Abstract

Abstract
The health centers group (ACES) “Douro Sul II” is creating a Community Support Team for Palliative Care (ECSCP) in order to provide palliative care services for the ACES’s population of 73,713 registered users. The team comes as a follow-up from a strategic plan, recently issued by the health ministry, in order to serve the patients in their own homes, providing the necessary support to them, to their families, and their caregivers. This approach has several benefits for the patients and their families, as well as for the healthcare system itself. To further promote the effectiveness of the ECSCP team, it was planned to develop an information system (IS), comprising several application modules, with the main objective to monitor the patients in their homes and deliver information to support the planning and execution of the ECSCP team activities. The system is based on an electronic services platform and several mobile and web applications, to be used by the patients, team’s staff and coordination. This way, we expect to overcome the geographic issues of the ACES territory, as well as the team’s human resources constraints, while remotely monitoring the patients and providing the necessary support, if and when needed, contributing to maintaining the conditions for the patients to live with dignity and quality in the comfort of their own homes. Copyright

Abstract

Abstract
The aging process causes physical and psychological changes, as well as social changes. It is one of the major risk factors for the onset of diseases and introduces restrictions on people's lifestyle. Although it constitutes a natural process undergone by every human being, the consequences of aging may be intensified by the deterioration of the social bonds and the loss of contact with family and friends, particularly when the elderly are permanently moved to an elderly care residence center. The usage of telepresence devices has been suggested to promote social interactions between older people and their social groups, allowing people to be in touch even though they are not close. This paper reviews four cases of telepresence robots being used to support the elderly and concludes that this type of solution and technology has made considerable progress, currently finding itself in its maturity stage, as shown by the cases described.

Abstract
For elderly people, social isolation is one significant factor in the deterioration of their life's quality. It has a profound impact on the general health and is produced by the diminution of social interactions. Nowadays, there is technology that can retrieve contextual data from the user's environment and interact with him in some simple, yet effective manners. The intelligent personal assistants can interact with the person by means of natural voice language. Previously, it was created a model for the adoption of electronic intelligent assistants by the elderly, as well a preliminary evaluation of the features of the intelligent personal assistants, currently available in the consumer market. In this article, it is evaluated the option of using the current consumer digital assistants to implement the proposed model. Several assistants were examined (Amazon, Google, Microsoft, and Apple), and their functionalities evaluated by creating four interaction scenarios and assessing the assistants' compliance with these scenarios.

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.