  A long-standing hypothesis in neuroscience is that the central nervous system
accomplishes complex motor behaviors through the combination of a small number
of motor primitives. Many studies in the last couples of decades have
identified motor primitives at the kinematic, kinetic, and electromyographic
level, thus supporting modularity at different levels of organization in the
motor system. However, these studies relied on heterogeneous definitions of
motor primitives and on different algorithms for their identification. Standard
unsupervised learning algorithms such as principal component analysis,
independent component analysis, and non-negative matrix factorization, or more
advanced techniques involving the estimation of temporal delays of the relevant
mixture components have been applied. This plurality of algorithms has made
difficult to compare and interpret results obtained across different studies.
Moreover, how the different definitions of motor primitives relate to each
other has never been examined systematically. Here we propose a comprehensive
framework for the definition of different types of motor primitives and a
single algorithm for their identification. By embedding smoothness priors and
specific constraints in the underlying generative model, the algorithm can
identify many different types of motor primitives. We assessed the
identification performance of the algorithm both on simulated data sets, for
which the properties of the primitives and of the corresponding combination
parameters were known, and on experimental electromyographic and kinematic data
sets, collected from human subjects accomplishing goal-oriented and rhythmic
motor tasks. The identification accuracy of the new algorithm was typically
equal or better than the accuracy of other unsupervised learning algorithms
used previously for the identification of the same types of primitives.
