Identification of parameters for human mental states to advance dynamic human-robot interaction
Successful interactions between humans and collaborative robots (Cobots) require the ability of the cobot to predict human behavior and respond with behavioral adjustments to avoid potential accidents.
This requires the assessment of cognitive and emotional states as well a personality traits of the human user. For example, human performance is significantly affected by the current state of attention. By investigating human behavior during different states of attention we aim to find reliable psycho-physiological parameters that can effectively predict performance. Second, we want to explore how attentive states are affected by external manipulation and inter-individual personality traits. By doing so, we hope to advance our understanding of the complex interplay between mental states and performance, and to contribute to the development of effective and safe cobots.
Methods
Behavior, EEG, Eye tracking
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