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Proceedings ArticleDOI

Classification of Biceps Brachii Muscle Fatigue Condition Using Phase Space Network Features.

16 Jun 2020-Vol. 270, pp 1219-1220

TL;DR: The results of the classification indicate that these features are capable of differentiating nonfatigue and fatigue condition with 91% accuracy and can be extended to applications such as diagnosis of neuromuscular disorder where fatigue is a symptom.
Abstract: In this, study, an attempt is made to differentiate muscle nonfatigue and fatigue condition using signal complexity metrics derived from phase space network features A total of 55 healthy adult volunteers performed dynamic contraction of the biceps brachii muscle The first and last curl are segmented and are considered as nonfatigue and fatigue condition respectively A weighted phase space network is constructed and reduced to a binary network based on various radii The mean and median degree centrality features are extracted from these networks and are used for classification The results of the classification indicate that these features are capable of differentiating nonfatigue and fatigue condition with 91% accuracy This method of analysis can be extended to applications such as diagnosis of neuromuscular disorder where fatigue is a symptom
Topics: Muscle fatigue (56%)
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Journal ArticleDOI
Yongqing Zhang1, Yongqing Zhang2, Siyu Chen2, Wenpeng Cao2  +6 moreInstitutions (2)
Abstract: Muscle fatigue detection based on surface Electromyography (sEMG) is one of the essential goals of human–computer interaction. The main challenge is that the sEMG signal is unstable and complex. Meanwhile, the individual’s difference in fatigue tolerance will increase the detection difficulty. In order to reduce the impact of the above challenges, in this article, we use the sEMG signal to detect muscle fatigue based on the Multi-dimensional Feature Fusion Network (MFFNet), which is composed of Attention Frequency domain Network (AFNet) and Attention Time domain Network (ATNet). Precisely, AFNet consists of the convolutional neural network, channel attention network and spatial attention network. ATNet is composed of a two-way long and short-term memory network and time attention network. Furthermore, through the filter and Gaussian short-time Fourier transform, we can analyze the feature of the time domain and frequency domain of sEMG. Subsequently, fuse features of different dimensions are used to predict fatigue detection in many muscle fatigue detection experiments based on sEMG. The proposed method has better performance and interpretability. Experimental results prove that the proposed method can promote the development of sEMG in the field of muscle fatigue detection.

1 citations


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20211