Exploration of deep learning-driven multimodal information fusion frameworks and their application in lower limb motion recognition
Authors:
- Changhe ZhangCZChanghe Zhang
- Zidong YuZYZidong Yu
- Xiaoyun WangXWXiaoyun Wang
- Ze-Jian ChenZCZe-Jian Chen
- Chao DengCDChao Deng
- Sheng Quan XieSXSheng Quan Xie
- 13 June 2024
- journal article
- research article
- Published by Elsevier in Biomedical Signal Processing and Control
- Vol. 96
Abstract
No abstract availableKeywords
This publication has 51 references indexed in Scilit:
- Lower limb motion recognition based on surface electromyographyBiomedical Signal Processing and Control, 2022
- Electromyography-controlled lower extremity exoskeleton to provide wearers flexibility in walkingBiomedical Signal Processing and Control, 2022
- Single-channel surface electromyography signal classification with variational mode decomposition and entropy feature for lower limb movements recognitionBiomedical Signal Processing and Control, 2022
- Recognition of lower limb movements using empirical mode decomposition and k-nearest neighbor entropy estimator with surface electromyogram signalsBiomedical Signal Processing and Control, 2022
- Voting-based 1D CNN model for human lower limb activity recognition using sEMG signalPhysical and Engineering Sciences in Medicine, 2021
- Design of upper limb prosthesis using real-time motion detection method based on EMG signal processingBiomedical Signal Processing and Control, 2021
- Evaluation of classification performance in human lower limb jump phases of signal correlation information and LSTM modelsBiomedical Signal Processing and Control, 2021
- Fusion of EEG and EMG signals for classification of unilateral foot movementsBiomedical Signal Processing and Control, 2020
- ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design2018
- Identity Mappings in Deep Residual Networks2016