SUMMARYRobotic devices are well-suited to provide high intensity upper limb therapy in order to induce plasticity and facilitate recovery from brain and spinal cord injury. In order to realise gains in functional independence, devices that target the distal joints of the arm are necessary. Further, the robotic device must exhibit key dynamic properties that enable both high dynamic transparency for assessment, and implementation of novel interaction control modes that significantly engage the participant. In this paper, we present the kinematic design, dynamical characterization, and clinical validation of the RiceWrist-S, a serial robotic mechanism that facilitates rehabilitation of the forearm in pronation-supination, and of the wrist in flexion-extension and radial-ulnar deviation. The RiceWrist-Grip, a grip force sensing handle, is shown to provide grip force measurements that correlate well with those acquired from a hand dynamometer. Clinical validation via a single case study of incomplete spinal cord injury rehabilitation for an individual with injury at the C3-5 level showed moderate gains in clinical outcome measures. Robotic measures of movement smoothness also captured gains, supporting our hypothesis that intensive upper limb rehabilitation with the RiceWrist-S would show beneficial outcomes.
We demonstrate the interaction control capabilities of the MR-SoftWrist, a novel MR-compatible robot capable of applying accurate kinesthetic feedback to wrist pointing movements executed during fMRI. The MR-SoftWrist, based on a novel design that combines parallel piezoelectric actuation with compliant force feedback, is capable of delivering 1.5 N [Formula: see text] of torque to the wrist of an interacting subject about the flexion/extension and radial/ulnar deviation axes. The robot workspace, defined by admissible wrist rotation angles, fully includes a circle with a 20 deg radius. Via dynamic characterization, we demonstrate capability for transparent operation with low (10% of maximum torque output) backdrivability torques at nominal speeds. Moreover, we demonstrate a 5.5 Hz stiffness control bandwidth for a 14 dB range of virtual stiffness values, corresponding to 25%-125% of the device's physical reflected stiffness in the nominal configuration. We finally validate the possibility of operation during fMRI via a case study involving one healthy subject. Our validation experiment demonstrates the capability of the device to apply kinesthetic feedback to elicit distinguishable kinetic and neural responses without significant degradation of image quality or task-induced head movements. With this study, we demonstrate the feasibility of MR-compatible devices like the MR-SoftWrist to be used in support of motor control experiments investigating wrist pointing under robot-applied force fields. Such future studies may elucidate fundamental neural mechanisms enabling robot-assisted motor skill learning, which is crucial for robot-aided neurorehabilitation.
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