Muscle analysis of hand and forearm during tapping using surface electromyography

Masayuki Yokoyama, Ryohei Koyama, Masao Yanagisawa

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

    2 Citations (Scopus)

    Abstract

    Surface electromyography (sEMG) is one of the promising sensors to handle biological information especially for user interfaces with low hardware cost. However, sEMG signals are noisy and the sensor position affects to the signal-to-noise ratio (SNR). Assuming sEMG sensors to be inputs of wearable controllers of some devices (head-mounted displays for instance), we examined the SNR of sEMG signals of a forearm muscle (flexor digitorum superficialis) and two hand muscles (dorsal interossei and lumbrical) when tapped on a desk by the index finger. As a result, the SNR of sEMG signals of hands were higher than the one of the signals of forearms. The result shows hands are more suitable than forearms for wearable controllers with tapping-gesture using sEMG. Ten subjects participated, and two different forms of tapping gesture by index fingers were adopted in our experiments.

    Original languageEnglish
    Title of host publication2015 IEEE 4th Global Conference on Consumer Electronics, GCCE 2015
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages595-598
    Number of pages4
    ISBN (Print)9781479987511
    DOIs
    Publication statusPublished - 2016 Feb 3
    Event4th IEEE Global Conference on Consumer Electronics, GCCE 2015 - Osaka, Japan
    Duration: 2015 Oct 272015 Oct 30

    Other

    Other4th IEEE Global Conference on Consumer Electronics, GCCE 2015
    CountryJapan
    CityOsaka
    Period15/10/2715/10/30

    Fingerprint

    electromyography
    forearm
    Electromyography
    muscles
    Forearm
    Muscle
    Hand
    Muscles
    Signal-To-Noise Ratio
    Signal to noise ratio
    Gestures
    signal to noise ratios
    Fingers
    sensors
    controllers
    Sensors
    flexors
    Controllers
    User interfaces
    hardware

    Keywords

    • dorsal interossei
    • EMG
    • finger
    • flexor digitorum superficialis
    • flexor muscle
    • lumbrical
    • signal-to-noise ratio
    • tapping

    ASJC Scopus subject areas

    • Instrumentation
    • Biotechnology
    • Computer Networks and Communications
    • Signal Processing
    • Electrical and Electronic Engineering

    Cite this

    Yokoyama, M., Koyama, R., & Yanagisawa, M. (2016). Muscle analysis of hand and forearm during tapping using surface electromyography. In 2015 IEEE 4th Global Conference on Consumer Electronics, GCCE 2015 (pp. 595-598). [7398505] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE.2015.7398505

    Muscle analysis of hand and forearm during tapping using surface electromyography. / Yokoyama, Masayuki; Koyama, Ryohei; Yanagisawa, Masao.

    2015 IEEE 4th Global Conference on Consumer Electronics, GCCE 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 595-598 7398505.

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

    Yokoyama, M, Koyama, R & Yanagisawa, M 2016, Muscle analysis of hand and forearm during tapping using surface electromyography. in 2015 IEEE 4th Global Conference on Consumer Electronics, GCCE 2015., 7398505, Institute of Electrical and Electronics Engineers Inc., pp. 595-598, 4th IEEE Global Conference on Consumer Electronics, GCCE 2015, Osaka, Japan, 15/10/27. https://doi.org/10.1109/GCCE.2015.7398505
    Yokoyama M, Koyama R, Yanagisawa M. Muscle analysis of hand and forearm during tapping using surface electromyography. In 2015 IEEE 4th Global Conference on Consumer Electronics, GCCE 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 595-598. 7398505 https://doi.org/10.1109/GCCE.2015.7398505
    Yokoyama, Masayuki ; Koyama, Ryohei ; Yanagisawa, Masao. / Muscle analysis of hand and forearm during tapping using surface electromyography. 2015 IEEE 4th Global Conference on Consumer Electronics, GCCE 2015. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 595-598
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