Intention Matrix in Brain-Computer Interface-Based Control Signal Generation for Lower Limb Exoskeleton in Therapy Applications

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Abstract
This study aims to develop a classification strategy over brain wave signals for the accurate control of lower limb exoskeleton during therapy scenario applications. The proposed framework mainly lies on the Intention Matrix, which governs the fusion of obtained data from the exoskeleton sensors and further generates the control signals sent to the lower level. During the generation of class centroids, the Intention wave plays a significant role. In most cases, the value of Intention waves should be the highest to map the user's intention movements correctly. The Intention Matrix successfully modifies the Action Engine by assessing input from the proprioceptive sensors and recommendations from the Mitigator block. Collaboration between the human user and the exoskeleton through intention reading occurs after the user's limb initiation. This principle indicates the preliminary feasibility of the Intention Matrix in supporting the EXOS-1 Skeleton during a therapy scenario.

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