Abstract
This research sought to establish which in-situ measures of cognitive fatigue, physical activity, social interaction, location, emotional state and facial landmarks, made using a smartphone application, could be used to indicate episodes of cognitive fatigue. This assessment was realised using cognitive tests (assessing memory, attention, reaction time, information processing speed and executive function), self-assessment, contextual factors and facial feature analysis. This study also investigated the use of an ensemble algorithm for the classification of cognitive fatigue utilising facial features and a Rotation Forest approach. Self-assessment of cognitive fatigue was shown to directly correlate with reaction time through a Psychomotor Vigilance Task (r = .643, p = .001), and self-reported increases in the level of social activity (r = .377, p = .001). Facial feature analysis revealed dominant emotions of sadness and anger when participants were cognitively fatigued. It also revealed underlying facial cues that indicated higher levels of cognitive fatigue including expressions of negative valence, and Facial Action Coding System units of increased brow furrow, eyelid tightening and lip suck. In addition, a Principle Component Analysis based Rotation Forest ensemble with a ternary output demonstrated a cognitive fatigue classification accuracy of 82.17%. The findings presented indicate that the inclusion of data relating to surrounding cognitive, social, physical and emotional factors can improve the accuracy of mobile in-situ cognitive fatigue assessment using our previously validated smartphone-based cognitive fatigue assessment approach. The findings further suggest gross-level fatigue status may be potentially classified to a reasonable degree of accuracy using facial features, which may give rise to personalised in-situ fatigue detection.
Highlights
Cognitive fatigue can be severe and debilitating and has been identified by people suffering from a range of conditions including Parkinson’s Disease [1], stroke [2], heart failure and Acquired Brain Injury [3]
Building on the understanding developed during the background review, and in our previously published work [16]–[19] — in which we validated an approach to the in-situ assessment of cognitive fatigue using a set of gamelike tasks delivered using a smartphone — this study aimed to address the research question: which in-situ measures of physical activity, social interaction, location, emotional state and facial landmarks, made using a bespoke smartphone application, could be used to indicate episodes of cognitive fatigue, as measured using our previously validated approach?
Results captured at University indicated higher levels of cognitive fatigue that were 45% greater than at Home, which is, again, consistent with social interaction levels being higher at University, indicating social interaction as a contributing factor to cognitive fatigue
Summary
Cognitive fatigue can be severe and debilitating and has been identified by people suffering from a range of conditions including Parkinson’s Disease [1], stroke [2], heart failure and Acquired Brain Injury [3]. A limitation of administering such assessments within a clinical setting is the need for a clinician to supervise, which is costly and time consuming. It does not allow assessment in-situ, within the daily locations and routines of the patient, which is where problematic instances of cognitive fatigue occur. Assessment of cognitive fatigue is not new and there are well established tools for use during assessment taking a traditional clinician-led approach. These generally involve the use of questionnaires or cognitive tests, often delivered by a medical professional. The tools used can include questionnaires, short-form questionnaires, or a variety of cognitive tests
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