Elbow angle generation during activities of daily living using a submovement prediction model

The present study aimed to develop a realistic model for the generation of human activities of daily living (ADL) movements. The angular profiles of the elbow joint during functional ADL tasks such as eating and drinking were generated by a submovement-based closed-loop model. First, the ADL movements recorded from three human participants were broken down into logical phases, and each phase was decomposed into submovement components. Three separate artificial neural networks were trained to learn the submovement parameters and were then incorporated into a closed-loop model with error correction ability. The model was able to predict angular trajectories of human ADL movements with target access rate = 100%, VAF = 98.9%, and NRMSE = 4.7% relative to the actual trajectories. In addition, the model can be used to provide the desired target for practical trajectory planning in rehabilitation systems such as functional electrical stimulation, robot therapy, brain-computer interface, and prosthetic devices.