Continuous estimation of wrist torques with stack-autoencoder based deep neural network: A preliminary study

The continuous estimation of kinematics or kinetics from electromyography (EMG) signals is essential for intuitive control of prostheses and other human-machine interfaces based on bioelectrical signals. In this preliminary study, we concentrate on the continuous estimation of wrist torques under is...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International IEEE/EMBS Conference on Neural Engineering (Online) s. 473 - 476
Hlavní autoři: Yu, Yang, Chen, Chen, Sheng, Xinjun, Zhu, Xiangyang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.03.2019
Témata:
Biological neural networks
Electrodes
Electromyography
Estimation
Training
Wrist
ISSN:1948-3554
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract The continuous estimation of kinematics or kinetics from electromyography (EMG) signals is essential for intuitive control of prostheses and other human-machine interfaces based on bioelectrical signals. In this preliminary study, we concentrate on the continuous estimation of wrist torques under isometric contraction of three separate degrees-of-freedom (D-oFs) with a stack-autoencoder based deep neural network. With this kind of deep neural network, features used for regression could be extracted autonomously other than in hand-crafted manner. Five subjects participated in the experiment under a visual feedback guide interface, in which surface EMG signals and wrist torques were concurrently recorded. It is shown that a promising estimation performance is achieved in all three DoFs. The outcomes of this study demonstrate the feasibility of this method on continuous estimation of wrist torques and reveal the potential for further being extended into continuous and simultaneous myoelectric control.
AbstractList The continuous estimation of kinematics or kinetics from electromyography (EMG) signals is essential for intuitive control of prostheses and other human-machine interfaces based on bioelectrical signals. In this preliminary study, we concentrate on the continuous estimation of wrist torques under isometric contraction of three separate degrees-of-freedom (D-oFs) with a stack-autoencoder based deep neural network. With this kind of deep neural network, features used for regression could be extracted autonomously other than in hand-crafted manner. Five subjects participated in the experiment under a visual feedback guide interface, in which surface EMG signals and wrist torques were concurrently recorded. It is shown that a promising estimation performance is achieved in all three DoFs. The outcomes of this study demonstrate the feasibility of this method on continuous estimation of wrist torques and reveal the potential for further being extended into continuous and simultaneous myoelectric control.
Author Zhu, Xiangyang
Yu, Yang
Chen, Chen
Sheng, Xinjun
Author_xml – sequence: 1
  givenname: Yang
  surname: Yu
  fullname: Yu, Yang
  organization: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
– sequence: 2
  givenname: Chen
  surname: Chen
  fullname: Chen, Chen
  organization: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
– sequence: 3
  givenname: Xinjun
  surname: Sheng
  fullname: Sheng, Xinjun
  organization: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
– sequence: 4
  givenname: Xiangyang
  surname: Zhu
  fullname: Zhu, Xiangyang
  organization: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
BookMark eNotkMtKAzEYRqMoWGv3gpu8wNRcJ4m7UuoFioLoumQmfzB2mtQkQ-nbW9DVWX0HvnONLmKKgNAtJXNKibl_Xb3PGaFmrhVtjaBnaGaUppLrVhlGyTmaUCN0w6UUV2hWyjchhDMiqNETNCxTrCGOaSwYSg07W0OKOHl8yKFUXFP-GaHgQ6hfuFTbbxs71gSxTw4y7mwBhx3AHkcYsx1OqIeUtw94gfcZhrAL0ebjaTq64w269HYoMPvnFH0-rj6Wz8367elluVg3gSpZGw4WtOiJJ8Rpa5gCUG1HmBTKs9YC9dQq53rnifDUOCud8rzrdG-kJIrxKbr78wYA2Ozz6VU-bv778F8GhF3j
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/NER.2019.8716941
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Xplore Digital Library
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 9781538679210
1538679213
EISSN 1948-3554
EndPage 476
ExternalDocumentID 8716941
Genre orig-research
GroupedDBID 6IE
6IF
6IL
6IN
AAWTH
ABLEC
ADZIZ
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
CHZPO
IEGSK
OCL
RIE
RIL
ID FETCH-LOGICAL-i175t-3eae84c0f00d8a927ee76b02547f26ae1f1a7ddcdf04f19da5d7f3bb8c9550723
IEDL.DBID RIE
ISICitedReferencesCount 7
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000469933200117&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Wed Aug 27 02:47:02 EDT 2025
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i175t-3eae84c0f00d8a927ee76b02547f26ae1f1a7ddcdf04f19da5d7f3bb8c9550723
PageCount 4
ParticipantIDs ieee_primary_8716941
PublicationCentury 2000
PublicationDate 2019-March
PublicationDateYYYYMMDD 2019-03-01
PublicationDate_xml – month: 03
  year: 2019
  text: 2019-March
PublicationDecade 2010
PublicationTitle International IEEE/EMBS Conference on Neural Engineering (Online)
PublicationTitleAbbrev NER
PublicationYear 2019
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0003204198
Score 1.7333565
Snippet The continuous estimation of kinematics or kinetics from electromyography (EMG) signals is essential for intuitive control of prostheses and other...
SourceID ieee
SourceType Publisher
StartPage 473
SubjectTerms Biological neural networks
Electrodes
Electromyography
Estimation
Training
Wrist
Title Continuous estimation of wrist torques with stack-autoencoder based deep neural network: A preliminary study
URI https://ieeexplore.ieee.org/document/8716941
WOSCitedRecordID wos000469933200117&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEA61ePCk0opvcvBobPaZjTeRFk-liEJvJY8JFMtuWXcr_nsz2VoRvHjKElgWZpKd5_cNITdWQxJpx1mc5palPuBg3ugJFhud5VgZVFCEYRNiOi3mcznrkdsdFgYAQvMZ3OFjqOXbyrSYKhuhcy8Rpb4nRN5htXb5lCTmqQ-gvyuRXI6m42ds3fJnoXvt1_yUYD4mh__78BEZ_uDw6GxnYY5JD8oBWSGh1LJsfchOkSKjwx7SytEPvLDUB9H4r6eYYaXe9zNvTLVNhYSVFmqKZstSC7CmyGWpVn4JneD39IGua1iFMV_1Jw3Es0PyOhm_PD6x7cwEtvSOQMMS8MJNDXec20LJWACIXCPkXbg4VxC5SAlrjXU8dZG0KrPCJVoXRiKzWZyckH5ZlXBKKKRGZM65DLTzihRaKS40gHQpxCpSZ2SAklqsO1qMxVZI539vX5ADVEbXvnVJ-k3dwhXZN5tm-V5fB11-AWo8pQI
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS8MwFA9jCnpS2cRvc_BoXdqmTetNZGPiLEMm7DaS5gWGYx21VfzvzUvnRPDiKSVQCu8lfZ-_3yPkSisIfWWYF_BYe9wGHJ41esILchXFWBmUkLhhEyLLkuk0HbfI9QYLAwCu-Qxu8NHV8nWR15gq66FznyJKfSviPGANWmuTUQkDxm0I_V2LZGkv6z9j85Y9Dc2LvyaoOAMy2Pvfp_dJ9weJR8cbG3NAWrDskAVSSs2XtQ3aKZJkNOhDWhj6gVeW2jAa__YUc6zUen_5qyfrqkDKSg0lRcOlqQZYUWSzlAu7uF7wW3pHVyUs3KCv8pM66tkueRn0J_dDbz01wZtbV6DyQrDi5TkzjOlEpoEAELFC0LswQSzBN74UWufaMG78VMtICxMqleQpcpsF4SFpL4slHBEKPBeRMSYCZawqhZKSCQWQGg6B9OUx6aCkZquGGGO2FtLJ39uXZGc4eRrNRg_Z4ynZRcU0zVxnpF2VNZyT7fy9mr-VF06vXydlqEk
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=International+IEEE%2FEMBS+Conference+on+Neural+Engineering+%28Online%29&rft.atitle=Continuous+estimation+of+wrist+torques+with+stack-autoencoder+based+deep+neural+network%3A+A+preliminary+study&rft.au=Yu%2C+Yang&rft.au=Chen%2C+Chen&rft.au=Sheng%2C+Xinjun&rft.au=Zhu%2C+Xiangyang&rft.date=2019-03-01&rft.pub=IEEE&rft.eissn=1948-3554&rft.spage=473&rft.epage=476&rft_id=info:doi/10.1109%2FNER.2019.8716941&rft.externalDocID=8716941