A machine-learning scraping tool for data fusion in the analysis of sentiments about pandemics for supporting business decisions with human-centric AI explanations.

The COVID-19 pandemic is changing daily routines for many citizens with a high impact on the economy in some sectors. Small-medium enterprises of some sectors need to be aware of both the pandemic evolution and the corresponding sentiments of customers in order to figure out which are the best commercialization techniques. This article proposes an expert system based on the combination of machine learning and sentiment analysis in order to support business decisions with data fusion through web scraping. The system uses human-centric artificial intelligence for automatically generating explanations. The expert system feeds from online content from different sources using a scraping module. It allows users to interact with the expert system providing feedback, and the system uses this feedback to improve its recommendations with supervised learning.

Unsupervised mobile cognitive testing for use in preclinical Alzheimer's disease.

INTRODUCTION: Unsupervised digital cognitive testing is an appealing means to capture subtle cognitive decline in preclinical Alzheimer's disease (AD). Here, we describe development, feasibility, and validity of the Boston Remote Assessment for Neurocognitive Health (BRANCH) against in-person cognitive testing and amyloid/tau burden. METHODS: BRANCH is web-based, self-guided, and assesses memory processes vulnerable in AD. Clinically normal participants (n = 234; aged 50-89) completed BRANCH; a subset underwent in-person cognitive testing and positron emission tomography imaging. Mean accuracy across BRANCH tests (Categories, Face-Name-Occupation, Groceries, Signs) was calculated. RESULTS: BRANCH was feasible to complete on participants' own devices (primarily smartphones). Technical difficulties and invalid/unusable data were infrequent. BRANCH psychometric properties were sound, including good retest reliability. BRANCH was correlated with in-person cognitive testing (r = 0.617, P < .001). Lower BRANCH score was associated with greater amyloid (r = -0.205, P = .007) and entorhinal tau (r = -0.178, P = .026). DISCUSSION: BRANCH reliably captures meaningful cognitive information remotely, suggesting promise as a digital cognitive marker sensitive early in the AD trajectory.

Algorithm based on normal coordinate vectors with 16 segments for the data fusion from hand-written Arabic text implemented with MATLAB.

Hand-written text recognition is useful for interpreting records in different fields such as healthcare, surgery and police in which professionals may avoid technical equipment and prefer writing notes on paper. In order to perform data fusion from different data sources, handwriting automatic recognition involves barriers such as different ways of writing letters and deformation due to many reasons. This work presents a novel handwriting recognition approach based on the application of coordinate vectors to find similarities in different kinds of deformations. In particular, it has been implemented using 16 segments in order to distinguish all the particularities in matching the new text considering a dataset with a machine-learning approach. The implementation of this approach with MATLAB shows promising results with accuracy of 92.8% for with ensemble and bagged trees, after analyzing 22 possible combinations of machine learning and processing techniques.

Head-Mounted Display-Based Application for Cognitive Training.

Virtual Reality (VR) has had significant advances in rehabilitation, due to the gamification of cognitive activities that facilitate treatment. On the other hand, Immersive Virtual Reality (IVR) produces outstanding results due to the interactive features with the user. This work introduces a VR application for memory rehabilitation by walking through a maze and using the Oculus Go head-mounted display (HMD) technology. The mechanics of the game require memorizing geometric shapes while the player progresses in two modes, autonomous or manual, with two levels of difficulty depending on the number of elements to remember. The application is developed in the Unity 3D video game engine considering the optimization of computational resources to improve the performance in the processing and maintaining adequate benefits for the user, while the generated data is stored and sent to a remote server. The maze task was assessed with 29 subjects in a controlled environment. The obtained results show a significant correlation between participants' response accuracy in both the maze task and a face-pair test. Thus, the proposed task is able to perform memory assessments.

Memoria de trabajo visual para objetos relacionados semánticamente en adultos sanos / Visual working memory for semantically related objects in healthy adults

INTRODUCCIÓN: Pocos estudios han examinado cómo la manipulación de la relación semántica entre objetos puede influir en el desempeño o el tiempo de reacción en tareas de memoria de trabajo visual. OBJETIVO: Caracterizar cómo la relación semántica de los objetos afecta el desempeño o el tiempo de reacción en una tarea de memoria de trabajo en adultos sanos utilizando una tarea cognitiva diseñada para el uso con tableta. SUJETOS Y MÉTODOS. Se usó una tarea de emparejamiento demorado (delayed matching to sample) con una muestra de 76 participantes adultos de España y Colombia. La tarea incluyó 80 conjuntos únicos de cuatro o seis objetos relacionados/no relacionados semánticamente. Se registraron las respuestas y el tiempo de reacción de los participantes. RESULTADOS: Cuando los objetos estaban semánticamente relacionados, el tiempo de reacción fue mayor en la condición de seis objetos con respecto a la condición de cuatro objetos. La edad se asoció positivamente con el tiempo de reacción, pero no con la precisión de las respuestas. Los participantes con menos años de educación formal tuvieron un peor de¬sempeño y un tiempo de reacción más lento en las condiciones somáticamente relacionadas en relación con los participantes con 16 años o más de educación formal. CONCLUSIÓN: Los resultados sugieren que, cuando los objetos están semánticamente relacionados y aumenta su número, se necesita más tiempo de procesamiento para determinar si un objeto está o no en el grupo de objetos codificado. Además, un mayor nivel educativo se relaciona con respuestas más rápidas y un mejor desempeño en ciertas condiciones de la tarea

INTRODUCTION. Few studies have examined how manipulating the semantic relationship between objects impacts visual working memory accuracy or reaction time. AIM. To characterize how the semantic relatedness of visual objects impacts working memory accuracy and reaction time in healthy adults using a newly developed mobile-tablet cognitive task. SUBJECTS AND METHODS. A delayed matching to sample paradigm on the tablet task was studied in a sample of 76 community-dwelling adult participants from Spain and Colombia. The tablet task included 80 unique sets of either four or six semantically related or semantically unrelated objects. The accuracy and reaction time of the participants on the task were recorded for analysis. RESULTS. When objects were semantically related, reaction time was greater in the six object sets relative to the four object sets. Age was positively associated with reaction time, but not accuracy across all four task conditions. Participants with fewer years of formal education than the sample median (16 years) exhibited worse response accuracy and slower reaction time on both the four and six semantically related conditions relative to participants with 16 or more years of formal education. CONCLUSIONS. Findings from this study suggest that when objects are semantically related (versus unrelated) and object load is increased, more processing time is needed to determine whether an object was or was not in the encoded set. The results also suggest that greater educational attainment which likely relates with greater exposure to more technologies is related with faster and more accurate responses on some task conditions

Augmented Reality Interface for Complex Anatomy Learning in the Central Nervous System: A Systematic Review.

The medical system is facing the transformations with augmentation in the use of medical information systems, electronic records, smart, wearable devices, and handheld. The central nervous system function is to control the activities of the mind and the human body. Modern speedy development in medical and computational growth in the field of the central nervous system enables practitioners and researchers to extract and visualize insight from these systems. The function of augmented reality is to incorporate virtual and real objects, interactively running in a real-time and real environment. The role of augmented reality in the central nervous system becomes a thought-provoking task. Gesture interaction approach-based augmented reality in the central nervous system has enormous impending for reducing the care cost, quality refining of care, and waste and error reducing. To make this process smooth, it would be effective to present a comprehensive study report of the available state-of-the-art-work for enabling doctors and practitioners to easily use it in the decision making process. This comprehensive study will finally summarise the outputs of the published materials associate to gesture interaction-based augmented reality approach in the central nervous system. This research uses the protocol of systematic literature which systematically collects, analyses, and derives facts from the collected papers. The data collected range from the published materials for 10 years. 78 papers were selected and included papers based on the predefined inclusion, exclusion, and quality criteria. The study supports to identify the studies related to augmented reality in the nervous system, application of augmented reality in the nervous system, technique of augmented reality in the nervous system, and the gesture interaction approaches in the nervous system. The derivations from the studies show that there is certain amount of rise-up in yearly wise articles, and numerous studies exist, related to augmented reality and gestures interaction approaches to different systems of the human body, specifically to the nervous system. This research organises and summarises the existing associated work, which is in the form of published materials, and are related to augmented reality. This research will help the practitioners and researchers to sight most of the existing studies subjected to augmented reality-based gestures interaction approaches for the nervous system and then can eventually be followed as support in future for complex anatomy learning.

Close2U: An App for Monitoring Cancer Patients with Enriched Information from Interaction Patterns.

The management of cancer patients' symptoms in doctor consultations is a cornerstone in clinical care, this process being fundamental for the follow-up of the evolution of these. This article presents an application that allows collecting periodically and systematically the data of cancer patients and their visualization by the medical team. In this article, we made the analysis, design, implementation, and final evaluation by analyzing the correlation of this data collection with interaction patterns to determine how the user information can be enriched with information from the interaction patterns. We have followed an agile methodology based on the iterative and incremental development of successive prototypes with increased fidelity, where the requirements and solutions have evolved over time according to the need and assessments made. The comprehensive analysis of the patient's condition allowed us to perform a first analysis of the correlation of the states of patients concerning mood, sleeping quality, and pain with the interaction patterns. A future goal of this project is to optimize the process of data collection and the analysis of information. Another future goal is to reduce the time dedicated to reporting the evolution of symptoms in face-to-face consultations and to help professionals in analyzing the patient's evolution even in the period that has not been attended in person.

Quantum Diffie-Hellman Extended to Dynamic Quantum Group Key Agreement for e-Healthcare Multi-Agent Systems in Smart Cities.

Multi-Agent Systems can support e-Healthcare applications for improving quality of life of citizens. In this direction, we propose a healthcare system architecture named smart healthcare city. First, we divide a given city into various zones and then we propose a zonal level three-layered system architecture. Further, for effectiveness we introduce a Multi-Agent System (MAS) in this three-layered architecture. Protecting sensitive health information of citizens is a major security concern. Group key agreement (GKA) is the corner stone for securely sharing the healthcare data among the healthcare stakeholders of the city. For establishing GKA, many efficient cryptosystems are available in the classical field. However, they are yet dependent on the supposition that some computational problems are infeasible. In light of quantum mechanics, a new field emerges to share a secret key among two or more members. The unbreakable and highly secure features of key agreement based on fundamental laws of physics allow us to propose a Quantum GKA (QGKA) technique based on renowned Quantum Diffie-Hellman (QDH). In this, a node acts as a Group Controller (GC) and forms 2-party groups with remaining nodes, establishing a QDH-style shared key per each two-party. It then joins these keys into a single group key by means of a XOR-operation, acting as a usual group node. Furthermore, we extend the QGKA to Dynamic QGKA (DQGKA) by adding join and leave protocol. Our protocol performance was compared with existing QGKA protocols in terms of Qubit efficiency (QE), unitary operation (UO), unitary operation efficiency (UOE), key consistency check (KCC), security against participants attack (SAP) and satisfactory results were obtained. The security analysis of the proposed technique is based on unconditional security of QDH. Moreover, it is secured against internal and external attack. In this way, e-healthcare Multi-Agent System can be robust against future quantum-based attacks.

A methodology for the design and development of gamified mobile apps for monitoring cancer survivors.

An increasing number of cancer patients is treated and recover each year, and consequently there are survivors that require specialized and coordinated follow-up. The physical, social, working, psychological and emotional aspects of these survivors have to be characterized, investigated and treated by multidisciplinary and interdisciplinary teams. Nowadays, oncology community is focused on tracking records of interest in Patient-Reported Outcome (PROs) for patients of different cancer types. In the last years, several articles have proven that PROs are an effective method to improve the management of patient symptoms and, subsequently, clinical care. In this scenario, patient engagement is one of the most relevant aspects for PROs success. In this sense, one of the most promising strategies for increasing engagement is gamification, that is, the introduction of game elements in systems that are not games. Therefore, in this work we introduce a methodology for developing gamification apps for cancer survivors that aims at increasing engagement when collecting PROs data.

A Collocation Method for Numerical Solution of Nonlinear Delay Integro-Differential Equations for Wireless Sensor Network and Internet of Things.

Wireless sensor network and industrial internet of things have been a growing area of research which is exploited in various fields such as smart home, smart industries, smart transportation, and so on. There is a need of a mechanism which can easily tackle the problems of nonlinear delay integro-differential equations for large-scale applications of Internet of Things. In this paper, Haar wavelet collocation technique is developed for the solution of nonlinear delay integro-differential equations for wireless sensor network and industrial Internet of Things. The method is applied to nonlinear delay Volterra, delay Fredholm and delay Volterra-Fredholm integro-differential equations which are based on the use of Haar wavelets. Some examples are given to show the computational efficiency of the proposed technique. The approximate solutions are compared with the exact solution. The maximum absolute and mean square roots errors for distant number of collocation points are also calculated. The results show that Haar method is efficient for solving these equations for industrial Internet of Things. The results are compared with existing methods from the literature. The results exhibit that the method is simple, precise and efficient.

Smart Cupboard for Assessing Memory in Home Environment.

Sensor systems for the Internet of Things (IoT) make it possible to continuously monitor people, gathering information without any extra effort from them. Thus, the IoT can be very helpful in the context of early disease detection, which can improve peoples' quality of life by applying the right treatment and measures at an early stage. This paper presents a new use of IoT sensor systems-we present a novel three-door smart cupboard that can measure the memory of a user, aiming at detecting potential memory losses. The smart cupboard has three sensors connected to a Raspberry Pi, whose aim is to detect which doors are opened. Inside of the Raspberry Pi, a Python script detects the openings of the doors, and classifies the events between attempts of finding something without success and the events of actually finding it, in order to measure the user's memory concerning the objects' locations (among the three compartments of the smart cupboard). The smart cupboard was assessed with 23 different users in a controlled environment. This smart cupboard was powered by an external battery. The memory assessments of the smart cupboard were compared with a validated test of memory assessment about face-name associations and a self-reported test about self-perceived memory. We found a significant correlation between the smart cupboard results and both memory measurement methods. Thus, we conclude that the proposed novel smart cupboard successfully measured memory.

Green Communication for Tracking Heart Rate with Smartbands.

The trend of using wearables for healthcare is steeply increasing nowadays, and, consequently, in the market, there are several gadgets that measure several body features. In addition, the mixed use between smartphones and wearables has motivated research like the current one. The main goal of this work is to reduce the amount of times that a certain smartband (SB) measures the heart rate (HR) in order to save energy in communications without significantly reducing the utility of the application. This work has used an SB Sony 2 for measuring heart rate, Fit API for storing data and Android for managing data. The current approach has been assessed with data from HR sensors collected for more than three months. Once all HR measures were collected, then the current approach detected hourly ranges whose heart rate were higher than normal. The hourly ranges allowed for estimating the time periods of weeks that the user could be at potential risk for measuring frequently in these (60 times per hour) ranges. Out of these ranges, the measurement frequency was lower (six times per hour). If SB measures an unusual heart rate, the app warns the user so they are aware of the risk and can act accordingly. We analyzed two cases and we conclude that energy consumption was reduced in 83.57% in communications when using training of several weeks. In addition, a prediction per day was made using data of 20 users. On average, tests obtained 63.04% of accuracy in this experimentation using the training over the data of one day for each user.

ABS-SmartComAgri: An Agent-Based Simulator of Smart Communication Protocols in Wireless Sensor Networks for Debugging in Precision Agriculture.

Smart communication protocols are becoming a key mechanism for improving communication performance in networks such as wireless sensor networks. However, the literature lacks mechanisms for simulating smart communication protocols in precision agriculture for decreasing production costs. In this context, the current work presents an agent-based simulator of smart communication protocols for efficiently managing pesticides. The simulator considers the needs of electric power, crop health, percentage of alive bugs and pesticide consumption. The current approach is illustrated with three different communication protocols respectively called (a) broadcast, (b) neighbor and (c) low-cost neighbor. The low-cost neighbor protocol obtained a statistically-significant reduction in the need of electric power over the neighbor protocol, with a very large difference according to the common interpretations about the Cohen's d effect size. The presented simulator is called ABS-SmartComAgri and is freely distributed as open-source from a public research data repository. It ensures the reproducibility of experiments and allows other researchers to extend the current approach.

ABS-FishCount: An Agent-Based Simulator of Underwater Sensors for Measuring the Amount of Fish.

Underwater sensors provide one of the possibilities to explore oceans, seas, rivers, fish farms and dams, which all together cover most of our planet's area. Simulators can be helpful to test and discover some possible strategies before implementing these in real underwater sensors. This speeds up the development of research theories so that these can be implemented later. In this context, the current work presents an agent-based simulator for defining and testing strategies for measuring the amount of fish by means of underwater sensors. The current approach is illustrated with the definition and assessment of two strategies for measuring fish. One of these two corresponds to a simple control mechanism, while the other is an experimental strategy and includes an implicit coordination mechanism. The experimental strategy showed a statistically significant improvement over the control one in the reduction of errors with a large Cohen's d effect size of 2.55.

Development and initial evaluation of a mobile application to help with mindfulness training and practice.

BACKGROUND AND OBJECTIVE: Different review articles support the usefulness and effectiveness of mindfulness techniques in health and wellbeing. In this paper we present a first prototype of a mobile application to help with the training and practice of mindfulness, taking into account the lacks detected in a previous literature review. Our aim was to measure acceptance and perceived quality, as well as gather data about app usage. Their dependence on demographic variables and the change in mindful level was also measured. MATERIALS AND METHODS: Two versions of a new application were developed, "Mindfulness" and "Mindfulness Sci". The application has been tested in two pilot studies: in traditional face-to-face mindfulness groups and in individual and independent use. RESULTS: 3977 users were involved in this study: 26 in the first trial during an 8-week usage period and 3951 in the second trial during 17 months. In the first study, participants assessed the app with high scores. They considered it as a helping tool for mindfulness practice, user-friendly and with high quality of use. The positive perception was maintained after 8-weeks meditation workshops, and participants considered that its use could contribute to obtain benefits for mental and physical health. In the second study, we found rather weak associations between usage time and age, nationality and educational level. The mindful level showed a weak positive correlation with the session accomplished but slightly above the boundary of statistical significance (p-value=0.051). Videos and information stood out as the most accessed resources. CONCLUSIONS: Up to our knowledge, this is the first app developed with the help of health professionals in Spanish that could be used with a general aim, in health and wellbeing. The results are promising with a positive evaluation in face-to-face and independent use situations. Therefore, the number of potential users is enormous in a global worldwide context.

Combining novelty detectors to improve accelerometer-based fall detection.

Research on body-worn sensors has shown how they can be used for the detection of falls in the elderly, which is a relevant health problem. However, most systems are trained with simulated falls, which differ from those of the target population. In this paper, we tackle the problem of fall detection using a combination of novelty detectors. A novelty detector can be trained only with activities of daily life (ADL), which are true movements recorded in real life. In addition, they allow adapting the system to new users, by recording new movements and retraining the system. The combination of several detectors and features enhances performance. The proposed approach has been compared with a traditional supervised algorithm, a support vector machine, which is trained with both falls and ADL. The combination of novelty detectors shows better performance in a typical cross-validation test and in an experiment that mimics the effect of personalizing the classifiers. The results indicate that it is possible to build a reliable fall detector based only on ADL.

A Kinect-Based System for Lower Limb Rehabilitation in Parkinson's Disease Patients: a Pilot Study.

This work brings together the emerging virtual reality techniques and the natural user interfaces to offer new possibilities in the field of rehabilitation. We have designed a rehabilitation game based on a low cost device (Microsoft Kinect(TM)) connected to a personal computer. It provides patients having Parkinson's Disease (PD) with a motivating way to perform several motor rehabilitation exercises to improve their rehabilitation. The experiment was tested on seven Parkinson's Disease patients and results demonstrated significant improvements in completion time score and in the 10 Meters Walk Test score. Nevertheless, additional research is needed to determine if this type of training has a long-term impact. Both the device and protocol were well accepted by subjects, being safe and easy to use. We conclude that our work provides a simple and suitable tool resulting in a more enriching rehabilitation process where motivation is highly encouraged in PD patients. Feedback coming from participants corroborate the hypothesis that the system can be applied not only in clinical rehabilitation centers but at home.