Abstract
The society is changing towards a new paradigm in which an increasing number of old adults live alone. In parallel, the incidence of conditions that affect mobility and independence is also rising as a consequence of a longer life expectancy. In this paper, the specific problem of falls of old adults is addressed by devising a technological solution for monitoring these users. Video cameras, accelerometers and GPS sensors are combined in a multi-modal approach to monitor humans inside and outside the domestic environment. Machine learning techniques are used to detect falls and classify activities from accelerometer data. Video feeds and GPS are used to provide location inside and outside the domestic environment. It results in a monitoring solution that does not imply the confinement of the users to a closed environment.

Abstract
In our daily life, we often have a sense of being exhausted due to mental or physical work, together with a feeling of performance degradation in the accomplishment of simple tasks. This is in part due to the fact that the working capacity and the performance of an individual, either physical or mental, generally decrease as the day progresses, although factors like motivation also play a significant role. These negative effects are especially significant when carrying out long or demanding tasks, as often happens in an educational context. In order to avoid these effects, initiatives to promote a good management of the time and effort invested in each task are mandatory. Such initiatives, when effective, can have a wide range of positive effects, including on the performance, productivity, attention and even mental health. Seeking to find a viable and realistic approach to address this problem, this paper presents a non-invasive and non-intrusive way to measure mental workload, one of the aspects that affects mental fatigue the most. Specifically, we target scenarios of e-learning, in which the professor may not be present to assess the student's state. The aim is to create a tool that enables an actual management of fatigue in such environments and thus allows for the implementation of more efficient learning processes, adapted to the abilities and state of each student.

Abstract
Humans exhibit their personality and their behavior through their daily actions. Moreover, these actions also show how behaviors differ between different scenarios or contexts. However, Human behavior is a complex issue as it results from the interaction of various internal and external factors such as personality, culture, education, social roles and social context, life experiences, among many others. This implies that a specific user may show different behaviors for a similar circumstance if one or more of these factors change. In past work we have addressed the development of behavior-based user identification based on keystroke and mouse dynamics. However, user states such as stress or fatigue significantly change interaction patterns, risking the accuracy of the identification. In this paper we address the effects of these variables on keystroke and mouse dynamics. We also show how, despite these effects, user identification can be successfully carried out, especially if task-specific information is considered.

Abstract
Touch screens are nowadays one of the major interfaces in the interaction between humans and technology, mostly due to the significant growth in the use of smartphones and tablets in the last years. This broad use, that reaches people from all strata of society, makes touch screens a relevant tool to study the mechanisms that influence the way we interact with electronic devices. In this paper we collect data regarding the interaction patterns of different users with mobile devices. We present a way to formalize these interaction patterns and analyze how aspects such as age and gender influence them. The results of this research may be relevant for developing mobile applications that identify and adapt to the users or their characteristics, including impairments in fine motor skills or in cognitive function.

Abstract
In our living, we often have a sense of being tired due to a mental or physical work, plus a feeling of performance degradation even in the accomplishment of simple tasks. However, these mental states are often not consciously felt or are ignored, an attitude that may result in human failures, errors and even in the occurrence of health problems or on a decrease in the quality of life. States of fatigue may be detected with a close monitoring of some indicators, such as productivity, performance or even the health states. In this work it is proposed a model and a prototype to detect and monitor fatigue based on some of these items. We focus specifically on mental fatigue, a key factor in an individual's performance. With this approach we aim to develop leisure and work context-aware environments that may improve the quality of life and the individual performance of any human being.

Abstract
In our daily life, we often have the feeling of being exhausted due to mental or physical work, and a sense of performance degradation in the execution of simple tasks. The maximum capacity of operation and performance of an individual, whether physical or mental, usually also decreases gradually as the day progresses. The loss of these resources is linked to the onset of fatigue, which is particularly noticeable in long and demanding tasks or repetitive jobs. However, good management of the working time and effort invested in each task, as well as the effect of breaks at work, can result in better performance and better mental health, delaying the effects of fatigue. This paper details a non-invasive approach on the monitoring of fatigue of a human being, based on the analysis of the performance of his interaction with the computer.