AE-CNN Based Multi-Source Data Fusion for Gait Motion Step Length Estimation

Step length estimation is essential for people to overcome different types of environmental obstacles in daily life. Aiming at the problem of low estimation accuracy based solely on surface electromyography (sEMG), in this paper, a multi-source (MS) data fusion method based on auto-encoder-convoluti...

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Bibliographic Details
Published inIEEE sensors journal p. 1
Main Authors Ling, Zi-Qin, Zhang, Yue-Peng, Cao, Guang-Zhong, Chen, Jiang-Cheng, Li, Ling-Long, Tan, Dong-Po
Format Journal Article
LanguageEnglish
Published IEEE 2022
Subjects
data fusion
Data integration
Electromyography
Estimation
Exoskeletons
Feature extraction
insole pressure
Legged locomotion
Muscles
Sensors
step length
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Summary:Step length estimation is essential for people to overcome different types of environmental obstacles in daily life. Aiming at the problem of low estimation accuracy based solely on surface electromyography (sEMG), in this paper, a multi-source (MS) data fusion method based on auto-encoder-convolutional neural network (AE-CNN) is proposed to estimate the motion parameters of human lower limbs, which improve the accuracy of step length estimation. Specifically, the time-domain feature data of sEMG and insole pressure data from the lower limbs are first collected simultaneously to form the MS. Then, by applying AE as the fusion units, fused data are obtained and dimensionality reduction is realized. Finally, by integrating AE and CNN, the step length can be well estimated by utilizing the MS. For experimental validation, the performances of different data fusion units and regression units in step length estimation are compared. The results show that the proposed method can achieve good estimation. The normalized root mean square error (NRMSE) and Pearson correlation coefficient (PCC) of proposed method reach 0.0479±0.0263 and 0.8273±0.1082, respectively. The NRMSE of the best trial is 0.0217, the PCC is 0.9774. Further, experiments at different walking speeds are carried out. The results show that the combination of AE and CNN has good performance with the increase of walking speed. In addition, a dimensionality reduction experiment based on AE is also carried out, and the optimal dimension is obtained.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3206883