title: Real-time estimation of FES-induced joint torque with evoked EMG : Application to spinal cord injured patients.
abstract: Functional electrical stimulation (FES) is a neuroprosthetic technique for restoring lost motor function of spinal cord injured (SCI) patients and motor-impaired subjects by delivering short electrical pulses to their paralyzed muscles or motor nerves. FES induces action potentials respectively on muscles or nerves so that muscle activity can be characterized by the synchronous recruitment of motor units with its compound electromyography (EMG) signal is called M-wave. The recorded evoked EMG (eEMG) can be employed to predict the resultant joint torque, and modeling of FES-induced joint torque based on eEMG is an essential step to provide necessary prediction of the expected muscle response before achieving accurate joint torque control by FES.
  
  
  
  Previous works on FES-induced torque tracking issues were mainly based on offline analysis. However, toward personalized clinical rehabilitation applications, real-time FES systems are essentially required considering the subject-specific muscle responses against electrical stimulation. This paper proposes a wireless portable stimulator used for estimating/predicting joint torque based on real time processing of eEMG. Kalman filter and recurrent neural network (RNN) are embedded into the real-time FES system for identification and estimation.
  
  
  
  Prediction results on 3 able-bodied subjects and 3 SCI patients demonstrate promising performances. As estimators, both Kalman filter and RNN approaches show clinically feasible results on estimation/prediction of joint torque with eEMG signals only, moreover RNN requires less computational requirement.
  
  
  
  The proposed real-time FES system establishes a platform for estimating and assessing the mechanical output, the electromyographic recordings and associated models. It will contribute to open a new modality for personalized portable neuroprosthetic control toward consolidated personal healthcare for motor-impaired patients.
authors.affiliation: INRIA, University of Montpellier, 860 rue St Priest, Montpellier Cedex 5, 34095, France.|School of Automation Engineering, University of Electronic Science and Technology of China, Xiyuan Ave 2006, Chengdu, 611731, China.
authors.forename: Zhan
authors.initials: Z
authors.lastname: Li
authors.affiliation: INRIA, University of Montpellier, 860 rue St Priest, Montpellier Cedex 5, 34095, France.
authors.forename: David
authors.initials: D
authors.lastname: Guiraud
authors.affiliation: INRIA, University of Montpellier, 860 rue St Priest, Montpellier Cedex 5, 34095, France.
authors.forename: David
authors.initials: D
authors.lastname: Andreu
authors.affiliation: INRIA, University of Montpellier, 860 rue St Priest, Montpellier Cedex 5, 34095, France.|Ecole Nationale d'Ingénieur de Tarbes, Tarbes, France.
authors.forename: Mourad
authors.initials: M
authors.lastname: Benoussaad
authors.affiliation: Centre Neurologique Mutualiste Propara [now with CRF COS DIVIO (Dijon)], Montpellier, 34090, France.
authors.forename: Charles
authors.initials: C
authors.lastname: Fattal
authors.affiliation: INRIA, University of Montpellier, 860 rue St Priest, Montpellier Cedex 5, 34095, France. mitsuhiro.hayashibe@inria.fr.
authors.forename: Mitsuhiro
authors.initials: M
authors.lastname: Hayashibe
mesh_terms: D004599:Electric Stimulation Therapy / Q000379:methods*; D004576:Electromyography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D018482:Muscle, Skeletal / Q000503:physiopathology; D016571:Neural Networks, Computer; D013119:Spinal Cord Injuries / Q000534:rehabilitation*; D019415:Torque
keywords: Evoked electromyography (eEMG); Functional electrical stimulation (FES); Joint torque; Spinal cord injured (SCI)