Steering disturbance rejection using a physics-based neuromusculoskeletal driver model

Mehrabi

McPhee

2015

Self Cite

This paper presents a new model-based method to define muscle synergies. Unlike the conventional factorization approach, which extracts synergies from electromyographic data, the proposed method employs a biomechanical model and formally defines the synergies as the solution of an optimal control problem. As a result, the number of required synergies is directly related to the dimensions of the operational space. The estimated synergies are posture-dependent, which correlate well with the results of standard factorization methods. Two examples are used to showcase this method: a two-dimensional forearm model, and a three-dimensional driver arm model. It has been shown here that the synergies need to be task-specific (i.e., they are defined for the specific operational spaces: the elbow angle and the steering wheel angle in the two systems). This functional definition of synergies results in a low-dimensional control space, in which every force in the operational space is accurately created by a unique combination of synergies. As such, there is no need for extra criteria (e.g., minimizing effort) in the process of motion control. This approach is motivated by the need for fast and bio-plausible feedback control of musculoskeletal systems, and can have important implications in engineering, motor control, and biomechanics.

Section: Methodsmentioning

confidence: 99%

“…As a more complex example, we have considered a 3D arm model rotating a steering wheel, Mehrabi et al ( 2015a , b , see Figure 1B ). This model consists of four body segments: trunk, upper arm, forearm, and hand.…”

Section: Methodsmentioning

confidence: 99%

A model-based approach to predict muscle synergies using optimization: application to feedback control

Mehrabi

McPhee

2015

Self Cite

“…In hierarchical models (Menegaldo et al, 2006; Guigon et al, 2007; Mehrabi et al, 2015), the computational burden is reduced by separating the computation into two steps. In the first step, an optimal trajectory is generated based on a kinematic criterion; then, in the second step, the muscle sharing problem is solved based on another criterion (kinetic criterion).…”

Section: Discussionmentioning

confidence: 99%

Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control

Mehrabi

Ghannadi

et al. 2017

Self Cite

This article investigates the application of optimal feedback control to trajectory planning in voluntary human arm movements. A nonlinear model predictive controller (NMPC) with a finite prediction horizon was used as the optimal feedback controller to predict the hand trajectory planning and execution of planar reaching tasks. The NMPC is completely predictive, and motion tracking or electromyography data are not required to obtain the limb trajectories. To present this concept, a two degree of freedom musculoskeletal planar arm model actuated by three pairs of antagonist muscles was used to simulate the human arm dynamics. This study is based on the assumption that the nervous system minimizes the muscular effort during goal-directed movements. The effects of prediction horizon length on the trajectory, velocity profile, and muscle activities of a reaching task are presented. The NMPC predictions of the hand trajectory to reach fixed and moving targets are in good agreement with the trajectories found by dynamic optimization and those from experiments. However, the hand velocity and muscle activations predicted by NMPC did not agree as well with experiments or with those found from dynamic optimization.

“…Numerous computational models for the control of musculoskeletal systems have been proposed. Among these, many direct optimization-based models exist (Todorov et al, 2005; Liu and Todorov, 2009; Mehrabi et al, 2015a,b, 2017) that inherently control all the degrees of freedom at all times, and as a result are computationally costly. Another challenge to these optimization methods is the choice of the objective function, which is the topic of inverse optimal control (searching for the correct objective function, Mombaur et al, 2010; Laschowski et al, 2018; Berret et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

On the Relationship Between Muscle Synergies and Redundant Degrees of Freedom in Musculoskeletal Systems

Ghannadi

McPhee

2019

Self Cite

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.