Towards a Real-Time Cognitive Load Assessment System for Industrial Human-Robot Cooperation

Abstract

Robots are increasingly present in environments shared with humans. Robots can cooperate with their human teammates to achieve common goals and complete tasks. This paper focuses on developing a real-time framework that assesses the cognitive load of a human while cooperating with a robot to complete a collaborative assembly task. The framework uses multi-modal sensory data from Electrocardiography (ECG) and Electrodermal Activity (EDA) sensors, extracts novel features from the data, and utilizes machine learning methodologies to detect high or low cognitive load. The developed framework was evaluated on a collaborative assembly scenario with a user study. The results show that the framework is able to reliably recognize high cognitive load and it is a first step in enabling robots to understand better about their human teammates.