Multi-Class SVM Classification of Surface EMG Signal for Upper Limb Function
Electromyography (EMG) signal is electrical manifestation of neuromuscular activation, that provides access to physiological processes which cause the muscle to generate force and produce movement and allow us to interact with the world.In this paper, an identification of six degree of freedom for evaluating and recording physiologic properties of muscles of the forearm at rest and while contracting is presented.The first step of this method is to analyze the surface EMG signal from the subjects forearm using wavelet packet transform and extract features using the singular value decomposition.In this way, a new feature space is generated from wavelet packet coefficients.The second step is to import the feature values into multi class Support Vector Machine as a classifier, to identify six degree of freedom viz.open to close, close to open, supination, pronation, flexion and extension.The results showed that an accuracy of over 96％ could be obtained for a six degree of freedom classification problem
Wavelet Packet Tranform Singular Value Decomposition Multi-class Support Vector Machine (SVM)
Navleen Singh Rekhi Ajat Shatru Arora Sukhwinder Singh Dilbag Singh
Deptt.Of ICE,Dr.B R Ambedkar NIT,Jalandhar,Punjab,India Deptt.of EIE,Sant Longowal Institute of Engineering & Technology,Longowal,Punjab,India Deptt.of CSE,Sant Longowal Institute of Engineering & Technology,Longowal,Punjab,India Deptt.of CSE,Sant Longowal Institute of Engineering & Technology,Longowal,Punjab,India Deptt.of ICE,
国际会议
北京
英文
1-4
2009-06-11（万方平台首次上网日期，不代表论文的发表时间）