Measuring the Improvement of the Interaction Comfort of a Wearable Exoskeleton: A Multi-Modal Control Mechanism Based on Force Measurement and Movement Prediction

This paper presents a study conducted to evaluate and optimize the interaction experience between a human and a 9 DOF arm-exoskeleton by the integration of predictions based on electroencephalographic signals (EEG). Due to an ergonomic kinematic architecture and the presence of three contact points, which enable the reflection of complex force patterns, the developed exoskeleton takes full advantage of the human arm mobility, allowing the operator to tele-control complex robotic systems in an intuitive way via an immersive simulation environment. Taking into account the operator's percept and a set of constraints on the exoskeleton control system, it is illustrated how to quantitatively enhance the comfort and the performance of this sophisticated human-machine interface. Our approach of integrating EEG signals into the control of the exoskeleton guarantees the safety of the operator in any working modality, while reducing effort and ensuring functionality and comfort even in case of possible misclassification of the EEG instances. Tests on different subjects with simulated movement prediction values were performed in order to prove that the integration of EEG signals into the control architecture can significantly smooth the transition between the control states of the exoskeleton, as revealed by a significant decrease in the interaction force.