Artificial Neural Networks for Analyzing Inter-limb

Motor control research relies on theories, such as coordination dynamics, adapted from physical sciences to explain the emergence of coordinated movement in biological systems. Historically, many studies of coordination have involved inter-limb coordination of relatively few degrees of freedom. Moreover, the majority of experimental studies of coordination also involve continuous cyclic movements. This study had two aims: a) to explain the changes in inter-limb coordination used to perform the golf chip shot at varying distances and b) to ascertain the validity of Self-Organizing Maps (SOMs) as an analysis technique for high-dimensional discrete movement coordination. The experimental setup was specifically chosen to target a gap in the motor control literature in which discrete movements involving coordination of many degrees of freedom are underrepresented. The golf chip shot was chosen as a movement model. Four golfers performed ten chip shots to each of six target distances. 24 kinematic variables were used as input for a SOM in order to compress the data to a low-dimensional mapping. In this study, the trajectory of consecutive best-matching nodes on the output map was used as a collective variable and subsequently fed into a second SOM which was used to create a visualization of coordination stability. The SOM trajectories showed changes in coordination between movement patterns used for short chip shots and movement patterns used for long chip shots. The stability of coordination for Player MW showed a non-linear phase transition from 4 m to 20 m. For Players HI and PB the instability between stable states of coordination was not as clear as it was for Player MW, therefore, the existence of a phase transition for these two players is speculative. Player AW