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Objective Assessment of Human Workload in Physical Human-robot Cooperation Using Brain Monitoring

The notions of safe and compliant interaction are not sufficient to ensure effective physical human-robot cooperation. To obtain an optimal compliant behavior (e.g., variable impedance/admittance control), assessment techniques are required to measure the effectiveness of the interaction in terms of perceived workload by users. This study investigates electroencephalography (EEG) monitoring as an objective measure to classify workload in cooperative manipulation with compliance. An experimental study is conducted including two types of manipulation (gross and fine) with two admittance levels (low- and high-damping). Performance and self-reported measures indicate that a proper admittance level that enhances perceived workload is task-dependent. This information is used to form a binary classification problem (low- and high-workload) with spectral power density and coherence as the features extracted from EEG data. Using a subject-independent feature selection approach, a subject-dependent Linear Discriminant Analysis (LDA) is used for classification. An average classification rate of 81% is achieved that indicates the reliability of the proposed approach for assessing human workload in interaction with varying compliance across the gross and fine manipulation. Furthermore, to validate our proposed objective measure of workload, we have conducted a second experiment composed of both fine and gross motor tasks. Compared to interaction with a constant admittance, a lower EEG-based workload is observed with an open-loop variable admittance controller. This observation is in agreement with the subjective workload score (NASA-TLX).