Dexterous Hand Motion Classification and Recognition Based on Multimodal Sensing

  • Yaxu Xue
  • Zhaojie Ju
  • Kui Xiang
  • Chenguang Yang
  • Honghai Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10462)

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.

Keywords

Multimodal sensing EMG Contact force Data glove Support vector machine 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yaxu Xue
    • 1
    • 2
    • 3
  • Zhaojie Ju
    • 2
  • Kui Xiang
    • 1
  • Chenguang Yang
    • 4
  • Honghai Liu
    • 2
  1. 1.School of AutomationWuhan University of TechnologyWuhanChina
  2. 2.School of ComputingThe University of PortsmouthPortsmouthUK
  3. 3.School of Electrical and Mechanical EngineeringPingdingshan UniversityPingdingshanChina
  4. 4.College of EngineeringSwansea UniversitySwanseaUK

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