Abstract
It has been suggested that the human nervous system controls motions in the task (or operational) space. However, little attention has been given to the separation of the control of the task-related and task-irrelevant degrees of freedom.Aim: We investigate how muscle synergies may be used to separately control the task-related and redundant degrees of freedom in a computational model.Approach: We generalize an existing motor control model, and assume that the task and redundant spaces have orthogonal basis vectors. This assumption originates from observations that the human nervous system tightly controls the task-related variables, and leaves the rest uncontrolled. In other words, controlling the variables in one space does not affect the other space; thus, the actuations must be orthogonal in the two spaces. We implemented this assumption in the model by selecting muscle synergies that produce force vectors with orthogonal directions in the task and redundant spaces.Findings: Our experimental results show that the orthogonality assumption performs well in reconstructing the muscle activities from the measured kinematics/dynamics in the task and redundant spaces. Specifically, we found that approximately 70% of the variation in the measured muscle activity can be captured with the orthogonality assumption, while allowing efficient separation of the control in the two spaces.Implications: The developed motor control model is a viable tool in real-time simulations of musculoskeletal systems, as well as model-based control of bio-mechatronic systems, where a computationally efficient representation of the human motion controller is needed.
Highlights
Two of the major complexities associated with the human motor control system are: (1) The number of degrees of freedom in the human body greatly exceeds the minimum number required to finish a task. (2) Each degree of freedom is affected by multiple muscles that need to cooperate in order to perform the movement
The present paper introduces an extension to the motor control framework of Sharif Razavian et al (2019) by proposing how the same framework can be used to control the redundant degrees of freedom alongside the task-related ones
A motor control framework for fast feedback control of complex musculoskeletal systems was previously presented (Sharif Razavian et al, 2019), which was based on the relationship between muscle synergies and the task space
Summary
Two of the major complexities associated with the human motor control system are: (1) The number of degrees of freedom in the human body greatly exceeds the minimum number required to finish a task. (2) Each degree of freedom is affected by multiple muscles that need to cooperate in order to perform the movement. The uncontrolled manifold theory (UCM, Scholz and Schöner, 1999) theorizes that the nervous system actively controls the taskrelated degrees of freedom, and leaves the rest uncontrolled. These observations support the existence of a control mechanism in the task space. There are situations when not just the task-related variables, but all the degrees of freedom need to be actively controlled (e.g., reaching a target with a specific hand orientation). How do these situations fit in the “task space control” theme? We propose a computational framework that can achieve such a selective control scheme
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