Abstract
Facial expressions are a spontaneous way of perceiving emotions, which can provide information related to the cognitive state of a person. Facial expression recognition of the elderly is an important aid to better care them, according to their state of mind, although it can be a difficult task because their expressions might not be as easily perceived as those from younger persons. We proposed a model to classify the facial expressions of the elderly, presenting the differences between facial expression recognition in the elder and in other age group, as well as methods to surpass these difficulties. Viola Jones with Haar Features was used to extract the faces and Gabor Filter to extract the facial characteristics. These characteristics are classified using a Multiclass Support Vector Machine. We got an accuracy of 90.32%, 84.61% and 66.6%, when detecting the neutral state, happiness and sadness respectively in the elderly. In the other age group, we got an accuracy of 95.24%, 88.57%, and 80%, while detecting the neutral, happiness, and sadness states and concluded that aging influences negatively the facial expressions recognition tasks.

Abstract
Aging is a natural process during which a person's physical and mental condition will degrade, imposing limitations on their lifestyle. The consequences of aging can be aggravated when the elderly permanently move to an elderly care center, causing a decline in social relations and contact with family and friends, with negative effects on the person's well-being. Therefore, it is important to act to maintain these relationships in the later stages of life. In this work we propose a roadmap to introduce and evaluate the usage of telepresence robots in elderly care centers, so that family and friends can interact with the elderly more often and with better quality. The proposal defines a set of steps to introduce the robots into the care center environment, as well as an evaluation methodology to assess the success of implementing the roadmap.

Abstract
AppVox is a mobile application that provides support for children with speech and language impairments in their speech therapy sessions, while also allowing autonomous training at home. The application simulates a vocalizer with an audio stimulus feature, which can be used to train and amend the pronunciation of specific words through repetition. In this paper, we aim to present the development of the application as an assistive technology option, by adding new features to the vocalizer as well as assessing it as a usable option for daily training interaction for children with speech and language impairments. In this regard, we invited 15 children with speech and language impairments and 20 with no impairments to perform training activities with the application. Likewise, we asked three speech therapists and three usability experts to interact, assess, and give their feedback. In this assessment, we include the following parameters: successful conclusion of the training tasks (effectiveness); number of errors made, as well as number and type of difficulties found (efficiency); and the acceptance and level of comfort in completing the requested tasks (satisfaction). Overall, the results showed that children conclude the training tasks successfully and helped to improve their language and speech capabilities. Therapists and children gave positive feedback to the AppVox interface.

Abstract
Assistive systems which incorporate different technologies to provide simple and quick, yet informative, content, have recently been proposed to alleviate the mobility and accessibility constrains of users with visual impairment. Currently, technology has reached a maturation point that allows the development of systems based on video capturing, image recognition and geo-location referencing, which are key for providing features of artificial vision, assisted navigation and spatial perception. The miniaturization of electronics can be used to create devices, such as electronic canes equipped with sensors, that can provide contextual information to a blind user. In this paper, we describe the current work on assistive systems for the blind and propose a new perspective on using the base information of those systems to provide new services to the general public. By bridging the gap between the two groups, we expect to further advance the development of the current systems and contribute to their economic sustainability.

Abstract
In this work we reviewed the current state-of-the art regarding the usage of robots, in particular telepresence robots, on educational related activities. We also researched the current consumer and corporate grade telepresence robotic equipment and tested three of these devices. Lastly, we reviewed the problem of disabled students, including students with special education needs, which fail at accessing and staying on higher education. One of the reasons for such problem is the impossibility to physically attend all the classes due to temporary or permanent limitations. As a conclusion of this work, and considering the ongoing positive cases with robotics and the current equipment availability, we propose the creation of telepresence services on the higher education institutions as a solution for those students that can’t attend classes. © Springer Nature Switzerland AG 2019.

Abstract
Information can be conveyed to the user by means of a narrative, modeled according to the user’s context. A case in point is the weather, which can be perceived differently and with distinct levels of importance according to the user’s context. For example, for a blind person, the weather is an important element to plan and move between locations. In fact, weather can make it very difficult or even impossible for a blind person to successfully negotiate a path and navigate from one place to another. To provide proper information, narrated and delivered according to the person’s context, this paper proposes a project for the creation of weather narratives, targeted at specific types of users and contexts. The proposal’s main objective is to add value to the data, acquired through the observation of weather systems, by interpreting that data, in order to identify relevant information and automatically create narratives, in a conversational way or with machine metadata language. These narratives should communicate specific aspects of the evolution of the weather systems in an efficient way, providing knowledge and insight in specific contexts and for specific purposes. Currently, there are several language generator’ systems, which automatically create weather forecast reports, based on previously processed and synthesized information. This paper, proposes a wider and more comprehensive approach to the weather systems phenomena, proposing a full process, from the raw data to a contextualized narration, thus providing a methodology and a tool that might be used for various contexts and weather systems. © 2019, Springer Nature Switzerland AG.