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Studying the Effect of Motor Learning on Muscle Synergies During Balance

Karami, Hojat | 2021

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53698 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Farahmand, Farzam; Baniasad, Mina
  7. Abstract:
  8. Our body makes use of many degrees of freedom in muscle space. As a result, there are many solutions for activating muscles when performing a specific task. “how our central nervous system (CNS) controls abundant degrees of freedom?” has been a popular question for researchers in the field of motor control since Bernstein. The existence of muscle synergies is one of the proposed hypothesis, and there is experimental evidence that they are encoded in our CNS as building blocks. The aim of this study is to study the effect of training on the number, structures, and activation profiles of muscle synergies that are used for maintaining balance. For this purpose, electromyography data from 28 muscles of the lower limb and trunk of eight subjects ( 16 limbs in total) were recorded in two sessions on a balance board. Then structures and activation profiles of synergies were extracted using non-negative matrix factorization and then clustered using the k-means algorithm. Results suggested that balance indices have improved; however, there is no significant change in the number of synergies. Five structures with similar patterns are utilized before and after training, which can be attributed to five strategies for maintaining balance, including two ankle strategies (posterior and anterior), a trunk strategy, a knee strategy, and a hip strategy. By observing structures carefully, except for one posterior ankle strategy, some muscle weights in other structures have changed significantly. The highest effect of training was observed on activation profiles where activation level corresponding to knee strategy was reduced in 13 out of 16 limbs
  9. Keywords:
  10. Balance Training ; Electromyography ; Motor Learning ; Locomotion Control ; Muscle Synergy ; Non-Negative Matrix Factorization (NMF)

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