interests: psychiatry; dementia; caring for people with disabilities

Special Issue Information

Dear Colleagues,

According to the World Health Organization, over a billion people, about 15% of the world's population, have some form of disability. Furthermore, rapid growth of the aging population is causing an increase in chronic health conditions, and therefore a rise in the population rates of disability. Additionally, people with disabilities have less access to health care services and are more prone to experience unmet health care needs.

In this sense, recent advances in sensor research and innovation have boosted the prospects of the use of these technologies for assisting people with disabilities. Sensors are used for many different purposes in regards to disabled people. Monitoring and alarm systems, for example, can ameliorate the adverse effects of unpredictable events, such as sudden illness, falls, or wandering. Pressure sensors have been employed in robotics for the treatment of children with autism. IMUs and laser systems have been used in building a virtual cane for the blind. In sort, the use of sensors can improve the quality of life of people with disabilities, as well as promoting their independence.

Taking the above into account, research in sensor technologies for the disabled is an open field which needs attention from the research community. Thus, the aim of this Special Issue is to present recent developments on sensor technologies for caring people with disabilities, focusing on the different configurations that can be used and novel applications in the field. Additionally, unlike other sensor areas, there are some aspects not strictly related with the technology that could be envisaged such as: User acceptance, privacy, safety, standardization or the required qualification for the use of the sensor technologies.

This Special Issue invites contributions on the following topics (but is not limited to them):

  • Sensors in health monitoring
  • Sensors in rehabilitation
  • Indoor navigation aid
  • Real time tracking of disabled people
  • Assisted living
  • Home Medical Assistance
  • Privacy, safety or standardization issues

Prof. Dr. Francisco José García-Peñalvo
Dr. Manuel Franco-Martín
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 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 1800 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

  • Sensors
  • Disabled people
  • Assisted living
  • Assisting systems
  • Health monitoring
  • Wearable technologies
  • Indoor positioning
  • Human activity recognition
  • Vital sign monitoring
  • Personalized medicine

Published Papers (7 papers)

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Research

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Open AccessArticle System for Face Recognition under Different Facial Expressions Using a New Associative Hybrid Model Amαβ-KNN for People with Visual Impairment or Prosopagnosia
Sensors 2019, 19(3), 578; https://doi.org/10.3390/s19030578
Received: 16 November 2018 / Revised: 30 December 2018 / Accepted: 10 January 2019 / Published: 30 January 2019
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Abstract
Face recognition is a natural skill that a child performs from the first days of life; unfortunately, there are people with visual or neurological problems that prevent the individual from performing the process visually. This work describes a system that integrates Artificial Intelligence [...] Read more.
Face recognition is a natural skill that a child performs from the first days of life; unfortunately, there are people with visual or neurological problems that prevent the individual from performing the process visually. This work describes a system that integrates Artificial Intelligence which learns the face of the people with whom the user interacts daily. During the study we propose a new hybrid model of Alpha-Beta Associative memories (Amαβ) with Correlation Matrix (CM) and K-Nearest Neighbors (KNN), where the Amαβ-CMKNN was trained with characteristic biometric vectors generated from images of faces from people who present different facial expressions such as happiness, surprise, anger and sadness. To test the performance of the hybrid model, two experiments that differ in the selection of parameters that characterize the face are conducted. The performance of the proposed model was tested in the databases CK+, CAS-PEAL-R1 and Face-MECS (own), which test the Amαβ-CMKNN with faces of subjects of both sexes, different races, facial expressions, poses and environmental conditions. The hybrid model was able to remember 100% of all the faces learned during their training, while in the test in which faces are presented that have variations with respect to those learned the results range from 95.05% in controlled environments and 86.48% in real environments using the proposed integrated system. Full article
(This article belongs to the Special Issue Sensor Technologies for Caring People with Disabilities)
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Open AccessArticle Hand Movement Classification Using Burg Reflection Coefficients
Sensors 2019, 19(3), 475; https://doi.org/10.3390/s19030475
Received: 26 October 2018 / Revised: 31 December 2018 / Accepted: 16 January 2019 / Published: 24 January 2019
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Abstract
Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these [...] Read more.
Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these signals relies on the accuracy of the proposed algorithm and the possibility of its implementation in hardware. This paper considers the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with the focus lying on the signal model and time domain characteristics for better classification accuracy. The proposal considers a simple preprocessing technique that produces signals suitable for feature extraction and the Burg reflection coefficients to form learning and classification patterns. These coefficients yield a competitive classification rate compared to the time domain features used. Sometimes, the feature extraction from electromyographic signals has shown that the procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as few traits as possible. The algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification. Full article
(This article belongs to the Special Issue Sensor Technologies for Caring People with Disabilities)
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Open AccessArticle HUSP: A Smart Haptic Probe for Reliable Training in Musculoskeletal Evaluation Using Motion Sensors
Sensors 2019, 19(1), 101; https://doi.org/10.3390/s19010101
Received: 23 November 2018 / Revised: 21 December 2018 / Accepted: 23 December 2018 / Published: 29 December 2018
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Abstract
As a consequence of the huge development of IMU (Inertial Measurement Unit) sensors based on MEMS (Micro-Electromechanical Systems), innovative applications related to the analysis of human motion are now possible. In this paper, we present one of these applications: a portable platform for [...] Read more.
As a consequence of the huge development of IMU (Inertial Measurement Unit) sensors based on MEMS (Micro-Electromechanical Systems), innovative applications related to the analysis of human motion are now possible. In this paper, we present one of these applications: a portable platform for training in Ultrasound Imaging-based musculoskeletal (MSK) exploration in rehabilitation settings. Ultrasound Imaging (USI) in the diagnostic and treatment of MSK pathologies offers various advantages, but it is a strongly operator-dependent technique, so training and experience become of fundamental relevance for rehabilitation specialists. The key element of our platform is a replica of a real transducer (HUSP—Haptic US Probe), equipped with MEMS based IMU sensors, an embedded computing board to calculate its 3D orientation and a mouse board to obtain its relative position in the 2D plane. The sensor fusion algorithm used to resolve in real-time the 3D orientation (roll, pitch and yaw angles) of the probe from accelerometer, gyroscope and magnetometer data will be presented. Thanks to the results obtained, the integration of the probe into the learning platform allows a haptic sensation to be recreated in the rehabilitation trainee, with an attractive performance/cost ratio. Full article
(This article belongs to the Special Issue Sensor Technologies for Caring People with Disabilities)
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Open AccessArticle Web Page Design Recommendations for People with Down Syndrome Based on Users’ Experiences
Sensors 2018, 18(11), 4047; https://doi.org/10.3390/s18114047
Received: 21 October 2018 / Revised: 14 November 2018 / Accepted: 15 November 2018 / Published: 20 November 2018
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Abstract
At present, there is a high number of people with Down syndrome interested and trained to be an active part of society. According to the data extracted by our surveys we know that only 6% of the population with Down syndrome feels isolated [...] Read more.
At present, there is a high number of people with Down syndrome interested and trained to be an active part of society. According to the data extracted by our surveys we know that only 6% of the population with Down syndrome feels isolated in daily activities. However, when the activity requires the use of a computer, the percentage of people who feel isolated increases to 18%. This means that there are obvious website accessibility barriers that make it difficult for users with Down syndrome. To solve this problem, it is considered necessary to make an exhaustive study about Down syndrome. We know that the trisomy of chromosome 21 causes a series of symptoms that directly affect ones Internet browsing capabilities. For example, speech disturbances make communication and speed difficult. This guide is based on a neurological study of Down syndrome. Alterations in listening make understanding audio, retention of audio concepts and speed difficult. The alterations in the physiognomy of movement make it difficult for them to act quickly. Many of these alterations are caused by cognitive disability. After assessing the needs, the benefits of Web Content Accessibility Guidelines 2.0 (WCAG 2.0), and the existing usability guidelines are analyzed and those that may be useful for this profile are extracted. User tests are carried out through two websites developed specifically for this study with the aim of demonstrating the level of effectiveness of each of the planned guidelines. Considering the neurological characteristics of this intellectual disability, research is developed that seeks to extract a list of useful accessibility and usability guidelines for web developers. Full article
(This article belongs to the Special Issue Sensor Technologies for Caring People with Disabilities)
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Open AccessArticle Support for Employees with ASD in the Workplace Using a Bluetooth Skin Resistance Sensor–A Preliminary Study
Sensors 2018, 18(10), 3530; https://doi.org/10.3390/s18103530
Received: 7 September 2018 / Revised: 17 October 2018 / Accepted: 17 October 2018 / Published: 19 October 2018
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Abstract
The application of a Bluetooth skin resistance sensor in assisting people with Autism Spectrum Disorders (ASD), in their day-to-day work, is presented in this paper. The design and construction of the device are discussed. The authors have considered the best placement of the [...] Read more.
The application of a Bluetooth skin resistance sensor in assisting people with Autism Spectrum Disorders (ASD), in their day-to-day work, is presented in this paper. The design and construction of the device are discussed. The authors have considered the best placement of the sensor, on the body, to gain the most accurate readings of user stress levels, under various conditions. Trial tests were performed on a group of sixteen people to verify the correct functioning of the device. Resistance levels were compared to those from the reference system. The placement of the sensor has also been determined, based on wearer convenience. With the Bluetooth Low Energy block, users can be notified immediately about their abnormal stress levels via a smartphone application. This can help people with ASD, and those who work with them, to facilitate stress control and make necessary adjustments to their work environment. Full article
(This article belongs to the Special Issue Sensor Technologies for Caring People with Disabilities)
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Open AccessArticle Visual Localizer: Outdoor Localization Based on ConvNet Descriptor and Global Optimization for Visually Impaired Pedestrians
Sensors 2018, 18(8), 2476; https://doi.org/10.3390/s18082476
Received: 13 June 2018 / Revised: 25 July 2018 / Accepted: 26 July 2018 / Published: 31 July 2018
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Abstract
Localization systems play an important role in assisted navigation. Precise localization renders visually impaired people aware of ambient environments and prevents them from coming across potential hazards. The majority of visual localization algorithms, which are applied to autonomous vehicles, are not adaptable completely [...] Read more.
Localization systems play an important role in assisted navigation. Precise localization renders visually impaired people aware of ambient environments and prevents them from coming across potential hazards. The majority of visual localization algorithms, which are applied to autonomous vehicles, are not adaptable completely to the scenarios of assisted navigation. Those vehicle-based approaches are vulnerable to viewpoint, appearance and route changes (between database and query images) caused by wearable cameras of assistive devices. Facing these practical challenges, we propose Visual Localizer, which is composed of ConvNet descriptor and global optimization, to achieve robust visual localization for assisted navigation. The performance of five prevailing ConvNets are comprehensively compared, and GoogLeNet is found to feature the best performance on environmental invariance. By concatenating two compressed convolutional layers of GoogLeNet, we use only thousands of bytes to represent image efficiently. To further improve the robustness of image matching, we utilize the network flow model as a global optimization of image matching. The extensive experiments using images captured by visually impaired volunteers illustrate that the system performs well in the context of assisted navigation. Full article
(This article belongs to the Special Issue Sensor Technologies for Caring People with Disabilities)
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Review

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Open AccessReview Technological Ecosystems in Care and Assistance: A Systematic Literature Review
Sensors 2019, 19(3), 708; https://doi.org/10.3390/s19030708
Received: 21 January 2019 / Revised: 4 February 2019 / Accepted: 6 February 2019 / Published: 9 February 2019
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Abstract
Applying the concepts of technological ecosystems to the care and assistance domain is an emerging field that has gained interest during the last years, as they allow to describe the complex relationships between actors in a technologically boosted care domain. In that context, [...] Read more.
Applying the concepts of technological ecosystems to the care and assistance domain is an emerging field that has gained interest during the last years, as they allow to describe the complex relationships between actors in a technologically boosted care domain. In that context, this paper presents a systematic review and mapping of the literature to identify, analyse and classify the published research carried out to provide care and assistance services under a technological ecosystems’ perspective. Thirty-seven papers were identified in the literature as relevant and analysed in detail (between 2003–2018). The main findings show that it is indeed an emerging field, as few of the found ecosystem proposals have been developed in the real world nor have they been tested with real users. In addition, a lot of research to date reports the proposal of platform-centric architectures developed over existing platforms not specifically developed for care and services provision. Employed sensor technologies for providing services have very diverse natures depending on the intended services to be provided. However, many of these technologies do not take into account medical standards. The degree of the ecosystems’ openness to adding new devices greatly depends on the approach followed, such as the type of middleware considered. Thus, there is still much work to be done in order to equate other more established ecosystems such as business or software ecosystems. Full article
(This article belongs to the Special Issue Sensor Technologies for Caring People with Disabilities)
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