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Effects of muscle length and sensor placement on mechanomyography signals for optimized neuromuscular assessments

Author

Listed:
  • Pil Kee Min
  • Tatsuki Shimizu
  • Tota Mizuno
  • Naoaki Itakura
  • Kazuyuki Mito

Abstract

Mechanomyography (MMG) is widely used to assess neuromuscular function, yet the influence of muscle length and sensor placement on MMG signals remains unclear. In this study, we investigated MMG characteristics at different joint angles (60°, 90°, and 120°) and muscle force levels (20%, 40%, and 60% MVC) to identify the optimal measurement position. Five male participants performed isometric contractions of the biceps brachii while MMG signals were recorded from three distinct locations along the muscle. The results indicated that RMS values were highest at the muscle belly (M2) and decreased towards the periphery, while MDF remained relatively stable across conditions. Joint angle significantly influenced MMG signals (F (2,42) = 8.32, p = 0.0009), with the highest MDF values observed at 90°, suggesting optimal neuromuscular activation at mid-range angles. Additionally, muscle force levels had a significant effect on MDF (F (2,42) = Y.Y Y, p < 0.05), with a significant increase observed between 20% MVC and 60% MVC (p < 0.05). Interaction effects between joint angle and force level were also statistically significant (F (4,42) = Z.Z Z, p < 0.05), showing nonlinear trends, particularly at 90°, where MDF increased most sharply. These findings highlight the importance of muscle length and force level in MMG signal interpretation and suggest that sensor placement at the muscle belly, particularly at mid-range joint angles, provides the most reliable data (p < 0.05). This study contributes to optimizing MMG-based assessments in clinical and sports applications.

Suggested Citation

  • Pil Kee Min & Tatsuki Shimizu & Tota Mizuno & Naoaki Itakura & Kazuyuki Mito, 2025. "Effects of muscle length and sensor placement on mechanomyography signals for optimized neuromuscular assessments," Edelweiss Applied Science and Technology, Learning Gate, vol. 9(4), pages 43-53.
  • Handle: RePEc:ajp:edwast:v:9:y:2025:i:4:p:43-53:id:5935
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