A model-based exploration of the role of pattern generating circuits during locomotor adaptation

An Analytical Approach to Posture-Dependent Muscle Force and Muscle Activation Patterns

Personalized training by taking into account individual anatomy to improve performance is a research frontier. In this paper, we first introduce an analytical method to study the pattern of changes in muscle forces as a function of posture. Our method is also able to analyze variation of maximal muscle force and muscle activation values (in various postures) as a result of posture-dependent changes in moment arms. This method also helps us evaluate the utility of person specific training. It also provides us with model based approximations for activation and muscle force patterns during different motions without a need for subject recordings, which enables athletes to have a better understanding of how each muscle contributes during each posture, in a fast and efficient way. Second, we analyze the results of this method for a simple squat move. Our results show that both maximal muscle force and muscle activation values have variable sensitivity to the moment arm values for different postures and muscles. It suggests that individually modified training plans could likely improve performance for some sets of movements.

Model-Free Control of Movement in a Tendon-Driven Limb via a Modified Genetic Algorithm

Tendon-driven systems have many benefits over other actuation strategies such as torque-driven systems; however, their over-determined nature and posture-dependent actuation presents strong constraints on their control. Also, parameters or even exact structure of the model in these systems, especially in the biological ones, are normally not clear to the controller. Here, we propose a modified Genetic Algorithm that provides the tendon excursion values for the limb to follow a desired trajectory. Our results show that the proposed algorithm was able to accurately follow the desired trajectory without the model of the system being exposed to it. We believe that this method can enable biologically inspired tendon-driven mechanisms with variable mechanical structures to autonomously control their movements.