André Pimenta

Monitoring Mental Fatigue through the Analysis of Keyboard and Mouse Interaction Patterns

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.

Detection of distraction and fatigue in groups through the analysis of interaction patterns with computers

Nowadays, our lifestyle can lead to a scatter of focus, especially when we attend to several tasks in parallel or have to filter the important information from all the remaining one. In the context of a computer this usually means interacting with several applications simultaneously. Over the day, this significant demand on our brain results in the emergence of fatigue, making an individual more prone to distractions. 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 presents a non-intrusive and non-invasive method for measuring distraction and fatigue in an individual and in a group of people. The main aim is to allow team managers to better understand the state of their collaborators, thus preparing them to take better decisions concerning their management.

A neural network to classify fatigue from human-computer interaction

Fatigue, 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 individuals 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.

Analysis of Human Performance as a Measure of Mental Fatigue

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.

Mental workload management as a tool in e-learning scenarios

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 students 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.

A Discomfort-Sensitive Chair for Pointing Out Mental Fatigue

In our busy daily life, we often have the feeling of being exhausted, accompanied with a sense of performance degradation and increase of discomfort in the execution of even simple tasks. This often takes place in the workplace and in a silent way, influencing our productivity, our performance the number of errors or the quality of our production. This paper details a chair to be used in workplace environments that is sensitive to the onset of fatigue. Based on built-in accelerometers it recognizes signs of discomfort, which may be related to mental fatigue, to point out moments when an individual should consider taking a pause or a rest. This chair complements a previously developed software for the assessment of mental fatigue from the analysis of the individual’s interaction with the computer.

Dynamically improving collective environments through mood induction procedures

In our daily living, the environment surrounding us influences us as much or more than we influence it. Whether it is a domestic, leisure or working environment, its conditions will certainly have short and long-term effects on aspects such as stress, mood or fatigue, which will in turn influence indicators such as productivity, quality of work, quality of life, personal/group performance or even health. In this paper a dynamic environment is proposed that, based on the behavioural analysis of its users, will adapt its conditions to improve particular indicators. This will result in better working environments, with an impact on the quality of the work produced.

Non-intrusive quantification of performance and its relationship to mood

The number of jobs that takes place entirely or partially in a computer is nowadays very significant. These workplaces, as many others, often offer the key ingredients for the emergence of stress and the performance drop of its long-term effects: long hours sitting, sustained cognitive effort, pressure from competitiveness, among others. This has a toll on productivity and work quality, with significant costs for both organizations and workers. Moreover, a tired workforce is generally more susceptible to negative feelings and mood, which results in a negative environment. This paper contributes to the current need for the development of non-intrusive methods for monitoring and managing worker performance in real time. We propose a framework that assesses worker performance and a case study in which this approach was validated. We also show the relationship between performance and mood.

Real time analytics for characterizing the computer user's state

- This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013.

Improving Modularity, Interoperability and Extensibility in Ambient Intelligence

Ambient Intelligence (AmI) and its related fields emerged some years ago with the exciting promise of pervasive intelligence, magic interaction mechanisms, and everywhere availability. This promise would be materialized in homes that knew all about our habits and preferences, proactive workplaces to support people’s work or personal digital assistants to improve our daily living in all aspects possible. This somewhat utopian vision, expected by many to have already taken place, remains unaccomplished and far from it. Many challenges still lay ahead which delayed and continue to delay the expected technological unravelling. In this paper we focus on the immense technological challenges of designing and implementing AmI Systems. Specifically, we propose a technological approach that will contribute to overcome some of these challenges by making developed AmI solutions more modular, interoperable, and extensible. This will result especially advantageous for large development teams or teams that span multiple institutions.