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Temporal Muscle Synergy Features Estimate Effects of Short-Term Rehabilitation in Sit-to-Stand of Post-Stroke Patients

Authors :
Moeka Sonoo
Atsushi Yamashita
Hironori Otomune
Kazunori Yoshida
Yusuke Tamura
Takanori Fujii
Kouji Takahashi
Hajime Asama
Hiroki Kogami
Qi An
Matti Itkonen
Fady Shibata-Alnajjar
Makoto Kinomoto
Hiroshi Yamakawa
Noriaki Hattori
Ningjia Yang
Shingo Shimoda
Ichiro Miyai
Hiroshi Yamasaki
Source :
IEEE Robotics and Automation Letters. 5:1796-1802
Publication Year :
2020
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), 2020.

Abstract

Sit-to-stand (STS) motion is an important daily activity and many post-stroke patients have difficulty in performing the STS motion. Post-stroke patients who can perform STS independently, still utilize four muscle synergies (synchronized muscle activation) as seen in healthy people. In addition, temporal muscle synergy features can reflect motor impairment of post-stroke patients. However, it has been unclear whether post-stroke patients improve their STS movements in short-term rehabilitation and which muscle synergy features can estimate this improvement. Here, we demonstrate that temporal features of muscle synergies which contribute to body extension and balance maintenance can estimate the effect of short-term rehabilitation based on machine learning methods. By analyzing muscle synergies of post-stroke patients (n = 33) before and with the intervention of physical therapists, we found that about half of the patients who were severely impaired, improved activation timing of muscle synergy to raise the hip with the intervention. Additionally, we identified the temporal features that can estimate whether severely impaired post-stroke patients improve. We conclude that temporal features of muscle synergies can estimate the motor recovery in short-term rehabilitation of post-stroke patients. This finding may lead to new rehabilitation strategies for post-stroke patients that focus on improving activation timing of different muscle synergies.

Details

ISSN :
23773774
Volume :
5
Database :
OpenAIRE
Journal :
IEEE Robotics and Automation Letters
Accession number :
edsair.doi...........94071f3ff9cd01f5d0697f14ebcc0baa
Full Text :
https://doi.org/10.1109/lra.2020.2969942