Prediction of EMG Activation Profiles from Gait Kinematics and Kinetics during Multiple Terrains

Zabre-Gonzalez, Erika V; Amieva-Alvarado, Diego; Beardsley, Scott A.

doi:10.1109/embc46164.2021.9630067

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…The current findings are also in agreement with the study reported by Zabre-Gonzalez et al [ 11 ]. Their study proposed using a NARX neural network and kinematics data derived from the motion capture system to estimate the muscle activity of two muscles.…”

Section: Discussionsupporting

confidence: 94%

“…In [ 10 ], musculoskeletal modelling and a simulation tool was used to estimate muscle activity. In [ 11 ], a feedforward nonlinear autoregressive model with exogenous (NARX) and joint kinetics and kinematics data were proposed to estimate gastrocnemius and tibialis anterior muscle activities. Both studies demonstrated the feasibility of using joint kinematics and kinetics to estimate muscle behaviour.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Lower Extremity Muscle Activity in Gait Using the Wearable Inertial Measurement Units and Neural Network

Khant

Gouwanda

Gopalai

et al. 2023

Sensors

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The inertial measurement unit (IMU) has become more prevalent in gait analysis. However, it can only measure the kinematics of the body segment it is attached to. Muscle behaviour is an important part of gait analysis and provides a more comprehensive overview of gait quality. Muscle behaviour can be estimated using musculoskeletal modelling or measured using an electromyogram (EMG). However, both methods can be tasking and resource intensive. A combination of IMU and neural networks (NN) has the potential to overcome this limitation. Therefore, this study proposes using NN and IMU data to estimate nine lower extremity muscle activities. Two NN were developed and investigated, namely feedforward neural network (FNN) and long short-term memory neural network (LSTM). The results show that, although both networks were able to predict muscle activities well, LSTM outperformed the conventional FNN. This study confirms the feasibility of estimating muscle activity using IMU data and NN. It also indicates the possibility of this method enabling the gait analysis to be performed outside the laboratory environment with a limited number of devices.

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Section: Discussionsupporting

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Estimation of Lower Extremity Muscle Activity in Gait Using the Wearable Inertial Measurement Units and Neural Network

Khant

Gouwanda

Gopalai

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…[ 48 ] However, the neural network is usually applied to model or predict part of EMG signal parameters, such as prediction of torque using the NARX neural network,[ 49 ] prediction of the wrist angle based on the intensity of different loads on the muscle using the genetic algorithm,[ 50 ] prediction of the wrist angle based on the neural network and Kalman filter,[ 51 ] and prediction of the EMG signal from the Gait Kinematics and Kinetics using the NARX neural network. [ 52 ]…”

Section: Discussionmentioning

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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Estimation of electrical muscle activity during gait using inertial measurement units with convolution attention neural network and small-scale dataset

Liang,

Muhammad Rehan Afzal,

Qiao

et al. 2024

Journal of Biomechanics

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Prediction of EMG Activation Profiles from Gait Kinematics and Kinetics during Multiple Terrains

Cited by 4 publications

References 9 publications

Estimation of Lower Extremity Muscle Activity in Gait Using the Wearable Inertial Measurement Units and Neural Network

Estimation of Lower Extremity Muscle Activity in Gait Using the Wearable Inertial Measurement Units and Neural Network

Prediction of Biceps Muscle Electromyogram Signal Using a NARX Neural Network

Estimation of electrical muscle activity during gait using inertial measurement units with convolution attention neural network and small-scale dataset

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