Vinícius Pereira Gonçalves

NodePM: A Remote Monitoring Alert System for Energy Consumption Using Probabilistic Techniques

In this paper, we propose an intelligent method, named the Novelty Detection Power Meter (NodePM), to detect novelties in electronic equipment monitored by a smart grid. Considering the entropy of each device monitored, which is calculated based on a Markov chain model, the proposed method identifies novelties through a machine learning algorithm. To this end, the NodePM is integrated into a platform for the remote monitoring of energy consumption, which consists of a wireless sensors network (WSN). It thus should be stressed that the experiments were conducted in real environments different from many related works, which are evaluated in simulated environments. In this sense, the results show that the NodePM reduces by 13.7% the power consumption of the equipment we monitored. In addition, the NodePM provides better efficiency to detect novelties when compared to an approach from the literature, surpassing it in different scenarios in all evaluations that were carried out.

Providing adaptive smartphone interfaces targeted at elderly people: an approach that takes into account diversity among the elderly

Abstract The growth of the elderly population in many countries makes it necessary to develop appropriate technologies for them. These include mobile phones, as they are increasingly becoming cheaper and more widespread. Furthermore, many families would like their elderly relatives to use this technology as a means of fostering digital inclusion. However, the current designs for mobile devices software are aimed at a young audience, rather than taking account of the different needs of the elderly. The elderly population can be characterized by their wide diversity which can be attributed to decades of varied experiences. Moreover, this is heightened by sharp differences in acquired education levels, use of technology at work, cognitive skills and physical dexterity. The authors believe that this diversity is even more striking in developing countries, such as Brazil, since in these countries a huge economic gap still exists between different elderly people. This paper seeks to investigate: (1) What can be done to solve the problem of enabling the elderly to use smartphone interfaces and (2) how one should develop adaptive smartphone interfaces that can be targeted to the elderly. With this in mind, this paper shows how a prototyped platform was implemented and evaluated. It is worth stressing that real-life experiments with Brazilian elderly people were carried out. The results suggested that there had been a reduction in the interaction time as well as a significant increase in user satisfaction.

Assessing users’ emotion at interaction time: a multimodal approach with multiple sensors

Users’ emotional states influence decision making and are essential for the knowledge and explanation of users’ behavior with computer applications. However, collecting emotional states during the interaction time with users is a onerous task because it requires very careful handling of the empirical observation, leading researchers to carry out assessments of emotional responses only at the end of the interaction. This paper reports our research in assessing users’ behavior at interaction time and also describes the results of a case study which analyzed users’ emotional responses while interacting with a game. We argue that capturing emotions during interaction time can help us in making changes on users’ behavior (e.g., changing from stressed to a less stressed state) or even suggesting an user to have a break. This can be all possible if both (1) emotions are captured during interaction and (2) changes are suggested at runtime (e.g., through persuasion). The results of this study suggest that there are significant differences between emotional responses captured during the interaction and those declared at the end.

Enhancing intelligence in multimodal emotion assessments

Computer systems are a part of everyday life, since they influence human behavior and stimulate changes in the emotional states of the users. The assessment of users’ emotions during their interaction with computer systems can help to provide tailorable website interfaces and better recommendations systems. However, emotions are complex and difficult to identify or assess. Previous studies have shown that, in a real-world scenario, the use of single sensors do not provide an accurate emotional assessment. Hence, in this study, we propose a framework that takes into account multiple sensors so that conclusions can be drawn about the emotional state of the user at the time of interaction. The proposed multi-sensing approach includes several inputs from users (such as speech, facial movements, and everyday activities), and uses an artificial intelligent strategy to map these different responses into one or more emotional states. The Componential Emotion Theory and Scherer’s Emotional Semantic Space are used to underpin the theoretical framework. The experimental results show that the combination of outputs generated by multiple sensors provides a more accurate assessment of emotional states than when the sensors are treated individually.