TY - JOUR
T1 - Identification of time-varying and time-scalable synergies from continuous electromyographic patterns
AU - Ramos, Felipe Moreira
AU - D'Avella, Andrea
AU - Hayashibe, Mitsuhiro
N1 - Funding Information:
Manuscript received February 23, 2019; accepted June 11, 2019. Date of publication June 24, 2019; date of current version July 3, 2019. This letter was recommended for publication by Associate Editor S. Crea and Editor P. Valdastri upon evaluation of the reviewers’ comments. This work was supported by the JSPS Grant-in-Aid for Scientific Research (B) (18H01399). (Corresponding author: Felipe Moreira Ramos.) F. M. Ramos and M. Hayashibe are with the Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai 980-8576, Japan (e-mail: f.ramos@neuro.mech.tohoku.ac.jp; hayashibe@tohoku.ac.jp).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Muscle synergies, which is the concept of modular activation of a set of muscles for producing complex motor behaviors, have been studied for a long time. Several definitions of muscle synergies have been proposed, and different algorithms have identified synergies in a large number of contexts. However, most of the studies so far used the dataset with a prior segmentation. This approach restricted the variety of movements that can be used for the muscle synergy analysis. We propose an extended version of the time-varying synergy algorithm to support continuous recordings of electromyographic signals and movements with different scales in time. We observed that the reconstruction accuracy with the new algorithm was comparable to the one of the original case scenario, whereas time-varying synergies algorithm had a poor performance when it was applied to movements with different scales in time. In addition, the similarity of parameters suggests that it is possible to identify a movement independent of movement frequency using time-varying and time-scalable synergies.
AB - Muscle synergies, which is the concept of modular activation of a set of muscles for producing complex motor behaviors, have been studied for a long time. Several definitions of muscle synergies have been proposed, and different algorithms have identified synergies in a large number of contexts. However, most of the studies so far used the dataset with a prior segmentation. This approach restricted the variety of movements that can be used for the muscle synergy analysis. We propose an extended version of the time-varying synergy algorithm to support continuous recordings of electromyographic signals and movements with different scales in time. We observed that the reconstruction accuracy with the new algorithm was comparable to the one of the original case scenario, whereas time-varying synergies algorithm had a poor performance when it was applied to movements with different scales in time. In addition, the similarity of parameters suggests that it is possible to identify a movement independent of movement frequency using time-varying and time-scalable synergies.
KW - Rehabilitation robotics
KW - motion control
KW - neurorobotics
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U2 - 10.1109/LRA.2019.2924854
DO - 10.1109/LRA.2019.2924854
M3 - Article
AN - SCOPUS:85068643650
SN - 2377-3766
VL - 4
SP - 3053
EP - 3058
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 3
M1 - 8744588
ER -