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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

To promote rapid recovery and quality of life after a musculoskeletal disorder, rehabilitation exercises that are suitable for each individual’s physical condition are important. In cases of disuse muscle atrophy of the quadriceps, inappropriate training can cause injury. Although resistance-training robotic systems have been developed and could adjust resistance load, a systematic detection method with appropriate force strength for automatic adjustment for each individual has not yet been established. In the current study, we developed an electromyogram (EMG) based method that determines the minimum effective resistance load for muscle growth. Using an integrated EMG (IEMG) model of incremental resistance load focused, we constructed a method to determine the minimum effective resistance load with logarithmic functions. The feasibility of our method was tested with a slow training protocol using a wire-driven leg extension training robot to measure the relationship between IEMG and resistance load by applying the incremental resistance load. The proposed model was found to be suitable for six young and four elderly subjects with different levels of muscle mass, and the load derived for each person was shown to induce effectively acute thigh circumference expansion, which is a factor leading to future muscle hypertrophy.

Original languageEnglish
Pages (from-to)34-43
Number of pages10
JournalIEEE Transactions on Human-Machine Systems
Volume54
Issue number1
DOIs
Publication statusPublished - 2024 Feb 1

Keywords

  • Electromyogram (EMG)
  • exercise therapy
  • integrated electromyogram (IEMG)
  • leg extension
  • rehabilitation robotics

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Control and Systems Engineering
  • Signal Processing
  • Human-Computer Interaction
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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