Abstract
Robotic exoskeletons are being increasingly and successfully used in neuro-rehabilitation therapy scenarios. Indeed, they allow patients to perform movements requiring more complex inter-joint coordination and gravity counterbalancing, including assisted object grasping. We propose a robust RGB-D camera-based approach for automated tracking of both still and moving objects that can be used for assisting the reaching/grasping tasks in the aforementioned scenarios. The proposed approach allows to work with non pre-processed objects, giving the possibility to propose a flexible therapy. Moreover, our system is specialized to estimate the pose of cylinder-like shaped objects to allow cylinder grasps with the help of a robotic hand orthosis.
To validate our method both in terms of tracking and of reaching/grasping performances, we present the results achieved conducting tests both on simulations and on real robotic-assisted tasks performed by a patient.
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This work has been partially funded from the EU FP7 project n. 601165 WEARHAP.
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Loconsole, C., Stroppa, F., Bevilacqua, V., Frisoli, A. (2014). A Robust Real-Time 3D Tracking Approach for Assisted Object Grasping. In: Auvray, M., Duriez, C. (eds) Haptics: Neuroscience, Devices, Modeling, and Applications. EuroHaptics 2014. Lecture Notes in Computer Science(), vol 8618. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44193-0_50
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DOI: https://doi.org/10.1007/978-3-662-44193-0_50
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