14. Grasping-posture classification using myoelectric signal on hand pre-shaping for natural control of myoelectric hand

Daiki Suzuki, Yusuke Yamanoi, Hiroshi Yamada, Ko Wakita, Ryu Kato, Hiroshi Yokoi

    Abstract

    A stationary grasping posture is classified in the control method of an electromyogram prosthetic hand. This grasping posture is static, such as an open hand posture, and one in which the operator of an electromyogram prosthetic hand intentionally continues muscular contraction. In classifying the stationary grasping posture, a movement delay of the robot hand occurs, which feels unnaturally to the operator. To solve these problems, authors propose a method that predicts a grasping posture using the surface electromyogram (sEMG) of low muscle contraction power in hand pre-shaping. In this paper, our research on the performance of grasping posture classification using sEMG for naturally reaching for and grasping an object is presented. Experimental results demonstrate that when the sEMG amplitude peaks in hand pre-shaping, it is useful in classifying the grasping posture.

    Original languageEnglish
    Title of host publicationIEEE Conference on Technologies for Practical Robot Applications, TePRA
    PublisherIEEE Computer Society
    Volume2015-August
    ISBN (Print)9781479987573, 9781479987573
    DOIs
    StatePublished - 2015 Aug 24
    EventIEEE International Conference on Technologies for Practical Robot Applications, TePRA 2015 - Woburn, United States

    Other

    OtherIEEE International Conference on Technologies for Practical Robot Applications, TePRA 2015
    CountryUnited States
    CityWoburn
    Period15/5/1115/5/12

    Fingerprint

    Prosthetics
    End effectors
    Muscle
    Robots

    Keywords

    • artificial neural networks
    • Electromyogram classification
    • human-robot interface
    • medical robotics
    • prosthetic hand

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Networks and Communications
    • Computer Science Applications
    • Computer Vision and Pattern Recognition
    • Electrical and Electronic Engineering

    Cite this

    Suzuki, D., Yamanoi, Y., Yamada, H., Wakita, K., Kato, R., & Yokoi, H. (2015). 14. Grasping-posture classification using myoelectric signal on hand pre-shaping for natural control of myoelectric hand. In IEEE Conference on Technologies for Practical Robot Applications, TePRA. (Vol. 2015-August). [7219657] IEEE Computer Society. DOI: 10.1109/TePRA.2015.7219657

    14. Grasping-posture classification using myoelectric signal on hand pre-shaping for natural control of myoelectric hand. / Suzuki, Daiki; Yamanoi, Yusuke; Yamada, Hiroshi; Wakita, Ko; Kato, Ryu; Yokoi, Hiroshi.

    IEEE Conference on Technologies for Practical Robot Applications, TePRA. Vol. 2015-August IEEE Computer Society, 2015. 7219657.

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

    Suzuki, D, Yamanoi, Y, Yamada, H, Wakita, K, Kato, R & Yokoi, H 2015, 14. Grasping-posture classification using myoelectric signal on hand pre-shaping for natural control of myoelectric hand. in IEEE Conference on Technologies for Practical Robot Applications, TePRA. vol. 2015-August, 7219657, IEEE Computer Society, IEEE International Conference on Technologies for Practical Robot Applications, TePRA 2015, Woburn, United States, 11-12 May. DOI: 10.1109/TePRA.2015.7219657
    Suzuki D, Yamanoi Y, Yamada H, Wakita K, Kato R, Yokoi H. 14. Grasping-posture classification using myoelectric signal on hand pre-shaping for natural control of myoelectric hand. In IEEE Conference on Technologies for Practical Robot Applications, TePRA. Vol. 2015-August. IEEE Computer Society. 2015. 7219657. Available from, DOI: 10.1109/TePRA.2015.7219657

    Suzuki, Daiki; Yamanoi, Yusuke; Yamada, Hiroshi; Wakita, Ko; Kato, Ryu; Yokoi, Hiroshi / 14. Grasping-posture classification using myoelectric signal on hand pre-shaping for natural control of myoelectric hand.

    IEEE Conference on Technologies for Practical Robot Applications, TePRA. Vol. 2015-August IEEE Computer Society, 2015. 7219657.

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

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