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2017 | OriginalPaper | Buchkapitel

Dexterous Hand Motion Classification and Recognition Based on Multimodal Sensing

verfasst von : Yaxu Xue, Zhaojie Ju, Kui Xiang, Chenguang Yang, Honghai Liu

Erschienen in: Intelligent Robotics and Applications

Verlag: Springer International Publishing

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Abstract

Human hand motions analysis is an essential research topic in recent applications, especially for dexterous robot hand manipulation learning from human hand skills. It provides important information about gestures, moving, speed and the control force captured via multimodal sensing technologies. This paper presents a comprehensive discussion of the nature of human hand motions in terms of simple motions, such as grasps and gestures, and complex motions, e.g. in-hand manipulations and re-grasps. And then, a novel multimodal sensing based hand motion capture system is proposed to acquire the sensory information. By using an adaptive directed acyclic graph algorithm, the experimental results show the proposed system has a higher recognition rate compared with those with individual sensing technologies.

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Vorheriges Kapitel Preprocessing and Transmission for 3D Point Cloud Data

Nächstes Kapitel Static Hand Gesture Recognition with Parallel CNNs for Space Human-Robot Interaction

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Titel: Dexterous Hand Motion Classification and Recognition Based on Multimodal Sensing
verfasst von: Yaxu Xue
Zhaojie Ju
Kui Xiang
Chenguang Yang
Honghai Liu
Verlag: Springer International Publishing
Buch: Intelligent Robotics and Applications
Print ISBN: 978-3-319-65288-7

Electronic ISBN: 978-3-319-65289-4

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-65289-4_43

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