Neural Profiling With fNIRS of Operator Performance in Teleoperated Human-Like Social Robot Interactions

Abstract

Social robot teleoperation is a skill that must be acquired through practice with the social robot. Mobile neuroimaging and human-computer interface performance metrics permit the gathering of information from the operators’ systemic and behavioral responses associated with their skill acquisition. Profiling the skill levels of social robot operators using this information can help improve training protocols. In this study, thirty-two participants performed real-world social robot teleoperation tasks. Brain function signals from the prefrontal cortex (PFC), and behavioral data from interactions with the system were collected using functional near-infrared spectroscopy (fNIRS). Participants were divided into two groups (high and low performance) based on an integrative metric of task efficiency, workload, and presence when operating the social robot. Significant differences were found in the operation time, width, and multiscale entropy of the hemoglobin oxygenation curve of the operator’s PFC. Functional connectivity in the PFC also depicted differences in the low- and high-performance groups when connectivity networks were compared and in the leaf fraction metrics of the functional networks. These findings contribute to understanding the operator’s progress during teleoperation training protocols and designing the interface to assist in enhancing task performance.