ArticleHealthcare (Basel, Switzerland)2022
Visualization and Semantic Labeling of Mood States Based on Time-Series Features of Eye Gaze and Facial Expressions by Unsupervised Learning.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study is intended to develop a stress measurement and visualization system for stress management in terms of simplicity and reliability. We present a classification and visualization method of mood states based on unsupervised machine learning (ML) algorithms. Our proposed method attempts to examine the relation between mood states and extracted categories in human communication from facial expressions, gaze distribution area and density, and rapid eye movements, defined as saccades. Using a psychological check sheet and a communication video with an interlocutor, an original benchmark dataset was obtained from 20 subjects (10 male, 10 female) in their 20s for four or eight weeks at weekly intervals. We used a Profile of Mood States Second edition (POMS2) psychological check sheet to extract total mood disturbance (TMD) and friendliness (
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