Development of an ontology for the inclusion of app users with visual impairments

Approximately 15% of the world’s population have some form of disability and the majority use apps on their mobile devices to help them in their daily lives with communication, healthcare, or for entertainment purposes. It is not, however, easy for users with impairments to choose the most suitable apps since this will depend on their particular personal characteristics or circumstances in a specific context, and because such users require apps with certain accessibility features which are not always specified in the app description. In order to overcome such difficulties, it is necessary to obtain a user profile that gathers the user’s personal details, abilities, disabilities, skills, and interests to facilitate selection. The basis for our research work is to develop an app that recommends a set of apps to users with disabilities. In this respect, the focus of this paper is to obtain a semantic user profile model on which more precise search requests can be performed. The disability we have chosen to concentrate on is that of visual impairment. We propose an ontology-based user profile that matches users’ characteristics, disabilities, and interests, and which not only simplifies the classification process but also provides a mechanism for linking them with existing disability ontologies, assistive devices, accessibility concepts, etc. Moreover, thanks to the inclusion of semantic relations and rules, it is possible to reason and infer new information that can be used to make more personalized recommendations than a simple app store search.Spanish Ministry of Economy and Competitiveness (Agencia Estatal de Investigacion) PID2019-109644RB-I00/AEI/10.13039/50110001103

Accessibility and Activity-Centered Design for ICT Users: ACCESIBILITIC Ontology

Information and communication technologies (ICTs) are involved in daily human activities. Accessibility guarantees that individuals with different abilities can interact with ICTs. User pro le models are an explicit representation of the characteristics of an individual and are used to reason about what users need. They are implemented through ontologies. After identifying common and different aspects among important ontologies in the domain of accessibility and e-inclusion, we designed and implemented the ACCESIBILITIC ontology applying the NeOn methodology, speci cally by reusing and reengineering these ontologies. The strengths of our model include the user's ability to develop a high variety of activities despite his/her disabilities, support for inference processes, and providing answers to several competency questions. ACCESIBILITIC allows the representation of suitable technical support based on the user's capabilities when interacting with ICTs. To this end, we use an activity-centered design (ACD), which allows us to identify daily activities and to match these activities with a suitable technology to perform them.This research work is funded by the Spanish Ministry of Economy and Competitiveness - Agencia Estatal de Investigación - with European Regional Development Funds (AEI/FEDER, UE) through the project ref. TIN2016-79484-R

A Microservices e-Health System for Ecological Frailty Assessment Using Wearables

The population in developed countries is aging and this fact results in high elderly health costs, as well as a decrease in the number of active working members to support these costs. This could lead to a collapse of the current systems. One of the first insights of the decline in elderly people is frailty, which could be decelerated if it is detected at an early stage. Nowadays, health professionals measure frailty manually through questionnaires and tests of strength or gait focused on the physical dimension. Sensors are increasingly used to measure and monitor different e-health indicators while the user is performing Basic Activities of Daily Life (BADL). In this paper, we present a system based on microservices architecture, which collects sensory data while the older adults perform Instrumental ADLs (IADLs) in combination with BADLs. IADLs involve physical dimension, but also cognitive and social dimensions. With the sensory data we built a machine learning model to assess frailty status which outperforms the previous works that only used BADLs. Our model is accurate, ecological, non-intrusive, flexible and can help health professionals to automatically detect frailty.Ministry of Economy and Competitiveness from Spain MINECO/FEDER MAT2017-85999PEuropean Union (EU) MINECO/FEDER MAT2017-85999PRegional Government of Andalusia Research Fund from Spain A-BIO-157-UGR-1

Plataforma móvil de apoyo al aprendizaje en educación especial

Se presenta una plataforma para diseñar actividades para alumnos con necesidades educativas especiales, que se ejecutan en dispositivos móviles iPhone y iPod touch. Las principales aportaciones que ofrece son que permite adaptar la interfaz de usuario y el contexto educativo a las necesidades y capacidades del alumno, ofreciendo una enseñanza individualizada y dando soporte a la realización de actividades en grup

Reducing Response Time in Motor Imagery Using A Headband and Deep Learning

Electroencephalography (EEG) signals to detect motor imagery have been used to help patients with low mobility. However, the regular brain computer interfaces (BCI) capturing the EEG signals usually require intrusive devices and cables linked to machines. Recently, some commercial low-intrusive BCI headbands have appeared, but with less electrodes than the regular BCIs. Some works have proved the ability of the headbands to detect basic motor imagery. However, all of these works have focused on the accuracy of the detection, using session sizes larger than 10 s, in order to improve the accuracy. These session sizes prevent actuators using the headbands to interact with the user within an adequate response time. In this work, we explore the reduction of time-response in a low-intrusive device with only 4 electrodes using deep learning to detect right/left hand motion imagery. The obtained model is able to lower the detection time while maintaining an acceptable accuracy in the detection. Our findings report an accuracy above 83.8% for response time of 2 s overcoming the related works with both low- and high-intrusive devices. Hence, our low-intrusive and low-cost solution could be used in an interactive system with a reduced response time of 2 s.Spanish Ministry of Economy and Competitiveness (Agencia Estatal de Investigacion-AEI) TIN2016-79484-REuropean Union (EU) TIN2016-79484-RSpanish Government PID2019-109644RB-I00/AEI/10.13039/501100011033 FPU18/0028

A machine learning approach for semi-automatic assessment of IADL dependence in older adults with wearable sensors

The research conducted in this publication was funded by the Spanish Ministry of Economy and Competitiveness (Agencia Estatal de Investigacion) under grant number: PID2019-109644RB-I00/AEI/10.13039/501100011033. Funding for open access charge: Uni-versidad de Granada/CBUA.Background and Objective: The assessment of dependence in older adults currently requires a manual collection of data taken from questionnaires. This process is time consuming for the clinicians and intrudes the daily life of the elderly. This paper aims to semi-automate the acquisition and analysis of health data to assess and predict the dependence in older adults while executing one instrumental activity of daily living (IADL). Methods: In a mobile-health (m-health) scenario, we analyze whether the acquisition of data through wearables during the performance of IADLs, and with the help of machine learning techniques could replace the traditional questionnaires to evaluate dependence. To that end, we collected data from wearables, while older adults do the shopping activity. A trial supervisor (TS) labelled the different shopping stages (SS) in the collected data. We performed data pre-processing techniques over those SS and analyzed them with three machine learning algorithms: k-Nearest Neighbors (k-NN), Random Forest (RF) and Support Vector Machines (SVM). Results: Our results confirm that it is possible to replace the traditional questionnaires with wearable data. In particular, the best learning algorithm we tried reported an accuracy of 97% in the assessment of dependence. We tuned the hyperparameters of this algorithm and used embedded feature selection technique to get the best performance with a subset of only 10 features out of the initial 85. This model considers only features extracted from four sensors of a single wearable: accelerometer, heart rate, electrodermal activity and temperature. Although these features are not observational, our current proposal is semi-automatic, because it needs a TS labelling the SS (with a smartphone application). In the future, this labelling process could be automatic as well. Conclusions: Our method can semi-automatically assess the dependence, without disturbing daily activities of elderly people. This method can save clinicians’ time in the evaluation of dependence in older adults and reduce healthcare costs.Spanish Ministry of Economy and Competitiveness (Agencia Estatal de Investigacion) PID2019-109644RB-I00/AEI/10.13039/50110001103