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Probability-based prediction of activity in multiple arm muscles: implications for functional electrical stimulation

  title={Probability-based prediction of activity in multiple arm muscles: implications for functional electrical stimulation},
  author={Anderson, Chad V and Fuglevand, Andrew J},
  journal={Journal of neurophysiology},
  publisher={Am Physiological Soc}

EMGP , FES , Kinematics


Functional electrical stimulation (FES) involves artificial activation of muscles with implanted electrodes to restore motor function in paralyzed individuals. The range of motor behaviors that can be generated by FES, however, is limited to a small set of preprogrammed movements such as hand grasp and release. A broader range of movements has not been implemented because of the substantial difficulty associated with identifying the patterns of muscle stimulation needed to elicit specified movements. To overcome this limitation in controlling FES systems, we used probabilistic methods to estimate the levels of muscle activity in the human arm during a wide range of free movements based on kinematic information of the upper limb. Conditional probability distributions were generated based on hand kinematics and associated surface electromyographic (EMG) signals from 12 arm muscles recorded during a training task involving random movements of the arm in one subject. These distributions were then used to predict in four other subjects the patterns of muscle activity associated with eight different movement tasks. On average, about 40% of the variance in the actual EMG signals could be accounted for in the predicted EMG signals. These results suggest that probabilistic methods ultimately might be used to predict the patterns of muscle stimulation needed to produce a wide array of desired movements in paralyzed individuals with FES

En este trabajo se realiza prediccion de señales EMG para estimular un sistema de FES en la zona del brazo en base a informacion kinemática y entrenado con EMG pero usando un método probabilistico usando distribuciones de probabilidad condicional.

The premise for using a biomechanical model is that a set of equations could be identified that characterizes the relationship between limb kinematics and muscle activity. These equations could then be solved to yield
the patterns of muscle activity associated with some desired
kinematic state. However, 
The purpose of this study was therefore to evaluate the efficacy of a probabilistic, rather than deterministic, method to predict patterns of activity in multiple muscles associated with movements of the upper limb in human subjects. 

Here we show that this method can be used to predict, with good fidelity, the complex patterns of muscular activity arising during free movements of the arm based on hand-trajectory information.

Aqui por la graficas tambien parece que predicen la señal del EMG

Se relaciona en EMGP from kinematic