Structural Optimization of a Passive ExoNET for Forearm Supination

Abstract

Impairments in forearm supination are a common consequence of stroke, impeding essential activities of daily living and hand-object interactions. To address this, we present a forearm ExoNET-a novel, passive, wearable device designed to both assist and improve forearm supination for stroke survivors and others with movement deficits by generating clinician-defined, multijoint torque patterns using elastic actuators. Guided by torque deficit measurements from a custom-designed diagnostic device and driven by a mathematical model and structural optimization algorithm, the forearm ExoNET can be customized in real time to meet individual needs, providing configurations that deliver both assistive and therapeutic applied torque fields. Initial results demonstrate that the diagnostic device effectively identifies supination torque deficits in stroke survivors, while the optimization algorithm achieves strong predictive accuracy (R²=0.987) in designing ExoNET configurations to countract these deficits. These findings underscore the forearm ExoNET's potential to support active supination across the full range of motion, offering a lowcost, accessible solution with significant promise for improving upper-limb rehabilitation outcomes.