A comparison of neural networks algorithms for EEG and sEMG features based gait phases recognition

Wei, Pengna; Zhang, Jinhua; Tian, Feifei; Hong, Jun

doi:10.1016/j.bspc.2021.102587

Cited by 26 publications

(10 citation statements)

References 24 publications

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“…The sEMG signal can reflect the activity state of the muscle during exercise to a certain extent (Wang et al, 2019 ; Chai et al, 2021 ; Wei et al, 2021 ). Through the corresponding time and frequency domain analysis, the time and frequency domain characteristics and the corresponding muscle characteristics and movement correlation can be obtained, and the muscle function state of the human body during exercise can be obtained.…”

Section: Active Movement Intention Recognition For Upper Limbsmentioning

confidence: 99%

A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals

et al. 2022

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The use of upper limb rehabilitation robots to assist the affected limbs for active rehabilitation training is an inevitable trend in the field of rehabilitation medicine. In particular, the active motion intention-based control of the upper limb rehabilitation robots to assist subjects in rehabilitation training is a hot research topic in human-computer interaction control. Therefore, improving the accuracy of active motion intention recognition is the premise of the human-machine interaction controller design. Furthermore, there are external disturbances (bounded/unbounded disturbances) during rehabilitation training, which seriously threaten the safety of subjects. Thereby, eliminating external disturbances (especially unbounded disturbances) is the difficulty and key to the human-machine interaction control of the upper limb rehabilitation robots. In response to these problems, based on the surface electromyogram signal of the human upper limb, this paper proposes a fuzzy neural network active motion intention recognition method to explore the internal connection between the surface electromyogram signal of the human upper limb and active motion intention, and improve the real-time and accuracy of recognition. Based on this, two types of human-machine interaction controllers, which can be called as zeroing neural network controller and noise-suppressing zeroing neural network controller are designed to establish a safe and comfortable training environment to avoid secondary damage to the affected limb. Numerical experiments verify the feasibility and effectiveness of the proposed theories and methods.

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Section: Active Movement Intention Recognition For Upper Limbsmentioning

confidence: 99%

A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals

et al. 2022

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“…Qian et al [13] proposed a gait phase estimation method based on an adaptive oscillator, which can accurately estimate the gait phase of users when they move on various terrain. Wei et al [14] used SEMG and EEG to compare the performance of linear discriminant analysis (LDA), KNN, and a kernel support vector machine (KSVM). Qin et al [15] proposed a human gait phase recognition algorithm based on fuzzy theory to identify the gait phase at the next moment of human lower limb movement.…”

Section: Introductionmentioning

confidence: 99%

Gait Phase Classification of Lower Limb Exoskeleton Based on a Compound Network Model

Xia

Yang

et al. 2023

Symmetry

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The classification of lower limb gait phase is very important for the control of exoskeleton robots. In order to enable the exoskeleton to determine gait phase and provide appropriate assistance to the wearer, we propose a compound network based on CNN-BiLSTM. The method uses data from inertial measurement units placed on the leg and pressure sensor arrays placed on the sole as inputs to the model. The convolutional neural network (CNN) is used to obtain the local key features of gait data, and then the bidirectional long short-term memory (BiLSTM) network is used to extract the serialized gait phase information from the local key features to obtain the high-level feature expression. Finally, the seven phases of both feet were obtained through the classification of the softmax layer. We designed a gait acquisition system and collected the gait data from seven subjects at varying walking speeds. In the test set, the highest gait phase classification accuracy can reach 95.09%. We compared the proposed model with the long short-term memory (LSTM) network and gated recurrent unit (GRU) network. The experimental results show that the average accuracy of CNN-BiLSTM network from seven subjects is 0.417% higher than that of the LSTM network and 0.596% higher than that of the GRU network. Therefore, the ability of the CNN-BiLSTM network to classify gait phases can be applied in designing exoskeleton controllers that can better assist for different gait phases correctly to assist the wearer to walk.

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“…With the development of signal detection technology, it has become a highly concerned research via sEMG signals to evaluate the activity of neuromuscular in recent years [1][2][3]. The sEMG is a comprehensive effect of superficial muscle and nerve trunk electrical activity on skin surface, which contains a wealth of information about human behavior.…”

Section: Introductionmentioning

confidence: 99%

A neural network-based model for lower limb continuous estimation against the disturbance of uncertainty

Liu

Sun

et al. 2022

Biomedical Signal Processing and Control

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A comparison of neural networks algorithms for EEG and sEMG features based gait phases recognition

Cited by 26 publications

References 24 publications

A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals

A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals

Gait Phase Classification of Lower Limb Exoskeleton Based on a Compound Network Model

A neural network-based model for lower limb continuous estimation against the disturbance of uncertainty

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