Neuro-Musculoskeletal Modeling for Online Estimation of Continuous Wrist Movements from Motor Unit Activities

Decoding movement intentions from motor unit (MU) activities remains an ongoing challenge, which restricts our comprehension of the intricate transition mechanism from microscopic neural drive to macroscopic movements. This study presents an innovative neuro-musculoskeletal (NMS) model driven by MU...

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Vydané v:IEEE transactions on neural systems and rehabilitation engineering Ročník 32; s. 3804 - 3814
Hlavní autori: Liu, Yunfei, Zhang, Xu, Zhao, Haowen, Chen, Xiang, Yao, Bo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 2024
Predmet:
Adult
Algorithms
Biological system modeling
Biomechanical Phenomena
Calibration
Computational modeling
Data models
Electrodes
Electromyography
Electromyography - methods
Estimation
Female
HD-sEMG de-composition
Humans
joint angle estimation
Male
Models, Neurological
Motor Neurons - physiology
motor unit
Motors
Movement - physiology
Muscle, Skeletal - physiology
Muscles
Neuro-musculoskeletal model
Online Systems
Tendons - physiology
Trajectory
Wrist
Wrist - physiology
Wrist Joint - physiology
Young Adult
ISSN:1534-4320, 1558-0210, 1558-0210
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Popis
Shrnutí:Decoding movement intentions from motor unit (MU) activities remains an ongoing challenge, which restricts our comprehension of the intricate transition mechanism from microscopic neural drive to macroscopic movements. This study presents an innovative neuro-musculoskeletal (NMS) model driven by MU activities for online estimation of continuous wrist movements. The proposed model employs a physiological and comprehensive utilization of MU firings and waveforms, thus facilitating the localization of MUs to muscle-tendon units (MTU) as well as the computation of MU-specific neural excitation. Subsequently, the MU-specific neural excitation was integrated to form the MTU-specific neural excitation, which were then inputted into a musculoskeletal model to accomplish the joint angle estimation. To assess the effectiveness of this model, high-density surface electromyography and angular data were collected from the forearms of eight subjects during their performance of wrist flexion-extension task. Two pieces of <inline-formula> <tex-math notation="LaTeX">8\times 8 </tex-math></inline-formula> electrode arrays and a motion capture system were employed for data acquisition. Following offline model calibration with a global optimization algorithm, online angle estimation results demonstrated a significant superiority of the proposed model over the state-of-the-art NMS models (p < 0.05), yielding the lowest normalized root mean square error (<inline-formula> <tex-math notation="LaTeX">0.10~\pm ~0.02 </tex-math></inline-formula>) and the highest determination coefficient (<inline-formula> <tex-math notation="LaTeX">0.87~\pm ~0.06 </tex-math></inline-formula>). This study provides a novel idea for the decoding of joint movements from MU activities. The research findings hold the potential to advance the development of NMS models towards the control of multiple degrees of freedom, with promising applications in the fields of motor control, biomechanics, and neuro-rehabilitation engineering.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2024.3477607