Lower Limb Movement Intent Recognition Based on Grid Search Random Forest Algorithm-Reference-Cited by-同舟云学术

Lower Limb Movement Intent Recognition Based on Grid Search Random Forest Algorithm

Published:2018 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 3rd International Conference on Robotics, Control and Automation - ICRCA '18
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Author:

Song Ge¹,Wang Yucheng²,Wang Mingming¹,Li You³

Affiliation:

1. Department of Automation, University of Science and Technology of China

2. Institute of Advanced Manufacturing Technology, Hefei Institute of Physical Science, Chinese Academy of Science

3. National Key Laboratory of Human Factors Engineering, China Astronauts Research and Training Center

Publisher

ACM Press

Reference16 articles.

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2. Subasi, A. 2013. Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Comput Biol Med. 43, 5, (2013), 576--586.

3. Y. Li, F. Gao, X. Zheng, and H. Gan. 2017. Gait Recognition Using GA-SVM Method Based on Electromyography Signal. In Proceedings of the International Conference on Intelligent Robotics and Applications (Wuhan, China, August 15-18, 2017). 313--322.

4. L. L. Chen, Y. Y. Li, T. Y. Zhang, and Q. Wen, 2015. Electromyographic movement pattern recognition based on random forest algorithm. In Proceedings of the Chinese Control Conference (Hangzhou, China, July 28-30, 2015). IEEE. 3753--3758

5. Breiman, L. 2001. Random Forests. Mach. learn. 45, (2001), 5--32.

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