EMG-Based Detection of Minimum Effective Load With Robotic-Resistance Leg Extensor Training

Miyake, Tamon; Ito, Hiromasa; Okamura, Naomi; Kobayashi, Yo; Fujie, Masakatsu G.; Sugano, Shigeki

doi:10.1109/thms.2023.3347404

Cited by 1 publication

(1 citation statement)

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“…Several wavelengths of near-infrared lights are needed to estimate accurate muscle oxygenation (or deoxygenation), which requires the accurate attachment position of optical emitters and receivers above the target muscles. Considering oxygenation mechanism is related to EMG [ 40 , 49 ], oxygenation and muscle deformation, which is the change in path length of the light, are evoked simultaneously. Therefore, observation of change in reflectance of the near-infrared light, such as in [ 39 ], includes the effect of muscle deformation.…”

Section: Discussionmentioning

confidence: 99%

Optical Myography-Based Sensing Methodology of Application of Random Loads to Muscles during Hand-Gripping Training

Miyake,

Minakuchi,

Sato

et al. 2024

Sensors

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Hand-gripping training is important for improving the fundamental functions of human physical activity. Bernstein’s idea of “repetition without repetition” suggests that motor control function should be trained under changing states. The randomness level of load should be visualized for self-administered screening when repeating various training tasks under changing states. This study aims to develop a sensing methodology of random loads applied to both the agonist and antagonist skeletal muscles when performing physical tasks. We assumed that the time-variability and periodicity of the applied load appear in the time-series feature of muscle deformation data. In the experiment, 14 participants conducted the gripping tasks with a gripper, ball, balloon, Palm clenching, and paper. Crumpling pieces of paper (paper exercise) involves randomness because the resistance force of the paper changes depending on the shape and layers of the paper. Optical myography during gripping tasks was measured, and time-series features were analyzed. As a result, our system could detect the random movement of muscles during training.

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Section: Discussionmentioning

confidence: 99%