Authors:André Pimenta1;Sergio Gonçalves2;Davide Carneiro1;Florentino Fde-riverola2;José Neves1 andPaulo Novais1
Affiliations:1University of Minho, Portugal;2University of Vigo, Spain
Keyword(s):Mental Workload, Mental Fatigue, Machine Learning, e-Learning, Fatigue Management, Human Performance.
RelatedOntology Subjects/Areas/Topics:Adaptive Systems ;Ambient Intelligence ;Applications and Services ;Computer Vision, Visualization and Computer Graphics ;Context ;Context-Aware Applications ;Detection and Estimation ;Digital Signal Processing ;Embedded Communications Systems ;Enterprise Information Systems ;Human and Computer Interaction ;Human-Computer Interaction ;Mobile and Pervasive Computing ;Paradigm Trends ;Pervasive Health ;Real-Time Systems ;Software Engineering ;Telecommunications
Abstract:In our daily life, we often have a sense of being exhausted due to mental or physical work, together with afeeling of performance degradation in the accomplishment of simple tasks. This is in part due to the fact thatthe working capacity and the performance of an individual, either physical or mental, generally decrease asthe day progresses, although factors like motivation also play a significant role. These negative effects areespecially 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 eachtask are mandatory. Such initiatives, when effective, can have a wide range of positive effects, including on theperformance, productivity, attention and even mental health. Seeking to find a viable and realistic approach toaddress this problem, this paper presents a non-invasive and non-intrusive way to measure mental workload,oneof the aspects that affects mental fatigue the most. Specifically, we target scenarios of e-learning, in whichthe professor may not be present to assess the student’s state. The aim is to create a tool that enables an actualmanagement of fatigue in such environments and thus allows for the implementation of more efficient learningprocesses, adapted to the abilities and state of each student.(More)
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.