Estimation of Movement from Surface EMG Signals Using a Neural Network Model

Over the years, the neurophysiology and biomechanics of muscle systems have been investigated quite extensively in order to characterize the relations between muscle activity (EMG) and various dynamical and/or kinematic aspects of the ensuing movement behavior. There have been numerous efforts to correlate the duration, magnitude and timing of phasic EMG bursts with the amplitude, duration, and maximum speed of limb motion (Gottlieb, Corcos, and Agarwal 1989; Brown and Cooke 1990; Karst and Hasan 1991). Although the complexity of musculoskeletal systems has made it difficult to reconstruct movement accurately from EMG signals, this goal is central to efforts to model motor control mechanisms of the central nervous system (CNS) computationally.