Abstract
Optimization of gait features using Locally Linear Embedded with Multi-layer Perceptron for frontal view is explored in this study. Static gait features within a gait cycle are extracted from gait data extracted using Kinect. The extracted features are further optimized using Locally Linear Embedded and classified using Multi-layer Perceptron. To verify the effectiveness of the proposed method, original features are also utilised. Result showed that the recognition of human gait using Multi-layer Perceptron with 30 hidden units along with optimal feature of Locally Linear Embedded with K = 88 and d = 64 outshined the recognition rate specifically 98%.
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