Human motor control: learning to control a time-varying, nonlinear, many-to-one system

Human motor control has always presented a great challenge to both scientists and engineers. It has presented most of the problems they have found difficult to handle and manipulate, which is a consequence of it being a distributed, nonlinear, time-varying system with multiple degrees of freedom that include redundancy on many levels. In recent years, the fast development of computers and the emergence of the new scientific field of neural computation have enabled consideration of complex, adaptive, parallel architectures in the modeling of human motor-control performance. In this paper, some of the models that have been used in the study of motor control are reviewed, and some open questions are formalized and discussed. The main topics are adaptive and artificial neural-network control, parameter estimation, nonlinear properties of the muscles, and parallelism and redundancy