As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
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.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.