BookSense an Application for Mental Disorders Diagnosis: A Case Study for User Evaluation and Redesign

Booksense, a mobile application that allows to identify mental disorders such as depression, work stress and postraumatism [13], through a series of questions based on a mental health assessment that allows you to nd out if you have a mental illness, the app can detect if the user shows signs of a mental disorder, being the most important to detect the problem from its stages initials, plus it also has a database of institutions in the country where you can receive care. The World Health Organization (WHO) estimates that there are currently 300 million people on the planet who su er from depression. This is why it is important to have assisted diagnostic tools that help prevent this type of a ectations in the population, as well as keep informed. the people about help centers. All this would not be possible if you do not count an application that has three important aspects that are: E ciency, e ectiveness and satisfaction aspects that are not present in this diagnostic tool is why the importance of the use of usability evaluations. This research aims to generate a redesign of this application based on certain heuristics that ll the gaps in usabilityBooksense, una aplicación móvil que permite identificar trastornos mentales como depresión, estrés laboral y postraumatismo [13], a través de una serie de preguntas basadas en una evaluación de salud mental que te permite saber si tienes una enfermedad mental, la aplicación puede detectar si el usuario muestra signos de un trastorno mental, siendo lo más importante para detectar el problema desde sus etapas iniciales, además también cuenta con una base de datos de instituciones en el país donde puede recibir atención. La Organización Mundial de la Salud (OMS) estima que actualmente hay 300 millones de personas en el planeta que padecen depresión. Por eso es importante contar con herramientas de diagnóstico asistido que ayuden a prevenir este tipo de afectaciones en la población, así como a mantenerse informada. la gente sobre los centros de ayuda. Todo esto no sería posible si no se cuenta una aplicación que tiene tres aspectos importantes que son: Aspectos de eficiencia, efectividad y satisfacción que no están presentes en esta herramienta de diagnóstico de ahí la importancia del uso de evaluaciones de usabilidad. Esta investigación tiene como objetivo generar un rediseño de esta aplicación en base a ciertas heurísticas que llenen los vacíos de usabilida

BookSense an Application for Mental Disorders Diagnosis: A Case Study for User Evaluation and Redesign

Booksense, a mobile application that allows to identify mental disorders such as depression, work stress and postraumatism [13], through a series of questions based on a mental health assessment that allows you to nd out if you have a mental illness, the app can detect if the user shows signs of a mental disorder, being the most important to detect the problem from its stages initials, plus it also has a database of institutions in the country where you can receive care. The World Health Organization (WHO) estimates that there are currently 300 million people on the planet who su er from depression. This is why it is important to have assisted diagnostic tools that help prevent this type of a ectations in the population, as well as keep informed. the people about help centers. All this would not be possible if you do not count an application that has three important aspects that are: E ciency, e ectiveness and satisfaction aspects that are not present in this diagnostic tool is why the importance of the use of usability evaluations. This research aims to generate a redesign of this application based on certain heuristics that ll the gaps in usabilityBooksense, una aplicación móvil que permite identificar trastornos mentales como depresión, estrés laboral y postraumatismo [13], a través de una serie de preguntas basadas en una evaluación de salud mental que te permite saber si tienes una enfermedad mental, la aplicación puede detectar si el usuario muestra signos de un trastorno mental, siendo lo más importante para detectar el problema desde sus etapas iniciales, además también cuenta con una base de datos de instituciones en el país donde puede recibir atención. La Organización Mundial de la Salud (OMS) estima que actualmente hay 300 millones de personas en el planeta que padecen depresión. Por eso es importante contar con herramientas de diagnóstico asistido que ayuden a prevenir este tipo de afectaciones en la población, así como a mantenerse informada. la gente sobre los centros de ayuda. Todo esto no sería posible si no se cuenta una aplicación que tiene tres aspectos importantes que son: Aspectos de eficiencia, efectividad y satisfacción que no están presentes en esta herramienta de diagnóstico de ahí la importancia del uso de evaluaciones de usabilidad. Esta investigación tiene como objetivo generar un rediseño de esta aplicación en base a ciertas heurísticas que llenen los vacíos de usabilida

A Methodological Process for the Design of Frameworks Oriented to Infotainment User Interfaces

The objective of this paper was to propose a methodological process for the design of frameworks oriented to infotainment user interfaces. Four stages comprise the proposed process, conceptualization, structuring, documentation, and evaluation; in addition, these stages include activities, tasks, and deliverables to guide a work team during the design of a framework. To determine the stages and their components, an analysis of 42 papers was carried out through a systematic literature review in search of similarities during the design process of frameworks related to user interfaces. The evaluation method by a panel of experts was used to determine the validity of the proposal; the conceptual proposal was provided to a panel of 10 experts for their analysis and later a questionnaire in the form of a Likert scale was used to collect the information on the validation of the proposal. The results of the evaluation indicated that the methodological process is valid to meet the objective of designing a framework oriented to infotainment user interfaces

A Methodological Process for the Design of Frameworks Oriented to Infotainment User Interfaces

The objective of this paper was to propose a methodological process for the design of frameworks oriented to infotainment user interfaces. Four stages comprise the proposed process, conceptualization, structuring, documentation, and evaluation; in addition, these stages include activities, tasks, and deliverables to guide a work team during the design of a framework. To determine the stages and their components, an analysis of 42 papers was carried out through a systematic literature review in search of similarities during the design process of frameworks related to user interfaces. The evaluation method by a panel of experts was used to determine the validity of the proposal; the conceptual proposal was provided to a panel of 10 experts for their analysis and later a questionnaire in the form of a Likert scale was used to collect the information on the validation of the proposal. The results of the evaluation indicated that the methodological process is valid to meet the objective of designing a framework oriented to infotainment user interfaces

Desarrollo de Prototipo de Aplicación Móvil para Smart Tourism basado en Diseño Centrado en el Usuario

En este artículo, se presenta la implementación de la norma ISO 9241-210:2010 (Human Centred Design for Interactive Systems) para el desarrollo de una aplicación móvil con el fin de fortalecer la experiencia del usuario al momento de utilizar la aplicación móvil in situ. Siguiendo las fases que la norma dicta para el desarrollo y evaluación de software y hardware con el propósito de obtener un prototipo funcional, y al término del proceso un producto. La implementación de la norma permitió generar un prototipo inicial validado por usuarios reales(turistas), por lo que, para un trabajo futuro se llevará a cabo el uso de técnicas de inteligencia artificial (AI) y análisis de datos, estas mismas, complementarán este trabajo, dando como resultado una aplicación para Smart Tourism completamente validada y funcional. Cabe destacar que el propósito es usar el Diseño Centrado en el Usuario (DCU), logrando así un prototipo de alta fidelidad

Persistence of COVID-19 Symptoms after Recovery in Mexican Population

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the coronavirus disease (COVID-19), a highly contagious infectious disease that has caused many deaths worldwide. Despite global efforts, it continues to cause great losses, and leaving multiple unknowns that we must resolve in order to face the pandemic more effectively. One of the questions that has arisen recently is what happens, after recovering from COVID-19. For this reason, the objective of this study is to identify the risk of presenting persistent symptoms in recovered from COVID-19. This case-control study was conducted in one state of Mexico. Initially the data were obtained from the participants, through a questionnaire about symptoms that they had at the moment of the interview. Initially were captured the collected data, to make a dataset. After the pre-processed using the R project tool to eliminate outliers or missing data. Obtained finally a total of 219 participants, 141 recovered and 78 controls. It was used confidence level of 90% and a margin of error of 7%. From results it was obtained that all symptoms have an associated risk in those recovered. The relative risk of the selected symptoms in the recovered patients goes from 3 to 22 times, being infinite for the case of dyspnea, due to the fact that there is no control that presents this symptom at the moment of the interview, followed by the nausea and the anosmia with a RR of 8.5. Therefore, public health strategies must be rethought, to treat or rehabilitate, avoiding chronic problems in patients recovered from COVID-19

Facial Expression Recognition from Multi-Perspective Visual Inputs and Soft Voting

Automatic identification of human facial expressions has many potential applications in today’s connected world, from mental health monitoring to feedback for onscreen content or shop windows and sign-language prosodic identification. In this work we use visual information as input, namely, a dataset of face points delivered by a Kinect device. The most recent work on facial expression recognition uses Machine Learning techniques, to use a modular data-driven path of development instead of using human-invented ad hoc rules. In this paper, we present a Machine-Learning based method for automatic facial expression recognition that leverages information fusion architecture techniques from our previous work and soft voting. Our approach shows an average prediction performance clearly above the best state-of-the-art results for the dataset considered. These results provide further evidence of the usefulness of information fusion architectures rather than adopting the default ML approach of features aggregation

Comparative study of convolutional neural network architectures for gastrointestinal lesions classification

The gastrointestinal (GI) tract can be affected by different diseases or lesions such as esophagitis, ulcers, hemorrhoids, and polyps, among others. Some of them can be precursors of cancer such as polyps. Endoscopy is the standard procedure for the detection of these lesions. The main drawback of this procedure is that the diagnosis depends on the expertise of the doctor. This means that some important findings may be missed. In recent years, this problem has been addressed by deep learning (DL) techniques. Endoscopic studies use digital images. The most widely used DL technique for image processing is the convolutional neural network (CNN) due to its high accuracy for modeling complex phenomena. There are different CNNs that are characterized by their architecture. In this article, four architectures are compared: AlexNet, DenseNet-201, Inception-v3, and ResNet-101. To determine which architecture best classifies GI tract lesions, a set of metrics; accuracy, precision, sensitivity, specificity, F1-score, and area under the curve (AUC) were used. These architectures were trained and tested on the HyperKvasir dataset. From this dataset, a total of 6,792 images corresponding to 10 findings were used. A transfer learning approach and a data augmentation technique were applied. The best performing architecture was DenseNet-201, whose results were: 97.11% of accuracy, 96.3% sensitivity, 99.67% specificity, and 95% AUC