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Real-time grasp type recognition using leap motion controller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

The recognition of grasp type is essential for a more detailed analysis of human action. In this paper, we propose a novel method for real-time grasp type recognition using Leap motion controller (LMC). Our proposal is based on the tracking data provided by the LMC sensor and a series of feature descriptors are introduced and extracted from LMC data. Combining the feature descriptors of relative positions of thumb, finger joint angles and finger directions lead to the best representation of the arrangement of the fingers. And then the grasp type classification can be achieved by using a SVM classifier. An experimental study of our approach is addressed and we show that recognition rate could be improved. The current implementation is also can satisfy the real-time requirements.
Original languageEnglish
Title of host publicationIntelligent Robotics and Applications
Subtitle of host publication12th International Conference, ICIRA 2019, Shenyang, China, August 8–11, 2019, Proceedings, Part III
EditorsHaibin Yu, Jinguo Liu, Lianquing Liu, Zhaojie Liu, Dalin Zhou
Number of pages9
ISBN (Electronic)978-3-030-27535-8
ISBN (Print)978-3-030-27534-1
Publication statusPublished - 2 Aug 2019
Event12th International Conference on Intelligent Robotics and Applications - Shenyang, China
Duration: 8 Aug 201911 Aug 2019

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLecture Notes in Artificial Intelligence
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th International Conference on Intelligent Robotics and Applications
Abbreviated titleICIRA 2019


  • Zou_Real-time Grasp Type Recognition_ICIRA2019_154_final_v4

    Rights statement: This is a post-peer-review, pre-copyedit version of an article published in Intelligent Robotics and Applications. ICIRA 2019. The final authenticated version is available online at:

    Accepted author manuscript (Post-print), 378 KB, PDF document

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ID: 15451397