@inproceedings{cb25e6e36653408eba7b1a5201f76658,
title = "The real time gait phase detection based on long short-term memory",
abstract = "Multiple sensors system has been adopted in most existing gait phase research. In this paper, we present a novel method to detect and discriminate gait phase based on single Inertial Measurement Unit (IMU). At first, we built a data acquisition system consisting of single IMU measuring the kinematic data of shank and foot switches labeling the data to collect the gait samples. And then we developed a gait phase recognition algorithm based on long short-term memory (LSTM) and trained it with phase-labeled data. The experimental results show that the algorithm has 91.4\% accuracy on the testing set. Compared with existing approaches, the proposed method ensures the accuracy of gait phase detection and avoids the complex sensor system.",
keywords = "Deep learning, Gait phase detection, IMU, LSTM",
author = "Zhen Ding and Chifu Yang and Kai Xing and Xueyan Ma and Kai Yang and Hao Guo and Yi, \{Chun Zhi\} and Feng Jiang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 3rd IEEE International Conference on Data Science in Cyberspace, DSC 2018 ; Conference date: 18-06-2018 Through 21-06-2018",
year = "2018",
month = jul,
day = "16",
doi = "10.1109/DSC.2018.00014",
language = "英语",
series = "Proceedings - 2018 IEEE 3rd International Conference on Data Science in Cyberspace, DSC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "33--38",
booktitle = "Proceedings - 2018 IEEE 3rd International Conference on Data Science in Cyberspace, DSC 2018",
address = "美国",
}