Artificial neural network model of the mapping between electromyographic activation and trajectory patterns in free-arm movements
Artificial neural networks (ANNs) have been used to identify the relationship between electromyographic (EMG) activity and arm kinematics during the execution of motor tasks. Although considerable work has been devoted to showing that ANNs perform this mapping, there has been little work to explore any relationship with physiological properties of the neuromuscular systems. A back-propagation through time (BPTT) ANN was used to map the EMG of five selected muscles (pectoralis major (PM), anterior deltoid (AD), posterior deltoid (PD), biceps brachii (BB) and triceps brachii (TB)) on arm kinematics in seven normal subjects performing three-dimensional unrestrained grasping movements. To investigate the physiological validity of the BPTT-ANN, inputs were artificially altered, and the predicted outputs were analysed. Results show that the BPTT-ANN performed the mapping correctly (root mean square (RMS) error between target and predicted outputs averaged across subject test sets was 0.092 +/- 0.015). Moreover, it provided insights into the roles of muscles in performing the movement (average indexes measuring the output alteration with respect to the target were 0.070 +/- 0.027, 0.356 +/- 0.172, 0.568 +/- 0.413, 0.510 +/- 0.268, 0.681 +/- 0.430 for PM, AD, PD, BB, TB, respectively, in the movement forward phase, and 0.077 +/- 0.015, 0.179 +/- 0.147, 0.291 +/- 0.247, 0.671 +/- 0.054, 0.232 +/- 0.097 in the return phase).
three-dimensional movements upper limb emg-kinematics relationship motion analysis recurrent anns system identification