The Prediction of Ankle Injury Based on sEMG Using Cerebellar Model Neural Network

Yu, Shou-Yan; Jiang, Haiyan; Lin, Chih‐Min

doi:10.1109/ifuzzy46984.2019.9066246

Cited by 1 publication

(1 citation statement)

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“…Musculoskeletal rehabilitation uses methods such as physical therapy to relieve pain, improve, or restore the musculoskeletal function, so as to improve the quality of life. Musculoskeletal injury covers various diseases affecting soft tissue, bone, and joints, which are common causes of long-term pain and physical disability [10]. So it is important to monitor the musculoskeletal rehabilitation status based on sEMG signal.…”

Section: Introductionmentioning

confidence: 99%

Musculoskeletal Rehabilitation Status Monitoring Based on sEMG

Han

Zhao²,

Wang³

et al. 2021

Mobile Information Systems

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The reduction and improper movements in people’s modern life will lead to physical discomfort, pain, and inflammation, which have generally affected the quality of people’s daily life and work efficiency. The pain caused by improper movements are called musculoskeletal pain, which can be relieved or eliminated with treatment. Musculoskeletal disorders are actually one of the most common medical conditions, which affects approximately one quarter of all adults in the world. Although surface electromyography (sEMG) is an acknowledged technology in musculoskeletal rehabilitation study, it is considerably significant to monitor the musculoskeletal rehabilitation status based on sEMG. In order to monitor the musculoskeletal rehabilitation status, we combine fuzzy theory with neural network. This article proposes variable size, sliding window-based, generalized, dynamic, fuzzy neural network (GD-FNN), musculoskeletal rehabilitation status monitoring, that is, the window length of sliding window of sample data changes with the size of sample period. Finally, this study made a simulation on subjects, and the experimental results show that the proposed variable size, sliding window-based GD-FNN, musculoskeletal rehabilitation status monitoring method not only has good monitoring effect but also put on a good performance in root-mean-squared error (RMSE) and mean absolute percentage error (MAPE).

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Section: Introductionmentioning

confidence: 99%