I propose and discuss some principles that I believe are substantial for percep- tion, various kinds of memory, expectations and the capacity for imagination in the mammal brain, as well as for the design of a biologically inspired artificial cognitive architecture. I also suggest why these same principles could explain our ability to represent novel concepts and imagine non-existing and perhaps impossible objects, while there are still limits to what we can imagine and think about. Some ideas re- garding how these principles could be relevant for an autonomous agent to become functionally conscious are discussed as well.

I propose a number of principles that I believe are substantial for various faculties of the mammalian brain, such as perception, expectations, imagery, and memory. The same principles are also of interest when designing an artificial but biologically inspired cognitive architecture. Moreover, I discuss how the same principles may lie behind the ability to represent new concepts and to imagine fictitious and impossible objects, while also giving us reasons to believe that there are limits to our imagination and to what it is possible for us to think about. Some ideas regarding how these principles could be relevant for an autonomous agent to become functionally conscious are discussed as well.

The objective of this work is presenting a mobile platform for improving the individual development of social skills of people with mental disorders, offering them a mobile application which promotes independence and enhances social and cognitive abilities by means of geolocation and ludic reward. First, in order to improve the independence of the people with mental disorders we promote outings and physical activity by means of a mobile application which provides a personal map where familiar living places are located. In the map, the current location of the user is referenced between the familiar places. Additionally, the location is collected in a cloud server, where relatives and caregivers can locate him/her in case of need by SMS. Second, in order to improve their social skills, a mobile system of ludic rewards has been implemented using NFC tags. In this way, when the person with social disorder fulfills his/her tasks properly, the caregiver or family member brings closer an NFC tag with the value of the corresponding reward to the smartphone. So, the person with disabilities is able to check in the mobile application the reward points which he/she keeps based on his/her middle-term behavior. The virtual ludic money allows the child or person to enjoy activities, such as, watching TV, subtracting those points when they spend this reward. The system is developed as an easily scalable and configurable module to enable the personalization of parameters for each person with social disorder. 

In the complex study to obtain indicators in the autism spectrum disorder it is very common to perform many and very complex tasks. Often, these tasks require the completion of a series of forms and surveys that are even more complex and tedious, which means that the accuracy of the reports is not always satisfactory. In this paper, we propose a general architecture based on machine learning techniques and data mining for prediction of the main indicators in the diagnosis and intervention of the autistic spectrum disorder. The main idea of this approach is to replace those print documents by mobile tests, tablet or smartphones tests through games, store them in databases and analyse them. Furthermore, very often these last two steps are not undertaken with the lack of quantitative and qualitative analysis that could be generated. Finally, the presented architecture is oriented to data collection with the objective of the creation of large specialized datasets. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

We have investigated machine learning based cheating behaviour detection in physical activity-based smart-phone games. Sensor data were acquired from the accelerometer/gyroscope of an iPhone 7 during activities such as jumping, squatting, stomping, and their cheating counterparts. Selected attributes providing the most information gain were used together with a sequential model yielding promising results in detecting fake activities. Even better results were achieved by employing a random forest classifier. The results suggest that machine learning is a strong candidate for detecting cheating behaviours in physical activity-based smartphone games.

There is a growing awareness that the complexity of managing Big Data is one of the main challenges in the developing field of the Internet of Things (IoT). Complexity arises from several aspects of the Big Data life cycle, such as gathering data, storing them onto cloud servers, cleaning and integrating the data, a process involving the last advances in ontologies, such as Extensible Markup Language (XML) and Resource Description Framework (RDF), and the application of machine learning methods to carry out classifications, predictions, and visualizations. In this review, the state of the art of all the aforementioned aspects of Big Data in the context of the Internet of Things is exposed. The most novel technologies in machine learning, deep learning, and data mining on Big Data are discussed as well. Finally, we also point the reader to the state-of-the-art literature for further in-depth studies, and we present the major trends for the future.