Search

The study of electroencephalographic (EEG) signals has gained popularity in recent years because they are unlikely to intentionally fake brain activity. However, the reliability of the results is still subject to various noise sources and potential inaccuracies inherent to the acquisition process. Analyzing these signals involves three main processes: feature extraction, feature selection, and classification. The present study extensively evaluates feature sets across domains and their impact on emotion recognition. Feature selection improves results across the different domains. Additionally, hybrid models combining features from various domains offer a superior performance when applying the public DEAP dataset for emotion classification using EEG signals. Time, frequency, time–frequency, and spatial domain attributes and their combinations were analyzed. The effectiveness of the input vectors for the classifiers was validated using SVM, KNN, and ANN, which are simple classification algorithms selected for their widespread use and better performance in the state of the art. The use of simple machine learning algorithms makes the findings particularly valuable for real-time emotion recognition applications where the computational resources and processing time are often limited. After the analysis stage, feature vector combinations were proposed to identify emotions in four quadrants of the valence–arousal representation space using the DEAP dataset. This research achieved a classification accuracy of 96% using hybrid features in the four domains and the ANN classifier. A lower computational cost was obtained in the frequency domain.

There are millions of people worldwide using e-Learning platforms. However, people with disabilities still have problems using these platforms due to a lack of accessibility. In the present study, a model for the generation of the profiles of users with disabilities is proposed. This model enables e-Learning platforms to have information on users’ accessibility needs as input for automated adaptation of its interfaces. This would enable equal access for all learners with and without disabilities. In this context, different studies related to the profiling of users on e-Learning platforms were identified and analyzed. The model presented considers the Web Content Accessibility Guidelines (WCAG) that can be implemented in the interfaces of the e-Learning platforms and the metadata that represent the accessibility needs of users based on Schema.org. The researchers used Unified Modelling Language diagrams to design the model and define a description of the interaction between users, user interfaces, WCAG, Schema.org., and the eXtensible Markup Language (XML) profile generated. The testing of the prototype was carried out using WAVE and ARC Toolkit. The validation with the support of forty-four users was conducted using the System Usability Scale (SUS). The most outstanding results of this study are the identification of WCAG success criteria that are automatically implementable, the inclusion of two special categories for combined accessibility needs related to the elderly and linguistics, and the automatic generation of the profile as an XML file containing the metadata needed to enable the adaptation of e-Learning platform interfaces.

In the recent years, software applications for medical assistance, including the tele-rehabilitation, have known a high and continuous presence in the medical field. Guaranteeing the correct use of these applications induces to incorporate a study of usability in the life cycle of any interactive system. This paper synthesizes the results obtained from a study of effectiveness, efficiency, and subjective user satisfaction conducted with ePHoRt. ePHoRt is a web-based platform for home motor rehabilitation; 39 participants tested the platform. We report the empirical results based on the subjective usability perception and self-reported feedback based on the IBM Computer System Usability Questionnaire. The results suggest that ePHoRt is useful, effective, efficient, easy to use, and its interfaces are acceptable. Overall, the participants are satisfied to use the platform. The main findings of this paper show that the user guidance is a critical aspect to ensure a good usability of the tele-rehabilitation platform.

The goal of a telerehabilitation platform is to safely and securely facilitate the rehabilitation of patients through the use of telecommunication technologies complemented with the use of biomedical smart sensors. The purpose of this study was to perform a usability evaluation of a telerehabilitation platform. To improve the level of usability, the researchers developed and proposed an iterative process. The platform uses a digital representation of the patient which duplicates the therapeutic exercise being executed by the patient; this is detected by a Kinect camera and sensors in real time. This study used inspection methods to perform a usability evaluation of an exploratory prototype of a telerehabilitation platform. In addition, a cognitive workload assessment was performed to complement the usability evaluation. Users were involved through all the stages of the iterative refinement process. Usability issues were progressively reduced from the first iteration to the fourth iteration according to improvements which were developed and applied by the experts. Usability issues originally cataloged as catastrophic were reduced to zero, major usability problems were reduced to ten (2.75%) and minor usability problems were decreased to 141 (38.74%). This study also intends to serve as a guide to improve the usability of e-Health systems in alignment with the software development cycle.