This study presents and approach to measure the levels of acute stress in humans by analysing their behavioural patterns when interacting with technological devices. We study the effects of stress on eight behavioural, physical and cognitive features. The data was collected with the participation of 19 users in different phases, with different levels of stress induced. A non-parametric statistical hypothesis test is used to determine which features show statistically significant differences, for each user, when under stress. It is shown that the features more related to stress are the acceleration and the mean and maximum intensity of the touch. It is also shown that each user is affected by stress in a specific way. Moreover, all the process of estimating stress is undertaken in a non-invasive way. This work constitutes the foundation of a context layer for a virtual environment for conflict resolution. The main objective is to overcome some of the main drawbacks of communicating online, namely the lack of contextual information such as body language or gestures.

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.

Stress is a highly complex, subjective and multidimensional phenomenon. Nonetheless, it is also one of our strongest driving forces, pushing us forward and preparing our body and mind to tackle the daily challenges, independently of their nature. The duality of the effects of stress, that can have positive or negative effects, calls for approaches that can take the best out of this biological mechanism, providing means for people to cope effectively with stress. In this paper we propose an approach, based on mouse dynamics, to assess the level of stress of students during online exams. Results show that mouse dynamics change in a consistent manner as stress settles in, allowing for its estimation from the analysis of the mouse usage. This approach will allow to understand how each individual student is affected by stress, providing additional valuable information for educational institutions to efficiently adapt and improve their teaching processes.

a b s t r a c tFatigue, especially in its mental form, is one of the most worrying health problems nowadays. It affects not only health but also motivation, emotions and feelings and has an impact both at the individual and organizational level. Fatigue monitoring and management assumes thus, in this century, an increased importance, that should be promoted by private organizations and governments alike. While traditional approaches are mostly based on questionnaires, in this paper we present an alternative one that relies on the observation of the individual's interaction with the computer. We show that this interaction changes with the onset of fatigue and that these changes are significant enough to support the training of a neural network that can classify mental fatigue in real time. The main outcome of this work is the development of non-invasive systems for the continuous classification of mental fatigue that can support effective and efficient fatigue management initiatives, especially in the context of desk jobs.

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.