Abstract
This paper investigated the most suitable multi-class support vector machine (SVM) coding design in recognising human gait based on frontal view that include one-versus-all (OVA), one-versus-one (OVO), error correcting output codes (ECOC), ordinal, sparse random and dense random algorithms. Firstly, walking gait of 30 subjects is captured using Kinect sensor. Next, all 20 skeleton joints within the full gait cycle are extracted as input features. Further, the gait features acted as inputs to the SVM classifier, specifically using linear kernel with various coding design algorithms are evaluated and tested in determining the most optimum results in recognition of human gait based on frontal view. Result proven that one-versus-all (OVA) attained the highest accuracy, specifically 96%.
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Acknowledgment
This research is funded by Ministry of Higher Education (MOHE) Malaysia under the Niche Research Grant Scheme (NRGS) Project No: 600-RMI/NRGS 5/3 (8/2013). The authors wish to thank Human Motion Gait Analysis (HMGA) Laboratory, IRMI Premier Laboratory, IRMI, UiTM, Malaysia for the instrumentation and experimental facilities provided as well as Faculty of Electrical Engineering UiTM Shah Alam for all the support given during this research.
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Sahak, R., Md Tahir, N., Yassin, I., Zaman, F.H.H.K. (2019). Kinect-Based Frontal View Gait Recognition Using Support Vector Machine. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-01054-6_37
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