@inproceedings{ac6bb6fa212d46fca32dfe60d8740ec6,
title = "Plantar pressure data based gait recognition by using long short-term memory network",
abstract = "As a kind of continuous time series, plantar pressure data contains rich contact of time information which has not been fully utilized in existing gait recognition methods. In this paper, we proposed a new gait recognition method based on plantar pressure data with a Long Short-Term Memory (LSTM) network. By normalization and dimensionality reduction, the raw pressure data was converted to feature tensor. Then we feed the LSTM network with the feature tensors and implement classification recognition. We collected data from 93 subjects of different age groups, and each subjects was collected 10 sets of pressure data. The experiment results turn out that our LSTM network can get high classification accuracy and performs better than CNN model and many traditional methods.",
keywords = "Gait recognition, LSTM, Plantar pressure data",
author = "Xiaopeng Li and Yuqing He and Xiaodian Zhang and Qian Zhao",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 13th Chinese Conference on Biometric Recognition, CCBR 2018 ; Conference date: 11-08-2018 Through 12-08-2018",
year = "2018",
doi = "10.1007/978-3-319-97909-0\_14",
language = "English",
isbn = "9783319979083",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "128--136",
editor = "Zhenan Sun and Shiguang Shan and Zhenhong Jia and Kurban Ubul and Jie Zhou and Jianjiang Feng and Zhenhua Guo and Yunhong Wang",
booktitle = "Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings",
address = "Germany",
}