Abstract
In this paper we present a CNN-based Interface for the control of prosthetic and robotic hand: a CNN visual system is trained with a set of images of daily life object in order to classify and recognize them. Such a classification provides useful information for the configuration of prosthetic and robotic hand: following the training, in fact, a low cost embedded computer combined with a low cost camera on the device (i.e. a prosthetic or robotic hand) can drive the device in order to approach and grasp whatever object belong to the training set.
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Acknowledgements
This work was presented in thesis form in fulfilment of the requirements for the MSC in Robotic Engineering for the student Daniel David McHugh under the supervision of E.L. Secco from the Robotics Laboratory, School of Mathematics, Computer Science and Engineering, Liverpool Hope University.
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Secco, E.L., McHugh, D.D., Buckley, N. (2022). A CNN-Based Computer Vision Interface for Prosthetics’ Control. In: Gao, X., Jamalipour, A., Guo, L. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-031-06368-8_3
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DOI: https://doi.org/10.1007/978-3-031-06368-8_3
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