Planar Reaching Movement Generation Using Submovement Prediction Model

The correct prediction of the optimal motor trajectory is necessary for movement rehabilitation and control systems such as functional electrical stimulation and robotic therapy. It seems that human reaching movements are composed of a set of submovements, each of which is a correction of the overall movement trajectory. Therefore, it is possible to interpret complex movements, learning, adaptability and other features of the motion control system using submovements. The purpose of this study was to predict and generate planar reaching movements using a realistic model similar to the actual mechanism of human movement and based on the submovement.The data used consists of different replications of four types of planar movement Performed by three healthy subjects. After the preprocessing and phasing, the movements decomposed to minimum-jerk submovement. In the next step, the training of three distinct neural networks was carried out to learn the submovement parameters including the amplitude, duration, and initiation time. Finally, the ANNs were combined to form a closed-loop model that generated accurate reaching movements based on the error correction. The target access rate for all predicted movements by the closed-loop model was 100%. Also, the mean distance to the target, the VAF, and the mean MSE error between the predicted and main movement trajectory showed that the predicted movements are a good approximation of the main movements. The results showed that when trained neural networks with submovements, were placed in a closed-loop model, they were able to predict proper submovements for complete access to targets due to the compensation of propagated errors from the previous steps. The results of this study can be used to improve motor rehabilitation methods.