Robot-Assisted Haptic Rendering for Nail Hammering: A Representative of IADL Tasks

Restoring the capability to perform instrumental activities of daily living (IADLs) is an imperative step towards independent living for neurologically impaired individuals. Robot-assisted task-oriented training with haptic feedback has the potential to enhance patients’ ability to perform IADLs. However, robot-assisted haptic rendering of IADLs is extremely challenging due to their complex dynamic properties and has been rarely reported. Considering the broad impedance range characteristics from free motion to hard contact, nail hammering (NH) is chosen as a representative IADL task. This paper presents our attempts to render the NH task via a customized robot. The core technologies consist of two aspects: 1) a robot-assisted haptic modeling technique with guaranteed accuracy and computation cost (by combining practical measurement data and experience-dependent analytical functions); 2) a robot-assisted haptic rendering technique involving a haptic robot with broad impedance range and sufficient force feedback (via a low gear ratio cable transmission and redundant actuation parallel mechanism) and closed-loop impedance control with guaranteed passivity and stability. Human experiments demonstrate an accurate NH task rendering that all Pearson correlation coefficients between real and virtual tasks are larger than 0.89. The modeling sensitivity analysis showed that stiffness parameter has the greatest effect on the realism of haptic rendering, with an effect size of 0.94. This study represents an important step towards comprehensive robot-assisted task-oriented therapy with haptic feedback. Note to Practitioners—The motivation of this work is to explore the techniques of robot-assisted haptic rendering of IADL tasks. On the one hand, current task modeling approaches are hard to balance accuracy and computational efficiency. On the other hand, existing haptic platforms have difficulty in meeting the broad impedance range and large force output requirements. In this work, we firstly developed a customized haptic robot via redundant actuation (enabling high robotic stiffness and force output) and low gear ratio cable transmission (enabling low friction and high back-drivability). We then built the nail hammering (NH) task model by combining practical measurement data (for accuracy) and experience-dependent analytical functions (for computational efficiency). Finally, we achieved the haptic rendering of the NH task using closed-loop impedance control with passivity and stability analysis. The proposed robot-assisted haptic modeling and rendering techniques can be extended to the haptic display of other types of IADL tasks.