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Gait identification using one-vs-one classifier model

Abdul Raziff, Abdul Rafiez and Sulaiman, Md. Nasir and Mustapha, Norwati and Perumal, Thinagaran (2016) Gait identification using one-vs-one classifier model. In: 2016 IEEE Conference on Open Systems (ICOS), 10-12 Oct. 2016, Langkawi, Kedah, Malaysia. pp. 71-75.

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Gait has been used in many research area including medical and health. One of the ways to capture gait signal is by using the accelerometer sensor in the smartphone. In this work, gait signal is used to identify a person. The accuracy of the gait recognition while the phone held in the palm is evaluated. Besides that, the factor of linear interpolation is examined. Lastly, k-NN, MLP and SVM algorithm are compared in determining the best accuracy that works best with the OvO classifier model. From the experiment, it can be seen that the gained accuracy for k-NN and MLP are both 96.7% with only 1 misclassified. Although the work is not related to medical and health, somehow it could provide the basis in healthcare related application. From the result, it is possible in adopting the proposed method in classifying decision based on the gait signal for medical and health purposes.

Item Type:Conference or Workshop Item (Paper)
Keyword:Gait; K-NN; MLP; OvO; SVM
Faculty or Institute:Faculty of Computer Science and Information Technology
DOI Number:10.1109/ICOS.2016.7881991
ID Code:55967
Deposited By: Nabilah Mustapa
Deposited On:03 Jul 2017 17:25
Last Modified:03 Jul 2017 17:25

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