Mental fatigue is a symptom of many neuromuscular disorders and acute diseases and is also closely associated with the long-lasting, repetitive and/or monotonous activities of normal everyday of healthy individuals. It is commonly defined as a state that involves mental and physical tiredness or exhaustion, with a vast range of symptoms including poor concentration, lack of motivation, tired eyes, yawning, and increased blinking. In neuromuscular rehabilitation, mental fatigue of a patient is closely related to effectiveness, usability and attractiveness of the rehabilitation process itself, reflecting possible non-compliance of the end-user, either due to frustrating, exhausting, boring, annoying or error-prone aspects of rehabilitation.
The phenomenon of mental fatigue has been addressed by many studies, investigating the association between brain electrical activity using electroencephalography (EEG) and the onset of fatigue symptoms. Although highly attractive and informative, BNCI-based systems are very new and considered unreliable for use in clinical practice. On the other hand, more evident visual signs of mental fatigue, such as tired eyes, yawning, and increased blinking can be robustly detected by video-based monitoring of a person’s face but offer less direct measures of a mental fatigue.
qFATIGUE proposes development and validation of a multimodal bio-feedback interface for simultaneous extraction of mental fatigue from video-based and EEG-based monitoring of a person. The main motivation builds on the need for improved understanding and assessment of motivation in rehabilitation patients during their daily exercise and their acceptance or rejection of different reward mechanisms. The proposed information extraction interface will be benchmarked in various experimental conditions, ranging from rich graphical support, such as in virtual environments, to simple visual and audio stimulations.
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