Real-time estimation of FES-induced joint torque with evoked EMG

Zhan Li (1) , David Guiraud (1) , David Andreu (2) , Mourad Benoussaad (3) , Charles Fattal (4) , Mitsuhiro Hayashibe (1)

1 CAMIN - Control of Artificial Movement and Intuitive Neuroprosthesis
2 EXPLORE - Robotique mobile pour l'exploration de l'environnement
3 IWR - Interdisciplinary Center for Scientific Computing
4 CRF COS Divio - Centre de Rééducation Fonctionnelle Divio [Dijon]

BackgroundFunctional 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.MethodsPrevious 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.ResultsPrediction 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.ConclusionThe 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.

Mots clés: SCI; FES; Joint torque; Functional electrical stimulation; Evoked electromyography (eEMG); Spinal cord injury

Domaines: Sciences du Vivant [q-bio]

Zhan Li, David Guiraud, David Andreu, Mourad Benoussaad, Charles Fattal, et al.. Real-time estimation of FES-induced joint torque with evoked EMG: Application to spinal cord injured patients. Journal of NeuroEngineering and Rehabilitation, 2016, 13 (1), pp.60-71. ⟨10.1186/s12984-016-0169-y⟩. ⟨hal-01336280⟩