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 re corded elbow and shoulder trajectories is used in a learning algorithm to d etermine 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 m ethod finds complex synergism between optimal joint trajectories for a give n set of data and angular velocities at the shoulder and elbow joints. Thre e other machine learning techniques (ML) are used for comparison with our m ethod of hierarchical clustering of trajectories. These MLs are: (1) radial basis functions (RBF), (2) inductive learning (IL), and (3) adaptive-netwo rk-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 w ith respect to the other MLs is in integrating the spatial and temporal ele ments of reaching movements. Determination and analysis of spatio-temporal events of movement trajectories is a useful tool in designing control syste ms for functional electrical stimulation (FES) assisted manipulation. (C) 1 999 IPEM, Published by Elsevier Science Ltd. All rights reserved.