A neural network model for reconstructing EMG signals from eight shoulder muscles: Consequences for rehabilitation robotics and biofeedback

Rittenhouse, D. Matheson; Abdullah, Hussein A.; Runciman, R. John; Basir, Otman

doi:10.1016/j.jbiomech.2005.05.008

Cited by 23 publications

(13 citation statements)

References 22 publications

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“…Here we have shown, as has been demonstrated previously [ 30 – 32 , 19 ], that EMG patterns associated with complex limb movements can be predicted with good fidelity from kinematics using artificial neural networks (ANNs). We have extended those findings here to show that the predictive capabilities of ANNs are retained for movements during which subjects grasp and move objects of varying weights and dimensions if grip force is included in the training of the ANN.…”

Section: Discussionsupporting

confidence: 69%

Prediction of muscle activity during loaded movements of the upper limb

Tibold

Fuglevand

2015

J NeuroEngineering Rehabil

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BackgroundAccurate prediction of electromyographic (EMG) signals associated with a variety of motor behaviors could, in theory, serve as activity templates needed to evoke movements in paralyzed individuals using functional electrical stimulation. Such predictions should encompass complex multi-joint movements and include interactions with objects in the environment.MethodsHere we tested the ability of different artificial neural networks (ANNs) to predict EMG activities of 12 arm muscles while human subjects made free movements of the arm or grasped and moved objects of different weights and dimensions. Inputs to the trained ANNs included hand position, hand orientation, and thumb grip force.ResultsThe ability of ANNs to predict EMG was equally as good for tasks involving interactions with external loads as for unloaded movements. The ANN that yielded the best predictions was a feed-forward network consisting of a single hidden layer of 30 neural elements. For this network, the average coefficient of determination (R2 value) between predicted and actual EMG signals across all nine subjects and 12 muscles during movements that involved episodes of moving objects was 0.43.ConclusionThis reasonable accuracy suggests that ANNs could be used to provide an initial estimate of the complex patterns of muscle stimulation needed to produce a wide array of movements, including those involving object interaction, in paralyzed individuals.

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Section: Discussionsupporting

confidence: 69%

Prediction of muscle activity during loaded movements of the upper limb

Tibold

Fuglevand

2015

J NeuroEngineering Rehabil

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“…Compared with the prediction in "open-loop" form, the closed-loop estimation would provide more accurate results. Additionally, it should be noted that the state-space regression model not only involves the forward prediction of joint-movements from muscle activities; but also involves its inverse process of reconstructing muscle activities by joint-movements, which has been investigated for neurosystem applications by some studies [28], [29].…”

Section: Discussion and Future Workmentioning

confidence: 99%

Continuous Estimation of Human Multi-Joint Angles From sEMG Using a State-Space Model

Ding

Han

Zhao

2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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-Due to the couplings among joint-relative muscles, it is a challenge to accurately estimate continuous multi-joint movements from multi-channel sEMG signals. Traditional approaches always build a nonlinear regression model, such as artificial neural network, to predict the multijoint movement variables using sEMG as inputs. However, the redundant sEMG-data are always not distinguished; the prediction errors cannot be evaluated and corrected online as well. In this work, a correlation-based redundancysegmentation method is proposed to segment the sEMG-vector including redundancy into irredundant and redundant subvectors. Then, a general state-space framework is developed to build the motion model by regarding the irredundant subvector as input and the redundant one as measurement output. With the built statespace motion model, a closed-loop prediction-correction algorithm, i.e., the unscented Kalman filter (UKF), can be employed to estimate the multi-joint angles from sEMG, where the redundant sEMG-data are used to reject model uncertainties. After having fully employed the redundancy, the proposed method can provide accurate and smooth estimation results. Comprehensive experiments are conducted on the multi-joint movements of the upper limb. The maximum RMSE of the estimations obtained by the proposed method is 0.16±0.03, which is significantly less than 0.25±0.06 and 0.27±0.07 (p < 0.05) obtained by common neural networks.

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“…This method can improve the quality and sensitivity of clinical analysis in poststroke robot-assisted therapy. The first therapeutic robot prototype we developed employed electromyography (EMG) measurements as biofeedback for assessing recovery over time [29]. However, after implementing the surface electrode EMG, we found that capturing the same muscles in subsequent training sessions was difficult.…”

Section: Biomechanical Model Developmentmentioning

confidence: 99%

Dynamic biomechanical model for assessing and monitoring robot-assisted upper-limb therapy

Abdullah¹,

Tarry²,

Datta³

et al. 2007

JRRD

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Abstract-This article describes the design, validation, and application of a dynamic biomechanical model that assesses and monitors trajectory, position, orientation, force, and torque generated by upper-limb (UL) movement during robot-assisted therapy. The model consists of two links that represent the upper arm and forearm, with 5 degrees of freedom (DOF) for the shoulder and elbow joints. The model is a useful tool for enhancing the functionality of poststroke robot-assisted UL therapy. The individualized inertial segment parameters were based on anthropometric measurements. The model performed inverse dynamic analysis of UL movements to calculate reaction forces and moments acting about the 3-DOF shoulder and 2-DOF elbow joints. Real-time fused biofeedback of a 6-DOF force sensor and three-dimensional (3-D) pose sensors supported the model validation and application. The force sensor was mounted between the robot manipulator and the subject's wrist, while the 3-D pose sensors were fixed at specific positions on the subject's UL segments. The model input and output parameters were stored in the subject's database, which is part of the rehabilitation information system. We assigned 20 nondisabled subjects three different therapy exercises to test and validate the biomechanical model. We found that when the biomechanical model is taught an exercise, it can accurately predict a subject's actual UL joint angles and torques and confirm that the exercise is isolating the desired movement.

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A neural network model for reconstructing EMG signals from eight shoulder muscles: Consequences for rehabilitation robotics and biofeedback

Cited by 23 publications

References 22 publications

Prediction of muscle activity during loaded movements of the upper limb

Prediction of muscle activity during loaded movements of the upper limb

Continuous Estimation of Human Multi-Joint Angles From sEMG Using a State-Space Model

Dynamic biomechanical model for assessing and monitoring robot-assisted upper-limb therapy

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