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Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 32-294
Full Name: Jianning Wu
Position: Assistant Professor
Age: ON
Sex: Male
Address: School of Mathematics and Computer Science,Fujian Normal University,Fuzhou, Fujian, 350007 China
Country: CHINA
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E-mail address: ewujianning@gmail.com
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Title of the Paper: A Machine Learning Approach for Quantitative Assessment of Gait Symmetry
Authors as they appear in the Paper: Jianning Wu
Email addresses of all the authors: ewujianning@gmail.com
Number of paper pages: 10
Abstract: The quantitative assessment of gait symmetry or asymmetry has played a very important role in the clinical diagnostics. This paper investigated the application of an advanced machine learning approach such as support vector machine (SVM) to evaluate gait symmetry or asymmetry quantitatively, and its basic idea is that the discrimination of the functional change of between human lower extremities can be hypothesized as binary classification task. The bilateral kinetic gait data of 24 elderly participants were acquired using a strain gauge force platform during normal walking, and each gait pattern was represented as 101 dimensions vector based on the normalized stance phase. The relative operating characteristic (ROC) curves were adopted to evaluate the gait classification performance, and the cross-validation test results illustrated that SVM-based model could classify the right and left side gait patterns of lower limbs with high accuracy, resulting in an eviden!
tly improved generalization performance when compared to the ANN-based classification model. These results suggested that the SVM could capture more useful information about the symmetry or asymmetry between human lower limbs, and enhance the sensitivity to the change of gait function. The proposed technique could function as an effective tool for clinical diagnostics in the future clinical circumstance.
Keywords: Gait analysis, Gait symmetry or asymmetry, Support vector machine, Gait classification, Kinetic gait data, Elderly
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