Neuroadaptive Systems for Improving User Performance
Human-robot collaboration is a promising paradigm in many fields due to its potential to exploit the strength of human flexibility and robot precision (Reason, 2000). Even with exceedingly sophisticated and highly evolved technologies, robotic systems are primarily operated by humans with varying degrees of intervention and control (Power et al., 2015). However, the teleoperated control that requires the surgeons to manipulate the robotic arms remotely may introduce problems such as ambiguity and a lack of motion feedback (Chen et al., 2007), resulting in excessive mental workload (MWL) that can compromise surgeon performance. As extreme MWL degrades performance and increases error probability (Yurko et al., 2010), operator workload is becoming a central concern in determining successful human-robot collaboration. Consequently, there has been an increased interest in developing robots that can provide operators with varying levels of assistance based on their MWL during task execution (i.e., mental workload-based adaptive automation) (MWL-AA).
