Preliminary study of online real-time control system for lower extremity exoskeletons based on EEG and sEMG fusion

Li, Wenju; Shao, K.W.; Zhu, Chi; Ma, Yue; Cao, Wujing; Yin, Meng; Yang, Lijun; Luo, Mingxiang; Wang, Xinyu

doi:10.1109/robio55434.2022.10011813

Cited by 2 publications

(1 citation statement)

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“…In [31], the authors designed an HMI integrating EEG and sEMG signals for controlling lower extremity exoskeletons. Through a deep analysis of human walking, they achieved a high level of accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble of Long Short-Term Memory Networks with an Attention Mechanism for Upper Limb Electromyography Signal Classification

Alotaibi,

Jahanshahi,

Yao

et al. 2023

Mathematics

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Advancing cutting-edge techniques to accurately classify electromyography (EMG) signals are of paramount importance given their extensive implications and uses. While recent studies in the literature present promising findings, a significant potential still exists for substantial enhancement. Motivated by this need, our current paper introduces a novel ensemble neural network approach for time series classification, specifically focusing on the classification of upper limb EMG signals. Our proposed technique integrates long short-term memory networks (LSTM) and attention mechanisms, leveraging their capabilities to achieve accurate classification. We provide a thorough explanation of the architecture and methodology, considering the unique characteristics and challenges posed by EMG signals. Furthermore, we outline the preprocessing steps employed to transform raw EMG signals into a suitable format for classification. To evaluate the effectiveness of our proposed technique, we compare its performance with a baseline LSTM classifier. The obtained numerical results demonstrate the superiority of our method. Remarkably, the method we propose attains an average accuracy of 91.5%, with all motion classifications surpassing the 90% threshold.

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Section: Introductionmentioning

confidence: 99%