The Japanese Journal of Ergonomics
Online ISSN : 1884-2844
Print ISSN : 0549-4974
ISSN-L : 0549-4974
Volume 53, Issue 1
Displaying 1-2 of 2 articles from this issue
Contribution
  • Chikako YOSHINO, Yoshihiro SHIMOMURA
    2017 Volume 53 Issue 1 Pages 1-7
    Published: February 20, 2017
    Released on J-STAGE: May 10, 2018
    JOURNAL FREE ACCESS

    The purpose of this study was to clarify sex differences in mechanomyographic (MMG) amplitude and frequency using a condenser microphone type MMG sensor during isometric muscle loading by comparing them with conventional electromyography (EMG). Isometric elbow flexion exercises were performed with a gradual increase in the load from 10% to 80% maximum voluntary contraction (MVC). Root Mean Square (RMS) values for EMG amplitude were load and BMI dependent but did not differ among sexes. The median EMG power frequency was almost constant regardless of load and tended to be of higher frequency in men. RMS values for MMG were load dependent and higher for in every load range in men. The median MMG power frequency was also load dependent but did not appear to differ among sexes. Based on EMG, women are considered to have a larger amount of slow-twitch muscle fibers and men are considered to have a larger amount of fast-twitch muscle fibers. This supports the belief that the amount and percentage of recruitment of these fibers characterizes EMG data. Furthermore, MMG amplitude is a good indicator of load.; however, the propagation characteristics of mechanical vibration may lead to steady differences among sexes independent of contraction strength.

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  • Hidenori BOTANI, Mieko OHSUGA
    2017 Volume 53 Issue 1 Pages 8-15
    Published: February 20, 2017
    Released on J-STAGE: May 10, 2018
    JOURNAL FREE ACCESS

    This study refers to a recognition algorithm for SSVEP (Steady State Visual Evoked Potential)-based brain computer interface for menu selection. Aimed at simple-to-use interfaces with no training, we propose an algorithm that requires neither calibration processes, nor machine learning. A commercial headset, “Emotiv EEG Neuroheadset,” was introduced to measure electroencephalograms in order to decrease the time required to attach electrodes. Visual stimulation with a frequency higher than 20 Hz was used to reduce the risk of photosensitive epileptic seizure. In the experiment, eight healthy young adults participated, and were asked to observe one of six squares with intensities modulated sinusoidally with six different frequencies between 20 to 30 Hz presented on an LCD (Liquid Cristal Display) with a refresh rate of 120 Hz. After studying the collected data, we have proposed an algorithm that compares the Z-scores of averaged SSVEP amplitudes of the left and right occipital lobe (O1, O2) obtained by the averaging method for each frequency, and selects the significantly large one. Five out of eight participants showed good performance; the average accuracy and ITR (Information Transfer Rate) are 83.3% and 30.5 bits/min, respectively. The average number of trials required for decisions was less than two. The best two showed 100% accuracy, and the average ITR was 35.8 bits/min.

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