Efficient Codes for Inverse Dynamics During Walking

Efficient codes have been used effectively in both computer science and neuroscience to better understand the information processing in visual and auditory encoding and discrimination tasks. In this paper, we explore the use of efficient codes for representing information relevant to human movements during locomotion. Specifically, we apply motion capture data to a physical model of the human skeleton to compute joint angles (inverse kinematics) and joint torques (inverse dynamics); then, by treating the resulting paired dataset as a supervised regression problem, we investigate the effect of sparsity in mapping from angles to torques. The results of our investigation suggest that sparse codes can indeed represent salient features of both the kinematic and dynamic views of human locomotion movements. However, sparsity appears to be only one parameter in building a model of inverse dynamics; we also show that the "encoding" process benefits significantly by integrating with the "regression" process for this task. In addition, we show that, for this task, simple coding and decoding methods are not sufficient to model the extremely complex inverse dynamics mapping. Finally, we use our results to argue that representations of movement are critical to modeling and understanding these movements.