A Method for Identification of Electrically Stimulated Muscle

Farahat, Waleed A.; Herr, Hugh M.

doi:10.1109/iembs.2005.1615918

Cited by 6 publications

(7 citation statements)

References 18 publications

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“…14. In this study, the proposed estimation method was validated for ankle torque prediction and fatigue tracking using eEMG under isometric condition, it would be promising to verify it in dynamic conditions by introducing a torque-joint angle function to the proposed muscle model as in [25].…”

Section: Discussionmentioning

confidence: 99%

“…It is popularly used to represent highly nonlinear systems, and particularly useful for modeling biomechanical systems, such as stretch reflex EMG signal [23] and electrically-stimulated muscles, relating stimulation to muscle force under isometric conditions [24]. It has been shown to extend to dynamic conditions [25], which is essential for developing stable adaptive controllers for applications in FES. In this study, the MAV of eEMG and FES-induced ankle torque are system input u(t) and output y(t), respectively.…”

Section: A Model Structurementioning

confidence: 99%

“…After 33s or 50s, they were changed linearly (a 1 and μ 21 ) or in steps (a 2 and μ 11 ). The pseudorandom binary sequence (PRBS), which is commonly used in muscle identification [25], was chosen as model input. Model input, output and the a posteriori estimate of the output are shown in Fig.…”

Section: ) Time-variant Parameter Tracking In Simulationmentioning

confidence: 99%

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FES-Induced Torque Prediction With Evoked EMG Sensing for Muscle Fatigue Tracking

Zhang¹,

Hayashibe²,

Fraisse³

et al. 2011

IEEE/ASME Trans. Mechatron.

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This study investigates a torque estimation method for muscle fatigue tracking, using stimulus evoked electromyography (eEMG) in the context of a functional electrical stimulation (FES) rehabilitation system. Although FES is able to effectively restore motor function in spinal cord injured (SCI) individuals, its application is inevitably restricted by muscle fatigue. In addition, the sensory feedback indicating fatigue is missing in such patients. Therefore, torque estimation is essential to provide feedback or feedforward signal for adaptive FES control. In this work, a fatigue-inducing protocol is conducted on five SCI subjects via transcutaneous electrodes under isometric condition, and eEMG signals are collected by surface electrodes. A myoelectrical mechanical muscle model based on the Hammerstein structure with eEMG as model input is employed to capture muscle contraction dynamics. It is demonstrated that the correlation between eEMG and torque is time-varying during muscle fatigue. Compared to conventional fixed-parameter models, the adaptedparameter model shows better torque prediction performance in fatiguing muscles. It motivates us to use a Kalman filter with forgetting factor for estimating the time-varying parameters and for tracking muscle fatigue. The assessment with experimental data reveals that the identified eEMG-to-torque model properly predicts fatiguing muscle behavior. Furthermore, the performance of the time-varying parameter estimation is efficient, suggesting that real-time tracking is feasible with a Kalman filter and driven by eEMG sensing in the application of FES.

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Section: Discussionmentioning

confidence: 99%

Section: A Model Structurementioning

confidence: 99%

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FES-Induced Torque Prediction With Evoked EMG Sensing for Muscle Fatigue Tracking

Zhang¹,

Hayashibe²,

Fraisse³

et al. 2011

IEEE/ASME Trans. Mechatron.

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“…It is also promising to be applied in dynamic condition by multiplying a force-length or force-velocity function to the proposed myoelectrical mechanical muscle model as proposed in [19].…”

Section: Discussion For Fatigue Tracking Controlmentioning

confidence: 99%

Muscle fatigue tracking based on stimulus evoked EMG and adaptive torque prediction

Zhang

Hayashibe

Guiraud

2011

2011 IEEE International Conference on Robotics and Automation

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Functional electrical stimulation (FES) is effective to restore movement in spinal cord injured (SCI) subjects. Unfortunately, muscle fatigue constrains the application of FES so that output torque feedback is interesting for fatigue compensation. Whereas, inadequacy of torque sensors is another challenge for FES control. Torque estimation is thereby essential in fatigue tracking task for practical FES employment. In this work, the Hammstein cascade with electromyography (EMG) as input is applied to model the myoelectrical mechanical behavior of the stimulated muscle. Kalman filter with forgetting factor is presented to estimate the muscle model and track fatigue. Fatigue inducing protocol was conducted on three SCI subjects through surface electrical stimulation. Assessment in simulation and with experimental data reveals that the muscle model properly fits the muscle behavior well. Moreover, the time-varying parameters tracking performance in simulation is efficient such that real time tracking is feasible with Kalman filter. The fatigue tracking with experimental data further demonstrates that the proposed method is suitable for fatigue tracking as well as adaptive torque prediction at different prediction horizons.

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“…In [7], a discrete-time Hammerstein model is used to describe the electrically stimulated muscle under isometric conditions (the muscle is held at a fixed length when stimulated). Later, this work was extended to non-isometric muscle [10], [14] and [13], but the key nonlinear muscle activation is still modeled by a Hammerstein structure with one or two more linear blocks in series [14] [13], or parallel [10]. Using such a model to describe the muscle activation system has the possible advantages of a well defined structure and the possibility of applying a separate identification procedure for each block.…”

Section: Introductionmentioning

confidence: 99%

Identification of electrically stimulated muscle after stroke

Markovsky

Freeman

et al. 2009

2009 European Control Conference (ECC)

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The design of controllers to enable the application of Functional Electrical Stimulation as part of a rehabilitation programme for stroke patients requires an accurate model of electrically stimulated muscle. In this paper, nonlinear dynamics of the electrically stimulated muscle under isometric conditions is investigated, leading to the requirement to identify a Hammerstein model structure. Here we develop a two-stage identification method based on a preliminary construction of the linear part that is used as an initial estimate. Then the two-stage method is applied to identify the nonlinear part and optimize the linear part. The separable least squares optimization algorithm and traditional ramp deconvolution method are implemented here and compared with the proposed method using a simulated muscle system that is based on experimental data from stroke patients. The results show that the proposed method outperforms two other previously proposed methods when implemented on the simulated muscle system with different noise levels.

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A Method for Identification of Electrically Stimulated Muscle

Cited by 6 publications

References 18 publications

FES-Induced Torque Prediction With Evoked EMG Sensing for Muscle Fatigue Tracking

FES-Induced Torque Prediction With Evoked EMG Sensing for Muscle Fatigue Tracking

Muscle fatigue tracking based on stimulus evoked EMG and adaptive torque prediction

Identification of electrically stimulated muscle after stroke

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