This work presents the main steps of design and testing of a novel haptic interface and adaptive admittance control scheme for optimal regulation of the human–machine interaction in hand rehabilitation mediated by a smart system in virtual–reality environment. The prototype development is the result of an integrated HW/SW design and, moreover, the advantages from additive manufacturing techniques and mechanical properties of soft materials are exploited for the realization steps. Indeed, to make the interface smart, a network of piezo-resistive force sensors is embedded into the user’s command interface and the acquired signals are used for the adaptive regulation of human–machine interaction. Another distinctive feature of the haptic interface, which enables to identify this latter as a smart system, is the interaction control based on the estimation of the user’s intention within a novel scheme of adaptive admittance control. The enhanced training process in rehabilitation assisted by the haptic interface and virtual environment has been experimentally validated during a series of goal-directed tasks. The improvement of the motor performance of the user under the assistance of the adaptive admittance control has been experimentally evaluated. Further results show that the rehabilitation system supports the quantitative assessment of the robustness of the motor learning performance of the hand under the generation of haptic disturbances.

Design, testing and control of a smart haptic interface driven by 3D-printed soft pneumatic actuators for virtual reality-based hand rehabilitation / Dragone, Donatella; Randazzini, Luigi; Stano, Gianni; Capace, Alessia; Nesci, Francesca; Cosentino, Carlo; Amato, Francesco; Colao, Roberto; Percoco, Gianluca; Merola, Alessio. - In: SMART MATERIALS AND STRUCTURES. - ISSN 0964-1726. - 32:4(2023), pp. 1-16. [10.1088/1361-665X/acbd76]

Design, testing and control of a smart haptic interface driven by 3D-printed soft pneumatic actuators for virtual reality-based hand rehabilitation

Amato, Francesco;
2023

Abstract

This work presents the main steps of design and testing of a novel haptic interface and adaptive admittance control scheme for optimal regulation of the human–machine interaction in hand rehabilitation mediated by a smart system in virtual–reality environment. The prototype development is the result of an integrated HW/SW design and, moreover, the advantages from additive manufacturing techniques and mechanical properties of soft materials are exploited for the realization steps. Indeed, to make the interface smart, a network of piezo-resistive force sensors is embedded into the user’s command interface and the acquired signals are used for the adaptive regulation of human–machine interaction. Another distinctive feature of the haptic interface, which enables to identify this latter as a smart system, is the interaction control based on the estimation of the user’s intention within a novel scheme of adaptive admittance control. The enhanced training process in rehabilitation assisted by the haptic interface and virtual environment has been experimentally validated during a series of goal-directed tasks. The improvement of the motor performance of the user under the assistance of the adaptive admittance control has been experimentally evaluated. Further results show that the rehabilitation system supports the quantitative assessment of the robustness of the motor learning performance of the hand under the generation of haptic disturbances.
2023
Design, testing and control of a smart haptic interface driven by 3D-printed soft pneumatic actuators for virtual reality-based hand rehabilitation / Dragone, Donatella; Randazzini, Luigi; Stano, Gianni; Capace, Alessia; Nesci, Francesca; Cosentino, Carlo; Amato, Francesco; Colao, Roberto; Percoco, Gianluca; Merola, Alessio. - In: SMART MATERIALS AND STRUCTURES. - ISSN 0964-1726. - 32:4(2023), pp. 1-16. [10.1088/1361-665X/acbd76]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/931664
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