TY - JOUR
T1 - Artificial neural network that modifies muscle activity in sit-to-stand motion using sensory input
AU - Yoshida, Kazunori
AU - An, Qi
AU - Hamada, Hiroyuki
AU - Yamakawa, Hiroshi
AU - Tamura, Yusuke
AU - Yamashita, Atsushi
AU - Asama, Hajime
N1 - Funding Information:
This work was supported by JSPS KAKENHI [grant numbers JP20J10255, JP18H01405, JP19K22799, and JP19H05729]. We would like to thank Editage (www.editage.com) for English language editing.
Publisher Copyright:
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2021
Y1 - 2021
N2 - Sit-to-stand motion is an important daily activity, and it is important to study the mechanism of the motion to improve the ability when it becomes weak. To study the mechanism, we hypothesized that muscle synergy generates muscle activity as a feedforward signal, which is modified by sensory input. This study focuses on determining the sensory input primarily used for modifying sit-to-stand motion. To obtain this, we built artificial neural network models that generate muscle activities based on sensory input and feedforward signals and analyzed the effect of each input on the output. The models were built for each motion phase. The input was information from vestibular and somatosensory input and averaged muscle synergy as feedforward signals, and the output was muscle synergy. As a result, it was revealed that humans may primarily use hip angle to bend forward, ankle and vertical foot reaction force to hip rise, ankle, knee, and lumber angles and vertical foot reaction force to extend body, and lumber angle to stabilize. This indicates the type of sensory input used to control each muscle synergy in each motion phase. The information should be used to modify the sit-to-stand motion in environmental conditions where the motion is performed.
AB - Sit-to-stand motion is an important daily activity, and it is important to study the mechanism of the motion to improve the ability when it becomes weak. To study the mechanism, we hypothesized that muscle synergy generates muscle activity as a feedforward signal, which is modified by sensory input. This study focuses on determining the sensory input primarily used for modifying sit-to-stand motion. To obtain this, we built artificial neural network models that generate muscle activities based on sensory input and feedforward signals and analyzed the effect of each input on the output. The models were built for each motion phase. The input was information from vestibular and somatosensory input and averaged muscle synergy as feedforward signals, and the output was muscle synergy. As a result, it was revealed that humans may primarily use hip angle to bend forward, ankle and vertical foot reaction force to hip rise, ankle, knee, and lumber angles and vertical foot reaction force to extend body, and lumber angle to stabilize. This indicates the type of sensory input used to control each muscle synergy in each motion phase. The information should be used to modify the sit-to-stand motion in environmental conditions where the motion is performed.
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U2 - 10.1080/01691864.2021.1917452
DO - 10.1080/01691864.2021.1917452
M3 - Article
AN - SCOPUS:85105135879
JO - Advanced Robotics
JF - Advanced Robotics
SN - 0169-1864
ER -