Classification of dynamic in-hand manipulation based on SEMG and kinect

Yaxu Xue; Zhaojie Ju; Kui Xiang

doi:10.1109/ICIST.2018.8426180

Classification of dynamic in-hand manipulation based on SEMG and kinect

Yaxu Xue, Zhaojie Ju, Kui Xiang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

182 Downloads (Pure)

Abstract

This paper proposes a hand motion capture system for recognizing dynamic in-hand manipulation of the subjects based on the famous sensing techniques, then transferring the manipulation skills into different bionic hand applications, such as prosthetic hand, animation hand, human computer interaction. By recoding the ten defined in-hand manipulations demonstrated by different subjects, the hand motion information is captured with hybrid SEMG and Kinect. Through the data preprocessing including motion segmentation and feature extraction, recognizing ten different types of hand motions based on the rich feature information are investigated by using Marquardt-Levenberg algorithm based artificial neural network, and the experimental results show the effectiveness and feasibility of this method.

Original language	English
Title of host publication	2018 Eighth International Conference on Information Science and Technology
Subtitle of host publication	ICIST
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	342-346
ISBN (Electronic)	978-1-5386-3782-1
ISBN (Print)	978-1-5386-3783-8
DOIs	https://doi.org/10.1109/ICIST.2018.8426180
Publication status	Published - 9 Aug 2018
Event	2018 Eighth International Conference on Information Science and Technology - Cordoba, Spain Duration: 30 Jun 2018 → 6 Jul 2018

Publication series

Name	IEEE ICIST Proceedings Series
ISSN (Electronic)	2573-3311

Conference

Conference	2018 Eighth International Conference on Information Science and Technology
Country/Territory	Spain
Period	30/06/18 → 6/07/18

Access to Document

10.1109/ICIST.2018.8426180

Classification of Dynamic In-hand Manipulation based on SEMG and Kinect
© © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript (Post-print), 5.81 MB

Cite this

@inproceedings{8156f06883cd497b8a3a82b49d0135bc,

title = "Classification of dynamic in-hand manipulation based on SEMG and kinect",

abstract = "This paper proposes a hand motion capture system for recognizing dynamic in-hand manipulation of the subjects based on the famous sensing techniques, then transferring the manipulation skills into different bionic hand applications, such as prosthetic hand, animation hand, human computer interaction. By recoding the ten defined in-hand manipulations demonstrated by different subjects, the hand motion information is captured with hybrid SEMG and Kinect. Through the data preprocessing including motion segmentation and feature extraction, recognizing ten different types of hand motions based on the rich feature information are investigated by using Marquardt-Levenberg algorithm based artificial neural network, and the experimental results show the effectiveness and feasibility of this method.",

author = "Yaxu Xue and Zhaojie Ju and Kui Xiang",

year = "2018",

month = aug,

day = "9",

doi = "10.1109/ICIST.2018.8426180",

language = "English",

isbn = "978-1-5386-3783-8",

series = "IEEE ICIST Proceedings Series",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "342--346",

booktitle = "2018 Eighth International Conference on Information Science and Technology",

address = "United States",

note = "2018 Eighth International Conference on Information Science and Technology ; Conference date: 30-06-2018 Through 06-07-2018",

}

Xue, Y, Ju, Z & Xiang, K 2018, Classification of dynamic in-hand manipulation based on SEMG and kinect. in 2018 Eighth International Conference on Information Science and Technology : ICIST. IEEE ICIST Proceedings Series, Institute of Electrical and Electronics Engineers Inc., pp. 342-346, 2018 Eighth International Conference on Information Science and Technology, Spain, 30/06/18. https://doi.org/10.1109/ICIST.2018.8426180

Classification of dynamic in-hand manipulation based on SEMG and kinect. / Xue, Yaxu; Ju, Zhaojie; Xiang, Kui.

2018 Eighth International Conference on Information Science and Technology : ICIST. Institute of Electrical and Electronics Engineers Inc., 2018. p. 342-346 (IEEE ICIST Proceedings Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Classification of dynamic in-hand manipulation based on SEMG and kinect

AU - Xue, Yaxu

AU - Ju, Zhaojie

AU - Xiang, Kui

PY - 2018/8/9

Y1 - 2018/8/9

N2 - This paper proposes a hand motion capture system for recognizing dynamic in-hand manipulation of the subjects based on the famous sensing techniques, then transferring the manipulation skills into different bionic hand applications, such as prosthetic hand, animation hand, human computer interaction. By recoding the ten defined in-hand manipulations demonstrated by different subjects, the hand motion information is captured with hybrid SEMG and Kinect. Through the data preprocessing including motion segmentation and feature extraction, recognizing ten different types of hand motions based on the rich feature information are investigated by using Marquardt-Levenberg algorithm based artificial neural network, and the experimental results show the effectiveness and feasibility of this method.

AB - This paper proposes a hand motion capture system for recognizing dynamic in-hand manipulation of the subjects based on the famous sensing techniques, then transferring the manipulation skills into different bionic hand applications, such as prosthetic hand, animation hand, human computer interaction. By recoding the ten defined in-hand manipulations demonstrated by different subjects, the hand motion information is captured with hybrid SEMG and Kinect. Through the data preprocessing including motion segmentation and feature extraction, recognizing ten different types of hand motions based on the rich feature information are investigated by using Marquardt-Levenberg algorithm based artificial neural network, and the experimental results show the effectiveness and feasibility of this method.

U2 - 10.1109/ICIST.2018.8426180

DO - 10.1109/ICIST.2018.8426180

M3 - Conference contribution

SN - 978-1-5386-3783-8

T3 - IEEE ICIST Proceedings Series

SP - 342

EP - 346

BT - 2018 Eighth International Conference on Information Science and Technology

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 Eighth International Conference on Information Science and Technology

Y2 - 30 June 2018 through 6 July 2018

ER -

Classification of dynamic in-hand manipulation based on SEMG and kinect

Abstract

Publication series

Conference

Access to Document

Fingerprint

Cite this