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Your search keyword '"Fady Shibata-Alnajjar"' showing total 9 results
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9 results on '"Fady Shibata-Alnajjar"'

8. Temporal Features of Muscle Synergies in Sit-to-Stand Motion Reflect the Motor Impairment of Post-Stroke Patients

Author

Ichiro Miyai, Takanori Fujii, Kouji Takahashi, Qi An, Hiroki Kogami, Makoto Kinomoto, Matti Itkonen, Hironori Otomune, Hajime Asama, Hiroshi Yamasaki, Fady Shibata-Alnajjar, Atsushi Yamashita, Hiroshi Yamakawa, Noriaki Hattori, Ningjia Yang, Shingo Shimoda, and Yusuke Tamura

Subjects
Adult, Male, medicine.medical_specialty, medicine.medical_treatment, Motor Disorders, Biomedical Engineering, 01 natural sciences, Motion (physics), Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Physical medicine and rehabilitation, Internal Medicine, Medicine, Humans, Muscle activity, Muscle, Skeletal, Postural Balance, Balance (ability), Aged, Sitting Position, Rehabilitation, business.industry, Sit to stand, Electromyography, General Neuroscience, 010401 analytical chemistry, Stroke Rehabilitation, Motor impairment, Healthy elderly, Middle Aged, 0104 chemical sciences, Stroke, Standing Position, Post stroke, Female, business, 030217 neurology & neurosurgery, Algorithms
Abstract

Sit-to-stand (STS) motion is an important daily activity, and many post-stroke patients have difficulty performing STS motion. Previous studies found that there are four muscle synergies (synchronized muscle activations) in the STS motion of healthy adults. However, for post-stroke patients, it is unclear whether muscle synergies change and which features primarily reflect motor impairment. Here, we use a machine learning method to demonstrate that temporal features in two muscle synergies that contribute to hip rising and balance maintenance motion reflect the motor impairment of post-stroke patients. Analyzing the muscle synergies of age-matched healthy elderly people ( $n = 12$ ) and post-stroke patients ( $n = 33$ ), we found that the same four muscle synergies could account for the muscle activity of post-stroke patients. Also, we were able to distinguish post-stroke patients from healthy people on the basis of the temporal features of these muscle synergies. Furthermore, these temporal features were found to correlate with motor impairment of post-stroke patients. We conclude that post-stroke patients can still utilize the same number of muscle synergies as healthy people, but the temporal structure of muscle synergies changes as a result of motor impairment. This could lead to a new rehabilitation strategy for post-stroke patients that focuses on activation timing of muscle synergies.

Published
2019

9. MUSCLE SYNERGY SPACE: LEARNING MODEL TO CREATE AN OPTIMAL MUSCLE SYNERGY.

Author

Fady Shibata_Alnajjar, Tytus Wojtara, Hidenori Kimura, and Shingo Shimoda

Subjects
MUSCLES, CENTRAL nervous system, BEHAVIOR, POSTURE, NEUROSCIENCES
Abstract

Muscle redundancy allows the central nervous system (CNS) to choose a suitable combination of muscles from a number of options. This flexibility in muscle combinations allows for efficient behaviors to be generated in daily life. The computational mechanism of choosing muscle combinations, however, remains a long-standing challenge. One effective method of choosing muscle combinations is to create a set containing the muscle combinations of only efficient behaviors, and then to choose combinations from that set. The notion of muscle synergy, which was introduced to divide muscle activations into a lower-dimensional synergy space and time-dependent variables, is a suitable tool relevant to the discussion of this issue. The synergy space defines the suitable combinations of muscles, and time-dependent variables vary in lower-dimensional space to control behaviors. In this study, we investigated the mechanism the CNS may use to define the appropriate region and size of the synergy space when performing skilled behavior. Two indices were introduced in this study, one is the synergy stability index (SSI) that indicates the region of the synergy space, the other is the synergy coordination index (SCI) that indicates the size of the synergy space. The results on automatic posture response experiments show that SSI and SCI are positively correlated with the balance skill of the participants, and they are tunable by behavior training. These results suggest that the CNS has the ability to create optimal sets of efficient behaviors by optimizing the size of the synergy space at the appropriate region through interacting with the environment. [ABSTRACT FROM AUTHOR]

Published
2013
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