Automatic synthesis of synergies for control of reaching--hierarchical clustering

In this paper we describe a novel method for determining synergies between joint motions in reaching movements by hierarchical clustering. A set of recorded elbow and shoulder trajectories is used in a learning algorithm to determine the relationships between angular velocities at elbow and shoulder joints. The learning algorithm is based on optimal criteria for obtaining the hierarchy of descriptions of movement trajectories. We show that this method finds complex synergism between optimal joint trajectories for a given set of data and angular velocities at the shoulder and elbow joints. Three other machine learning techniques (ML) are used for comparison with our method of hierarchical clustering of trajectories. These MLs are: (1) radial basis functions (RBF), (2) inductive learning (IL), and (3) adaptive-network-based fuzzy inference system (ANFIS). Better error characteristics were obtained using the method of hierarchical clustering in comparison with the other techniques. The advantage of the method of hierarchical clustering with respect to the other MLs is in integrating the spatial and temporal elements of reaching movements. Determination and analysis of spatio-temporal events of movement trajectories is a useful tool in designing control systems for functional electrical stimulation (FES) assisted manipulation.