Los sistemas inmersivos en los últimos años han tomado gran relevancia en la vida de los jóvenes. Estos incluyen tecnologías como la realidad aumentada (RA), la cual ha demostrado ser una herramienta poderosa para mejorar la experiencia de entretenimiento, interacción social y aprendizaje. Aunque para poderla usar correctamente se deben de considerar los posibles desafíos y riesgos, ya que también se puede causar adicción con el uso excesivo. Este estudio tiene como objetivo proporcionar una evaluación preliminar de como los sistemas inmersivos pueden ayudar en la vida de los jóvenes, ya que ellos enfrentan muchos retos en su día a día como depresión, estrés y ansiedad, por lo cual por medio de los sistemas inmersivos se pretende promover la mejora de la calidad de vida de los jóvenes actuales, esto a través de terapias no farmacológicas que los ayuden a tener un mejor bienestar emocional lo que desencadenaría una mejor calidad de vida. Para lo anterior se aplicaron una serie de pruebas a usuarios en un ambiente inmersivo controlado, recogiendo los datos producidos para posteriormente ser analizados. Como parte de los principales hallazgos de este trabajo se puede mencionar que los usuarios participantes tuvieron una mejora significativa en su bienestar emocional después de la aplicación de la terapia inmersiva. Estos resultados nos permitirán continuar explorando estas estrategias enfocadas a la mejora del bienestar emocional, incluyendo entornos laborales, personas bajo altos niveles de estrés, así como en población vulnerable (incluyendo adultos mayores).

The past decade has seen a rapid development of Augmented Reality (AR) systems and applications in many research areas including education, medicine and psychological treatments. This has been due to the growing evidence of the potential of AR systems on supporting learning tasks among other benefits. However, very little is known about the potential impact of using AR systems for influencing the emotional state of users in the treatment of cognitive impairment. In this paper, we build upon our previous work on developing a tool based on augmented reality to stimulate the emotions of patients suffering with dementia through audiovisual content (image, video and audio) [1]. Here, we focus on exploring the effects of stimulation of emotions in a controlled experiment where users of the AR system were presented with different types of multimedia. The data was collected in different sessions and analysed using a 2-factor ANOVA test to compare and determine the differences/changes in users’ emotions. Our findings suggest that there is a significant relationship between the emotions that the user feels before and after having contact with audiovisual stimuli, thus, showing the potential for being used as efficient tools for improving the emotional state of cognitive impairment sufferers.

Accordingly to the National Population Council (CONAPO) in Mexico, people over 60 years old are equivalent to 10% of the population [1]. This age is considered the beginning of the older adult stage, and by 2050 there will be an inevitable generational transition where older adults will represent 21.5% of the Mexican population. One of the most common diseases of this sector is cognitive impairment or dementia, being Alzheimer's Disease (AD) the most common with 60% of cases. AD causes depression and negative emotional states that impact the physical, mental and social health of adults and their families. Advances in artificial intelligence have proven to approach and understand human behavior and their emotional state under different circumstances. In this paper, we propose an Augmented Reality based strategy for measuring different emotional states of older adults. By interacting with multimedia content (audios, photos, and videos) we are laying the foundations for the creation of an intelligent platform that detects the negative emotional state and issues multimedia recommendations based on tastes customized to move the patient to a positive emotional state.

This paper presents a work in progress concerning the use of Creative Science methodology as a strategy for teaching English language to Spanish-speaking computer science students at the Instituto Tecnológico de León, Mexico. While this paper focuses on Spanish-speaking students the methods are generic and transferable to other languages. One of the challenges is that students must achieve an English level of B1 according to the common European framework, so it is of vital importance for students to achieve this score as it is a qualification requirement. In this paper, the use of the Creative Science methodology together with traditional English language teaching techniques is proposed, where students must propose creative solutions as well as build engineering prototypes as part of the learning process.

As the proportion of older adults grows, the number of special care provisions to help individuals with declining cognitive abilities needs also to increase. Information Communication Technology (ICT) is beginning to play an increasing role in facilitating the work and research of specialists to support and monitor individuals with cognitive impairment within their everyday environments. In addition, advances in artificial intelligence and the development of new algorithmic approaches can be used to approximate the computational processes of human behaviour in different circumstances. In this paper, we report on the development of a software system using game based therapies for older adults in Mexico suffering from cognitive impairment, where this system has been deployed in a unique day therapy centre. We further propose an evaluation module based on using an Artificial Neural Network (ANN) approach to monitor the user performance, a Fuzzy Logic based module to detect significant changes in performance that might indicate a possible cognitive decline, and a bio-signals sensor in order to gather information about the emotional state of the patient during the interaction. Keywords— Alzheimer, Artificial Neural Networks, Cognitive Impairment, Computer Assisted Therapy.

In this paper, we present a novel system for cognitive stimulation therapy to progressively assess cognitive impairment and emotional well-being of dementia patients in social care settings. The system assesses patients interactions and computes performance scores for different areas of cognitive stimulation. Patient interactions are initially classified into predefined performance categories through clustering of a sampled population. New personalized stimulation plans tailored to match the patient’s changing level of impairment are generated automatically through a set of fuzzy rule based systems using quantitative attributes and the overall scores of patients interactions. Therapists can redefine, evaluate and adjust the rules governing difficulty and activity levels for different stimulation areas to fine tune generated activity plans. The system can also be combined with an Internet of Things (IoT) enabled patient dialogue system for determining the affective state of participants during therapy sessions that could be used as a pervasive condition monitoring platform. Experiments consisting of therapy sessions of patients interacting with the system were performed in which the activity plans were automatically generated. Initial results showed that the system outputs were in agreement with the therapists own assessment in most of the stimulation areas. Simulation experiments were also conducted to analyse the system performance over multiple sessions. The results suggest that the system is able to adapt therapy plans overtime in response to changing levels of impairment/performance while supporting therapists to tune and evaluate therapy plans more effectively.

In this paper, we present a proposal for emotion recognition in a game-based therapy for patients with cognitive impairment. Cognitive impairment is a problem of global scope, where therapies have been sought to mitigate their progress, a way to do it is by using computer-assisted therapy because it performs a cognitive stimulation through software with exercises (designed by specialists) to be solved by the patient. Within the computer system, we seek to analyze specific characteristics to help psychologists to define a series of exercises appropriate to the patient using computational intelligence. Additionally, we aim to include other parameters, in particular, the emotional state of the user, as it has been found that they play an important role when interacting with the software. In this research, we are using novel technologies designed by IBM, such as the TJBot and IBM Watson platform. With the help of these tools, the proposal for the detection of user emotions in human-agent interactions is being considered and preliminary experiments were performed where good results were obtained.

The aim of the Course Timetabling problem is to ensure that all the students take their required classes and adhere to resources that are available in the school. The set of constraints those must be considered in the design of timetabling involves students, teachers, and classrooms. In the state of the art are different methodologies of design for Course Timetabling problem, in this paper we extend the proposal from Soria in 2013, in which they consider variables of students and classrooms, with four set of generic structures. This paper uses Soria's methodology to adding two more generic structures considering teacher restriction. We show an application of some different Metaheuristics using this methodology. Finally, we apply nonparametric test Wilcoxon signed-rank with the aim to find which metaheuristic algorithm shows a better performance in terms of quality.

In recent years, the search of efficient and non-invasive methods for the diagnosis of diseases has grown among the scientific community. One of the explored areas for that purpose is the analysis of the pupillary response to light stimulus, obtaining results in areas such Diabetes, Alzheimer, Neurological Disorders, Melancholia and other different physiological states. However, each of those investigations are focused on a concrete pathology or case of study and the data is analyzed in different ways, according the pathology that is searched. For that reason, we proposed a first scope to, through clustering techniques, find a model to be able to detect different groups of individuals with characteristics in common using the pupillary response time. The experiment was done over data coming from 32 healthy individuals and a characterization of their pupillary response, then a clustering algorithm was employed to group the data and finally an analysis of the clusters was performed. The result gives four clusters of which two of them have a high percentage of individuals with overweight. Thus it concludes that is possible to identify groups of people with characteristics in common using the pupillary response time as principal indicator and as future work is proposed a deep study using individuals with specific diagnosed pathologies and healthy individuals.

Alzheimer's disease International estimates that more than 35 million people worldwide are living with Alzheimer's disease or a related dementia. Patients with the disease at an early stage need regular checkups to monitor their care needs or problems that may arise, such as the development of confusion and loss of short term memory among others. Most of the time the person with the dementia has support provided by family or experts, in some cases not always need to be aware of the person, it depends on the degree of dementia suffered by the person but in all cases when the dementia occurs immediate attention is required. The objective of this paper is to present a fuzzy logic model system that allows granting a balance between independence and safety in an intelligent environment for monitoring and carefulness of people with Alzheimer. This model reacts according to the data that already have and those receiving throughout the process. The parameters and information considered by the model include activities, time spent in an area, when the person enters on a state of wandering, repetitive actions (perseverance) and displacement in the environment.

In our current situation, intelligent energy management is essential, it is necessary not only change the way how it is delivered, but also how users use it. Small changes in our life daily, such as turning off lights or air conditioning when there are not people who use it, can result in substantial energy savings. In this document, we provide information about of a support system for energy conservation using sensors, actuators and agents. The wireless network system is an embedded interconnected. Agents, sensors for monitoring a room and actuators for gradually manipulate elements like windows, blinds, led lamps and air conditioning, besides the application of optimization techniques and fuzzy logic. The energy required to give comfort, comes from two main sources possible, artificial (by the company supply electricity) and natural (natural air flows, solar lighting, etc.). Our main goal is through bio-inspired optimization algorithms and fuzzy logic control provides an adequate lighting and temperature settings according to an activity that takes place in the room.

In recent years, the problem of cyclic instability has been investigated mainly using two approaches: analysing the topological properties of the system (finding loops or feedback) and bio-inspired optimization. One of the main disadvantages of analysing the topology of the system (i.e. The connectivity of the agents involved in the environment) is the computational cost (that could be increased if the environment includes nomadic agents). Optimization-based approaches have been proven to work very well, even in the case of nomadic agents. However, the optimisation approach has been deployed mainly using computer simulations. With the breakthrough of integrated circuits, allowing a wide variety of low cost microcontrollers, the possibility of implementing intelligent algorithms (such as fuzzy logic, neural networks, etc.) on embedded agents is a reality. In this paper, we present a preliminary analysis toward the implementation of bio-inspired optimisation algorithms on embedded systems. Our long-term goal is to be able to prevent cyclic instability in real and complex rule based multi-agent environments using optimisation algorithms on embedded system.

The use of wireless sensor networks (WSN) in tracking applications is growing at a fast pace. In these applications, the sensor nodes discover, monitor and track an event or target object. A significant number of proposals relating the use of WSNs for target tracking have been published to date. However, they either focus on the tracking algorithm or on the communication protocol, and none of them address the problem integrally. In this paper, a comprehensive proposal for target detection and tracking is discussed. We introduce a tracking algorithm to detect and estimate a target location. Moreover, we introduce a low-overhead routing protocol to be used along with our tracking algorithm. The proposed algorithm has low computational complexity and has been designed considering the use of a mobile sink while generating minimal delay and packet loss. We also discuss the results of the evaluation of the proposed algorithms.

As the proportion of older adults grows, the number of special care provisions to help individuals with declining cognitive abilities needs to also increase. Information Communication Technology (ICT) is beginning to play an increasing role in facilitating the work of specialists to support and monitor individuals with cognitive impairment within their everyday environments. In addition, advances in artificial intelligence and the development of new algorithmic approaches can be used to approximate the computational processes of human behaviour in different circumstances. In this paper, we report on the development of a software system using game based therapies for older adults in Mexico suffering from cognitive impairment, where this system has been deployed in a unique day therapy centre. We further propose an evaluation module based on using AI approaches and affective sensing to monitor and detect significant changes in performance cognation that might indicate a possible cognitive decline.

The wireless sensor networks (WSNs) application scope has widened in recent years. For instance, the IEEE 802.15.4 technology allows establishing a mesh sensor network to support applications that increase the security of physical spaces, such as intruder detection, gas detection, and fire detection. In this paper, we focus on applications which require the support for mobility, including intruder detection and pursuit scenarios. Communications are based on the IEEE 802.15.4 standard due to its low power consumption, low latency and the ability to connect a large number of sensor nodes in a WSN. We propose the novel Mobile-sink Routing for Large Grids (MRLG) algorithm, which is intended to support sink mobility in a WSN. MRLG allows reducing the routing load by relying on local route recovery processes, which provides significant efficiency in scenarios with a large number of sensors. Experimental results show that, when compared to standard routing strategies based on sink announcements such as the Collection Tree Protocol (CTP), the performance of the MRLG algorithm is significantly boosted in terms of packet delivery ratio, end-to-end delay, and routing overhead.

Powered by digital transmission advances in recent years, wireless sensor networks (WSNs) are beginning to experience a boom in different areas. Of special interest are those applications offering real-time tracking and monitoring of objects in motion, both indoors and outdoors. A WSN can have a large number of nodes, each with multiple neighbor nodes. These nodes work together to create a self-configuring mesh that can efficiently achieve a common objective. In this paper, we focus on the accuracy of intruder tracking and monitoring, when relying on low-cost binary detection sensing mechanisms. To overcome the limitations imposed by this kind of sensors, we propose an intruder tracking algorithm to estimate the intruder location. In our study, we adopted the IEEE 802.15.4 standard for radio communications, and the MRLG (Mobile-sink Routing for Large Grids) protocol to route data to a mobile sink. Experimental results based on a grid sensor deployment show that the tracking error, measured as the mean euclidean distance between the estimated and the real intruder locations, is typically maintained below 10 meters, validating the applicability of the proposed solution.

The performance of wireless sensor networks (WSNs) at monitoring time-critical events is an important research topic, mainly due to the need to ensure that the actions to be taken upon these events are timely. To determine the effectiveness of the IEEE 802.15.4 standard at monitoring time-critical events in WSNs, we introduce a routing scheme based on drain announcements that seeks minimum routing overhead. We carried out a novel performance evaluation of the IEEE 802.15.4 technology under different conditions, to determine whether or not near-real-time event monitoring is feasible. By analyzing different simulation metrics such as packet loss rate, average end-to-end delay, and routing overhead, we determine the degree of effectiveness of the IEEE 802.15.4 standard at supporting time-critical tasks in multi-hop WSNs, evidencing its limitations upon the size and the amount of traffic flowing through the network.

Wireless Sensor Networks (WSNs) have proliferated significantly in recent years. Nowadays they are used in many fields, such as military, environmental and industrial. Reliability and low latency are desirable characteristics of many WSN applications. In particular, time-critical WSN applications must be able to act according to the observed changes in the environment as quickly as possible, assuring that the information collected by the sensor nodes is correct. In these applications the response time is a critical factor. In this paper, we focus on WSN monitoring applications for both indoor and outdoor environments. We propose a near real-time monitoring system based on binary detection sensor that offers delay bounded tracking of events, such as gas and fire. The performance of gas and fire tracking applications is evaluated using the IEEE 802.15.4 technology and a routing scheme for WSNs that relies on sink announcements for route discovery. The proposed routing protocol is tuned to introduce the lowest possible end-to-end delay to data packet delivery, by reducing control traffic to a minimum. To evaluate the performance, we develop both gas and fire propagation models for a framework that allows simulating emergency events, thus allowing us to determine the degree of accuracy achieved in the monitoring process.