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Torque Prediction Based on Evoked EMG in Fatiguing Muscle Toward Advanced Drop Foot Correction

Qin Zhang 1, 2, * Mitsuhiro Hayashibe 1 Bertrand Sablayrolles 3 Christine Azevedo Coste 1 
* Corresponding author
1 DEMAR - Artificial movement and gait restoration
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Electrical stimulation (ES) has been applied since 1961 for the correction of hemiplegic drop foot. One main drawback of the technique is the occurrence of early fatigue. Therefore, it is essential to predict force generation for precise ES closed loop control when the stimulated muscle becomes fatigued. This work aims to predict ankle torque using stimulus evoked EMG (eEMG) during different muscle fatigue states. Five healthy subjects participated in our study. Conventional stimulation for drop foot correction was applied by surface stimulation in sitting position. The results showed that during long-term stimulation the generated torque gradually declined due to muscle fatigue, the muscle activity (EMG) performed quite differently in different fatigue level. In this work, we carried out the torque prediction with an adapted parameters model according to muscle fatigue state by reidentification using the latest measurement. The prediction was improved with 21%~90.9% comparing to the fixed parameters model. The results revealed a promising approach to use evoked EMG for fatigue compensation in the application of drop foot correction.
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Submitted on : Tuesday, July 13, 2010 - 5:49:25 PM
Last modification on : Friday, August 5, 2022 - 10:47:23 AM
Long-term archiving on: : Thursday, October 14, 2010 - 3:45:24 PM


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  • HAL Id : lirmm-00502335, version 1


Qin Zhang, Mitsuhiro Hayashibe, Bertrand Sablayrolles, Christine Azevedo Coste. Torque Prediction Based on Evoked EMG in Fatiguing Muscle Toward Advanced Drop Foot Correction. FES: Vienna Workshop on Functional Electrical Stimulation, Sep 2010, Vienna, Austria. pp.105-107. ⟨lirmm-00502335⟩



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