Characterization of Postural Control in Post-Stroke Patients by Musculoskeletal Simulation
Kohei Kaminishi*1, Dongdong Li*2, Ryosuke Chiba*3, Kaoru Takakusaki*3, Masahiko Mukaino*4, and Jun Ota*1
*1Research into Artifacts, Center for Engineering (RACE), School of Engineering, The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
*2Department of Precision Engineering, School of Engineering, The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
*3Division of Neuroscience, Department of Physiology, Asahikawa Medical University
2-1-1-1 Midorigaoka-higashi, Asahikawa, Hokkaido 078-8510, Japan
*4Department of Rehabilitation Medicine, Hokkaido University Hospital
Kita 14, Nishi 5, Kita-ku, Sapporo, Hokkaido 060-8648, Japan
An association is observed between the standing sway posture and falls in patients with stroke; hence, it is important to study their standing balance. Although there are studies on the standing balance in stroke patients, differences in control have not been adequately investigated. This study aims to propose a method to characterize the postural sway in standing stroke patients using a mathematical model. A musculoskeletal model and neural controller model were used to simulate ten stroke patients (five patients with cerebral hemorrhages and five patients with cerebral infarctions) and eight young healthy participants, and their data were monitored during quiet standing. The model parameters were adjusted by focusing on the maximum-minimum difference in sway, which was considered important in a previous study, and sway speed, which is frequently used in the analysis. The adjusted model parameters were subjected to dimension reduction using non-negative matrix factorization. Consequently, the sway characteristics of stroke patients were expressed as the magnitude of gain parameters related to the extension of the entire body. The results of this study demonstrated the possibility of representing the characteristics of postural sway as model parameters in stroke patients using a mathematical model. This characterization could lead to the design of individualized rehabilitation systems in the future.
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