Bipedal locomotion is a basic motor activity that requires simultaneous control of multiple muscles. Physiological experiments suggest that the nervous system controls bipedal locomotion efficiently by using motor modules of synergistic muscle activations. If these modules were merged, abnormal locomotion patterns would be realized as observed in patients with neural impairments such as chronic stroke. However, sub-acute patients have been reported not to show such merged motor modules. Therefore, in this study, we examined what conditions in the nervous system merges motor modules. we built a two-dimensional bipedal locomotion model that included a musculoskeletal model with 7 segments and 18 muscles, a neural system with a hierarchical central pattern generator (CPG), and various feedback inputs from reflex organs. The CPG generated synergistic muscle activations comprising 5 motor modules to produce locomotion phases. Our model succeeded to acquire stable locomotion by using the motor modules and reflexes. Next, we examined how a pathological condition altered motor modules. Specifically, we weakened neural inputs to muscles on one leg to simulate a stroke condition. Immediately after the simulated stroke, the model did not walk. Then, internal parameters were modified to recover stable locomotion. We refitted either (a) reflex parameters or (b) CPG parameters to compensate the locomotion by adapting (a) reflexes or (b) the controller. Stable locomotion was recovered in both conditions. However the motor modules were merged only in (b). These results suggest that light or sub-acute stroke patients, who can compensate stable locomotion by just adapting reflexes, would not show merge of motor modules, whereas severe or chronic patients, who needed to adapt the controller for compensation, would show the merge, as consistent with experimental findings.
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http://dx.doi.org/10.3389/fnbot.2019.00079 | DOI Listing |
Commun Biol
January 2025
Laboratoire de Chimie Bactérienne (LCB) Institut de Microbiologie, Bioénergies et Biotechnologie (IMM), Aix-Marseille Université-CNRS, UMR 7283, Marseille, France.
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View Article and Find Full Text PDFEntropy (Basel)
December 2024
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300384, China.
Brain-computer interfaces (BCI) are an effective tool for recognizing motor imagery and have been widely applied in the motor control and assistive operation domains. However, traditional intention-recognition methods face several challenges, such as prolonged training times and limited cross-subject adaptability, which restrict their practical application. This paper proposes an innovative method that combines a lightweight convolutional neural network (CNN) with domain adaptation.
View Article and Find Full Text PDFHum Mov Sci
January 2025
Department of Sports Rehabilitation, Cheongju University, Republic of Korea. Electronic address:
This study investigated muscle synergies during squats, focusing on the individual variability in motor control strategies. Sixteen healthy young adults performed 20 squats at a consistent speed. Muscle synergies were extracted using non-negative matrix factorization, followed by k-means clustering and discriminant analysis to categorize similar muscle synergies.
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January 2025
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Deep learning has revolutionized electroencephalograph (EEG) decoding, with convolutional neural networks (CNNs) being a predominant tool. However, CNNs struggle with long-term dependencies in sequential EEG data. Models like long short-term memory and transformers improve performance but still face challenges of computational efficiency and long sequences.
View Article and Find Full Text PDFNat Commun
January 2025
Institute of Humanoid Robots, School of Engineering Science, University of Science and Technology of China, Hefei, 230026, China.
A human hand has 23-degree-of-freedom (DOF) dexterity for managing activities of daily living (ADLs). Current prosthetic hands, primarily driven by motors or pneumatic actuators, fall short in replicating human-level functions, primarily due to limited DOF. Here, we develop a lightweight prosthetic hand that possesses biomimetic 19-DOF dexterity by integrating 38 shape-memory alloy (SMA) actuators to precisely control five fingers and the wrist.
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