QFATIGUE

Quantification of mental fatigue by means of visual and physiological measures

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 Obiettivo del progetto (Objective)

'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.'

Introduzione (Teaser)

Mental fatigue experienced during neuromuscular rehabilitation is currently detected using electroencephalography (EEG)-based systems. A first, researchers successfully combined video monitoring and EEG to quantify mental fatigue.

Descrizione progetto (Article)

Deterioration of selective attention is a sign of mental fatigue and this reduces the efficacy of rehabilitation. EEG activity helps identify differences in the processing of attended and unattended information but is plagued with issues such as low signal-to-noise ratio and unreliability.

Visual signs of mental fatigue include changes in eye blinking rate and pupil diameter. Video monitoring coupled with EEG activity could thus prove to be more effective than EEG-based systems alone at detecting mental fatigue. To realise this, the EU-funded 'Quantification of mental fatigue by means of visual and physiological measures' (http://lspo.feri.um.si/qFATIGUE/ (QFATIGUE)) project was initiated.

Researchers simultaneously recorded EEG signals and frontal videos of the face of volunteers during several walking tasks in a robotic gait trainer. They processed videos and extracted data such as eye blinking statistics and relative distances between eye components and eyebrows.

Besides noise and artefact removal of EEG signals, the power spectral values of different cortical regions in standard brainwave bands were used as a measure. Power spectral values were selected as they demonstrated significant differences between non-fatigued and fatigued brain activity.

Combining the two modes resulted in a multimodal fatigue model that effectively detects fatigue. The results were disseminated through several papers as well as presentations at conferences.

QFATIGUE tools should prove to be an invaluable resource in optimising time-intensive rehabilitation procedures and adapting them to individual patient needs. Such personalised rehabilitation regimens should increase patient compliance and improve outcomes.