@inproceedings{aeb52c9e89ed4f1ba3c2a3db6afa9794,
title = "Gesture recognition method based on deep learning",
abstract = "With the rapid development of science and technology, human-computer interaction is born more frequently around us. Human motion analysis and recognition based on attitude sensor is a new field, which overcomes many shortcomings and limitations of motion recognition based on video and is more practical. In this paper, we proposes a new method based on time gesture recognition. By analyzing the kinematics of gestures, the features of gestures are extracted and classified using Recurrent Neural Networks and their variant networks. The methods achieved an accuracy of over 98% in 16 experimenters. The results show that the algorithm can quickly and accurately identify gestures.",
keywords = "Attitude sensor, Gesture recognition, Recurrent Neural Networks",
author = "Tong Du and Xuemei Ren and Huichao Li",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 33rd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2018 ; Conference date: 18-05-2018 Through 20-05-2018",
year = "2018",
month = jul,
day = "6",
doi = "10.1109/YAC.2018.8406477",
language = "English",
series = "Proceedings - 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "782--787",
booktitle = "Proceedings - 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2018",
address = "United States",
}