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- A novel Approach for Muscle Fatigue Disorders Detection Using EMG Based Time-Constant Neural Network...
A novel Approach for Muscle Fatigue Disorders Detection Using EMG Based Time-Constant Neural Networks
Authors : Michael Bidollahkhani, Ferhat Atasoy
Pages : 544-556
View : 16 | Download : 33
Publication Date : 2024-01-01
Article Type : Research
Abstract :In recent years, Liquid Time-Constant (LTC) Neural Networks have gained substantial interest due to their exceptional ability to accurately model complex, time-dependent data. Although their applications in various fields have been explored, the potential of utilizing LTC Neural Networks for electromyography-based muscle fatigue or disability detection has not been investigated. This research aims to showcase the effectiveness of LTC Neural Networks in addressing challenges unique to this domain and to offer new insights into its potential advantages. We employed an LTC Neural Network to analyze EMG signals obtained during patient examinations to accomplish this objective. We calculated five features from the collected signals, including Mean Absolute Value (MAV), Waveform Length (WL), Zero Crossings (ZC), Slope Sign Changes (SSC), and Center Frequency (CF). These features were used as input for the LTC Neural Network. The network\'s ability to predict values based on temporal data enabled it to precisely monitor signal changes indicative of nerve damage or muscle dysfunction. We compared the performance of the LTC Neural Network with traditional methods and other neural network-based techniques in detecting muscle fatigue from EMG signals. Our experimental results reveal that the LTC Neural Network achieved a high validation accuracy of % 99.72, indicating its effectiveness in identifying muscle disability. These findings suggest that LTC Neural Networks have the potential to outperform conventional approaches and provide successful results in the field of EMG-based muscle fatigue detection.Keywords : Gerçek zamanlı yorgunluk analizi, Sporcu performansı izleme, Elektromiyografi, Akışkan Yapay Sinir Ağı.