Spatiotemporal modular organization of muscle torques for sit-to-stand movements

The robustness of movement patterns is an essential factor for characterizing the adaptability of our daily motions; however, details of the mechanism underlying adaptive motion patterns are not well understood. Here, we utilized complex principal component analysis (CPCA) to examine the spatiotemporal structure of dynamic muscle torques during sit-to-stand (STS) movements. The motion of a three-link rigid body model in the sagittal plane was captured by a Vicon motion analysis system to compute the kinematics of the center of mass (COM), angular displacement, and joint torques. Using CPCA, dynamic muscle torques were decomposed into three components: a control signal, the phase lags of the joint torques, and weighting coefficients. Two kinetic modules were identified in STS, indicating spatiotemporal modular control of the COM in the horizontal and vertical directions. Simulation results suggested that fine-tuning of these two modules according to environmental conditions contributes to adaptive changes in motion pattern. Taken together, our findings suggest that the sources of behavioral adaptations to the environment include the use of fixed modules to reduce computational load on the central nervous system, fine-tuning of these modules, and control of the temporal signals that activate them.