Journal
2017 IEEE 13TH INTERNATIONAL COLLOQUIUM ON SIGNAL PROCESSING & ITS APPLICATIONS (CSPA)
Volume -, Issue -, Pages 303-308Publisher
IEEE
Keywords
Frontal gait; kinect; locally linear embedded; multi-layer perceptron
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Funding
- Ministry of Higher Education (MOHE) Malaysia under the Niche Research Grant Scheme (NRGS) [600-RMI/NRGS 5/3 (8/2013)]
- Institute of Research Management and Innovation (IRMI), Universiti Teknologi MARA (UiTM) [600-RMI/DANA 5/3/PSI (195/2013)]
- MOHE under MyBrain MyPhD
- Faculty of Electrical Engineering UiTM Shah Alam
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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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