How does the CNS control arm reaching movements? Introducing a hierarchical nonlinear predictive control organization based on the idea of muscle synergies

Abstract

In this study, we introduce a hierarchical and modular computational model to explain how the CNS (Central Nervous System) controls arm reaching movement (ARM) in the frontal plane and under different conditions. The proposed hierarchical organization was established at three levels: 1) motor planning, 2) command production, and 3) motor execution. Since in this work we are not discussing motion learning, no learning procedure was considered in the model. Previous models mainly assume that the motor planning level produces the desired trajectories of the joints and feeds it to the next level to be tracked. In the proposed model, the motion control is described based on a regulatory control policy, that is, the output of the motor planning level is a step function defining the initial and final desired position of the hand. For the command production level, a nonlinear predictive model was developed to explain how the time-invariant muscle synergies (MSs) are recruited. We used the same computational model to explain the arm reaching motion for a combined ARM task. The combined ARM is defined as two successive ARM such that it starts from point A and reaches to point C via point B. To develop the model, kinematic and kinetic data from six subjects were recorded and analyzed during ARM task performance. The subjects used a robotic manipulator while moving their hand in the frontal plane. The EMG data of 15 muscles were also recorded. The MSs used in the model were extracted from the recorded EMG data. The proposed model explains two aspects of the motor control system by a novel computational approach: 1) the CNS reduces the dimension of the control space using the notion of MSs and thereby, avoids immense computational loads; 2) at the level of motor planning, the CNS generates the desired position of the hand at the starting, via and the final points, and this amounts to a regulatory and non-tracking structure.

The costs of positon, velocity and force requirements in optimal control induce triphasic muscle activation during reaching movement

“…Dehghani and Bahrami suggested that arm movements are planned with some principle patterns of muscle synergies, and the plans are divided into few phases to reduce the dimension of the control space 43,44 . Also, Sakaguchi et al have proposed a computational model that brain adaptively divides the continuous-time axis into discrete segments and executes feedforward control in each segment to allow sensorimotor delays 45 .…”

Model-Based Mid-Level Regulation for Assist-As-Needed Hierarchical Control of Wearable Robots: A Computational Study of Human-Robot Adaptation

“…τ act and τ h are the input activation torque and the total human joint torque as output, respectively. As mentioned earlier, the CNS coordinates human arm motion by complex commands, which are the mixture of (I) the motion prediction and (II) the corrective command [4,33,34]. (I) the motion prediction or feed-forward control is calculated from an internal model or representation of the complex system.…”

“…The human CNS, involving the brain and the spinal cord, controls the human body’s motion [ 4 , 33 , 34 ]. It concurrently manages the kinetics and the kinematics, despite uncertain/unknown trajectories and complicated muscle dynamics [ 4 , 35 ].…”

Identifying the Effects of Age and Speed on Whole-Body Gait Symmetry by Using a Single Wearable Sensor

“…These authors, studying locomotion in the cat, identified neurons in the cerebellum whose neuronal discharge was associated with the global kinematics of different walking patterns, suggesting that this activity serves as a basis for the dynamics and the kinematics of individual segments of the lower limbs. In humans, such a process has been extensively studied for reaching and grasping movements where the activity of each muscle and the rotation of single joints, are encoded from a global representation of the trajectory along which the hand moves to reach a position and grasp an object [36,37]. A homologous model can be applied to human locomotion, with trunk and lower limb segments acting to ensure the correct body transfer from one position to another.…”