Estimation of the Torques Produced by Human Upper Limb during Eating Activities Using NARX-NN

Upper limb movement disorders significantly hamper the ability of impaired to perform basic activities of daily living (ADL). Eating, without doubt, is one of the essential ADLs necessary for human survival. To develop a rehabilitation system meant specifically to assist the hand during eating, an in-depth knowledge of hand motion and the forces/torques produced, during eating is vital. Since, Human Upper Limb (HUL) motion is highly dexterous, its dynamic model can be beneficial for predicting the torques during different eating activities. Four degrees of freedom (DOF), dynamic model of HUL including wrist and elbow joints, focusing on elbow and wrist flexion/extension, forearm pronation/supination, wrist flexion/extension and wrist adduction/abduction is formulated, using Nonlinear AutoRegressive network with eXogenous input Neural Network (NARX-NN), during different eating activities. We conducted an experimental validation involving five different food types and using two types of cutleries. Torque prediction accuracy of the model is determined by comparing predicted values to that of measured load cell torques, for all eating activities and using Root mean square error (RMSE) as a statistical measure, to test the model performance. Torques predicted by the model track the measured torque efficiently.