A Low-Cost End-to-End sEMG-Based Gait Sub-Phase Recognition System

Luo, Ruiming; Sun, Shouqian; Zhang, Xianfu; Tang, Zhichuan; Wang, Weide

doi:10.1109/tnsre.2019.2950096

Cited by 73 publications

(30 citation statements)

References 40 publications

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“…This characteristic provides the network with memory allowing to encode time dependency within the classification framework (Elman, 1990 networks are recurrent networks capable of learning long-term dependencies in time series without suffering from the vanishing gradient problem (Pascanu et al, 2013). This network was selected since it has already shown to be very effective in decoding gait events from either EEG (Tortora et al, 2020) and EMG (Luo et al, 2019) signals. The implementation of a LSTM network can be found in Hochreiter and Schmidhuber (1997), Gers et al (1999), and Tortora et al (2020) and it is beyond the scope of the paper.…”

Section: Eeg and Emg Long-short Term Memory (Lstm) Networkmentioning

confidence: 99%

“…An EEG and an EMG network were trained for each subject independently with the data from the training set only. The networks' architectures (i.e., number of LSTM layers, number of units per layer), shown in Figure 3A, were empirically defined for EEG and EMG signals separately based on the performance on the validation set, averaged across subjects, and on previous studies in literature (Craik et al, 2019;Luo et al, 2019;Roy et al, 2019;Tortora et al, 2020).…”

Section: Eeg and Emg Long-short Term Memory (Lstm) Networkmentioning

confidence: 99%

“…It can be easily expected that the use of the sole EMG signal is enough to achieve an accurate decoding of gait phases in conditions where the nervous and/or muscoloskeletal systems are not impaired (Luo et al, 2019), with respect to what could be achieved by an EEG-based classifier, thanks to its higher correlation with the movement. However, in order to evaluate the proposed hHMI with the available dataset, containing EEG and EMG of healthy subjects only (see section 2.1), we simulated two different conditions in which the reliability of the EMG is compromised, either temporary (section 2.5.1) or permanently (section 2.5.2).…”

Section: Experimental Validationmentioning

confidence: 99%

“…Electromyography (EMG) is certainly one of the most used techniques to control rehabilitation and assistive devices, particularly in robotic applications. EMG has been already successfully used to decode gait activity (Meng et al, 2010;Joshi et al, 2013;Li et al, 2016;Luo et al, 2019), and to control exoskeletons (Fleischer et al, 2005;Ferris and Lewis, 2009;Takahashi et al, 2015) or neuroprostheses based on functional electrical stimulation (FES) (Graupe et al, 1982;Dutta et al, 2008). However, the usability of EMG-based control strategies strongly depends on the user's residual muscular functions.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers

et al. 2020

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Despite the advances in the field of brain computer interfaces (BCI), the use of the sole electroencephalography (EEG) signal to control walking rehabilitation devices is currently not viable in clinical settings, due to its unreliability. Hybrid interfaces (hHMIs) represent a very recent solution to enhance the performance of single-signal approaches. These are classification approaches that combine multiple human-machine interfaces, normally including at least one BCI with other biosignals, such as the electromyography (EMG). However, their use for the decoding of gait activity is still limited. In this work, we propose and evaluate a hybrid human-machine interface (hHMI) to decode walking phases of both legs from the Bayesian fusion of EEG and EMG signals. The proposed hHMI significantly outperforms its single-signal counterparts, by providing high and stable performance even when the reliability of the muscular activity is compromised temporarily (e.g., fatigue) or permanently (e.g., weakness). Indeed, the hybrid approach shows a smooth degradation of classification performance after temporary EMG alteration, with more than 75% of accuracy at 30% of EMG amplitude, with respect to the EMG classifier whose performance decreases below 60% of accuracy. Moreover, the fusion of EEG and EMG information helps keeping a stable recognition rate of each gait phase of more than 80% independently on the permanent level of EMG degradation. From our study and findings from the literature, we suggest that the use of hybrid interfaces may be the key to enhance the usability of technologies restoring or assisting the locomotion on a wider population of patients in clinical applications and outside the laboratory environment.

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Section: Eeg and Emg Long-short Term Memory (Lstm) Networkmentioning

confidence: 99%

Section: Eeg and Emg Long-short Term Memory (Lstm) Networkmentioning

confidence: 99%

Section: Experimental Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers

et al. 2020

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“…AI Qingsong et al extracted the wavelet coefficients of sEMG, used linear discriminant analysis (LDA), and a support vector machine (SVM) based on the Gaussian kernel function to classify the lower limb motion accuracy higher than 95% [ 34 ]. In recent years, the neural network has been widely used in human complex motion classification because of its powerful nonlinear fitting function [ 35 , 36 , 37 , 38 , 39 ]. Chen Yang et al extracted RMS, WC, and PE features of sEMG signals using the backpropagation neural network, generalized regression neural network, and least square support vector regression (LS-SVR) to predict the knee angle; the root mean square error was less than 7.7°, which can be used in a rehabilitation robot [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Feature Extraction and Classification for Lower Limb Motion Based on sEMG Signal

Qin

Shi

2020

Entropy

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The real-time and accuracy of motion classification plays an essential role for the elderly or frail people in daily activities. This study aims to determine the optimal feature extraction and classification method for the activities of daily living (ADL). In the experiment, we collected surface electromyography (sEMG) signals from thigh semitendinosus, lateral thigh muscle, and calf gastrocnemius of the lower limbs to classify horizontal walking, crossing obstacles, standing up, going down the stairs, and going up the stairs. Firstly, we analyzed 11 feature extraction methods, including time domain, frequency domain, time-frequency domain, and entropy. Additionally, a feature evaluation method was proposed, and the separability of 11 feature extraction algorithms was calculated. Then, combined with 11 feature algorithms, the classification accuracy and time of 55 classification methods were calculated. The results showed that the Gaussian Kernel Linear Discriminant Analysis (GK-LDA) with WAMP had the highest classification accuracy rate (96%), and the calculation time was below 80 ms. In this paper, the quantitative comparative analysis of feature extraction and classification methods was a benefit to the application for the wearable sEMG sensor system in ADL.

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Evaluating Bilateral Surface EMG Features for Automatic Identification of Gait Phase Transitions in Ground Walking Conditions

Di Nardo,

Morbidoni,

Ventura

et al. 2024

IFMBE Proceedings

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A Low-Cost End-to-End sEMG-Based Gait Sub-Phase Recognition System

Cited by 73 publications

References 40 publications

Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers

Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers

Evaluation of Feature Extraction and Classification for Lower Limb Motion Based on sEMG Signal

Evaluating Bilateral Surface EMG Features for Automatic Identification of Gait Phase Transitions in Ground Walking Conditions

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