Elbow movement estimation based on EMG with NARX Neural Networks

Suplino, L. O.; Melo, Gabriel Chaves de; Umemura, Guilherme Silva; Forner-Cordero, Arturo

doi:10.1109/embc44109.2020.9176129

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…The former uses machine learning methods or Hill's musculoskeletal model to build a mapping between handcrafted features and joint angles/torques, which can achieve finer-grained movement prediction. Literature (Suplino et al, 2020 ) proposed an elbow joint angle estimation model based on a non-linear autoregressive with exogenous inputs neural network. This model can accurately predict the elbow joint's torque and angle during flexion and extension movement, with a mean square error within 7°.…”

Section: Related Workmentioning

confidence: 99%

MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram

et al. 2021

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The human–robot interface (HRI) based on biological signals can realize the natural interaction between human and robot. It has been widely used in exoskeleton robots recently to help predict the wearer's movement. Surface electromyography (sEMG)-based HRI has mature applications on the exoskeleton. However, the sEMG signals of paraplegic patients' lower limbs are weak, which means that most HRI based on lower limb sEMG signals cannot be applied to the exoskeleton. Few studies have explored the possibility of using upper limb sEMG signals to predict lower limb movement. In addition, most HRIs do not consider the contribution and synergy of sEMG signal channels. This paper proposes a human–exoskeleton interface based on upper limb sEMG signals to predict lower limb movements of paraplegic patients. The interface constructs an channel synergy-based network (MCSNet) to extract the contribution and synergy of different feature channels. An sEMG data acquisition experiment is designed to verify the effectiveness of MCSNet. The experimental results show that our method has a good movement prediction performance in both within-subject and cross-subject situations, reaching an accuracy of 94.51 and 80.75%, respectively. Furthermore, feature visualization and model ablation analysis show that the features extracted by MCSNet are physiologically interpretable.

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Section: Related Workmentioning

confidence: 99%

MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram

et al. 2021

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“…On the other hand, NARX models are different from other neural networks such that the model outputs as input for future predictions act in the form of feedback. [ 38 ]…”

Section: Introductionmentioning

confidence: 99%

Prediction of Biceps Muscle Electromyogram Signal Using a NARX Neural Network

Rahatabad¹,

Khodadadi²,

Sheikhani³

et al. 2023

Journal of Medical Signals &Amp; Sensors

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Background: This study was conducted to compare the response between the results of experimental data and the results achieved by the NARX neural network model to predict the electromyogram (EMG) signal on the biceps muscle in nonlinear stimulation conditions as a new stimulation model. Methods: This model is applied to design the controllers based on functional electrical stimulation (FES). To this end, the study was conducted in five stages, including skin preparation, placement of recording and stimulation electrodes, along with the position of the person to apply the stimulation signal and recording EMG, stimulation and recording of single-channel EMG signal, signal preprocessing, and training and validation of the NARX neural network. The electrical stimulation applied in this study is based on a chaotic equation derived from the Rossler equation and on the musculocutaneous nerve, and the response to this stimulation, i.e., the EMG signal, is from the biceps muscle as a single channel. The NARX neural network was trained, along with the stimulation signal and the response of each stimulation for 100 recorded signals from 10 individuals, and then validated and retested for trained data and new data after processing and synchronizing both signals. Results: The results indicate that the Rossler equation can create nonlinear and unpredictable conditions for the muscle, and we also can predict the EMG signal with the NARX neural network as a predictive model. Conclusion: The proposed model appears to be a good method to predict control models based on FES and to diagnose some diseases.

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Simultaneous and Continuous Motion Estimation of Upper Limb Based on SEMG and LSTM

Ruan

Chen

et al. 2021

Intelligent Robotics and Applications

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Elbow movement estimation based on EMG with NARX Neural Networks

Cited by 3 publications

References 21 publications

MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram

MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram

Prediction of Biceps Muscle Electromyogram Signal Using a NARX Neural Network

Simultaneous and Continuous Motion Estimation of Upper Limb Based on SEMG and LSTM

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