Papers by Keyword: Feature Evaluation

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Authors: Hyeon Ki Choi, Jae Hoon Jeong, Sung Ho Hwang, Hyeon Chang Choi, Won Hak Cho
Abstract: We recognized EMG signal patterns of lower limb muscles by using neural networks and performed feature evaluation during the recovery of postural balance of human body. Surface electrodes were attached to lower limb and EMG signals were collected during the balance recovery process from a perturbation without permitting compensatory stepping. A waist pulling system was used to apply transient perturbations in five horizontal directions. The EMG signals of fifty repetitions of five motions were analyzed for ten subjects. Twenty features were extracted from EMG signals of one event. Feature evaluation was also performed by using DB (Davies-Bouldin) index. By using neural networks, EMG signals were classified into five categories, such as forward perturbation, backward perturbation, lateral perturbation and two oblique perturbations. As results, motions were recognized with mean success rates of 75 percent. With the neural networks classifier of this study, the EMG patterns of lower limb muscles during the recovery of postural balance can be classified with high accuracy of recognition.
Authors: Zhi Bin Yu, Chun Xia Chen
Abstract: The one-dimensional feature-separability model concerning the feature-separability issue of radar emitter signals is proposed based on the probability theory and statistical theory, to evaluate the deinterleaving and recognition capability of extracted features. The proposed method is applied to analyze convention features of radar emitter signals. The theoretical analysis and experimental results show that the proposed model offers a new way to analyze the validity of extracted features, and is valid in both the original feature space and linear-transformed feature space.
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