Knowledge Commons of Institute of Automation,CAS
LSTM-Based Lower Limbs Motion Reconstruction Using Low-Dimensional Input of Inertial Motion Capture System | |
Tong, Lina1; Liu, Rongkai1; Peng, Liang2 | |
发表期刊 | IEEE SENSORS JOURNAL |
ISSN | 1530-437X |
2020-04-01 | |
卷号 | 20期号:7页码:3667-3677 |
通讯作者 | Peng, Liang(liang.peng@ia.ac.cn) |
摘要 | Motion capture system has been widely used in virtual reality and rehabilitation area. This study proposed a data-driven method using low-dimensional input of inertial motion capture system to reconstruct human lower-limb motions. The long short-term memory (LSTM) neural network was used and an ensemble LSTM architecture was involved to improve reconstruction performance. Besides, the selection of optimal sensor configuration scheme and time-step parameters of LSTM network was discussed in detail. The reconstruction experiment shows that the method could get the lowest reconstruction joint angle root mean square (RMS) errors of 4.031 degrees on separated motion dataset, and 5.105 degrees on completely new dataset of synthetic motions using ensemble LSTM model with 18 base learner and three sensors units. The computational consumption test shows that the single and ensemble LSTM model spend 0.15ms and 0.91ms respectively to predict next frame. These findings demonstrate that the proposed method is effective and efficient for motions reconstruction of lower limbs. |
关键词 | Motion reconstruction LSTM neural networks inertial motion capture dimension reduction sensor configuration |
DOI | 10.1109/JSEN.2019.2959639 |
关键词[WOS] | SENSORS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61603386] ; National Natural Science Foundation of China[61720106012] ; National Natural Science Foundation of China[U1613228] ; Beijing Municipal Natural Science Foundation[Z170003] |
项目资助者 | National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation |
WOS研究方向 | Engineering ; Instruments & Instrumentation ; Physics |
WOS类目 | Engineering, Electrical & Electronic ; Instruments & Instrumentation ; Physics, Applied |
WOS记录号 | WOS:000524351800032 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 多模态智能 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/38920 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
通讯作者 | Peng, Liang |
作者单位 | 1.China Univ Min & Technol Beijing, Elect Engn & Automat Dept, Beijing 100083, Peoples R China 2.Chinese Acad Sci, Inst Automat, SKLMCCS, Beijing 100190, Peoples R China |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Tong, Lina,Liu, Rongkai,Peng, Liang. LSTM-Based Lower Limbs Motion Reconstruction Using Low-Dimensional Input of Inertial Motion Capture System[J]. IEEE SENSORS JOURNAL,2020,20(7):3667-3677. |
APA | Tong, Lina,Liu, Rongkai,&Peng, Liang.(2020).LSTM-Based Lower Limbs Motion Reconstruction Using Low-Dimensional Input of Inertial Motion Capture System.IEEE SENSORS JOURNAL,20(7),3667-3677. |
MLA | Tong, Lina,et al."LSTM-Based Lower Limbs Motion Reconstruction Using Low-Dimensional Input of Inertial Motion Capture System".IEEE SENSORS JOURNAL 20.7(2020):3667-3677. |
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