MODEM: a multi-agent hierarchical structure to model the human motor control system

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

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.

Trajectory of human movement during sit to stand: a new modeling approach based on movement decomposition and multi-phase cost function

The purpose of this work is to develop a computational model to describe the task of sit to stand (STS). STS is an important movement skill which is frequently performed in human daily activities, but has rarely been studied from the perspective of optimization principles. In this study, we compared the recorded trajectories of STS with the trajectories generated by several conventional optimization-based models (i.e., minimum torque, minimum torque change and kinetic energy cost models) and also with the trajectories produced by a novel multi-phase cost model (MPCM). In the MPCM, we suggested that any complex task, such as STS, is decomposable into successive motion phases, so that each phase requires a distinct strategy to be performed. In this way, we proposed a multi-phase cost function to describe the STS task. The results revealed that the conventional optimization-based models failed to correctly predict the invariable features of STS, such as hip flexion and ankle dorsiflexion movements. However, the MPCM not only predicted the general features of STS with a sufficient accuracy, but also showed a potential flexibility to distinguish between the movement strategies from one subject to the other. According to the results, it seems plausible to hypothesize that the central nervous system might apply different strategies when planning different phases of a complex task. The application areas of the proposed model could be generating optimized trajectories of STS for clinical applications (such as functional electrical stimulation) or providing clinical and engineering insights to develop more efficient rehabilitation devices and protocols.

COMAP: a new computational interpretation of human movement planning level based on coordinated minimum angle jerk policies and six universal movement elements

Flash and Hogan (1985) suggested that the CNS employs a minimum jerk strategy when planning any given movement. Later, Nakano et al. (1999) showed that minimum angle jerk predicts the actual arm trajectory curvature better than the minimum jerk model. Friedman and Flash (2009) confirmed this claim. Besides the behavioral support that we will discuss, we will show that this model allows simplicity in planning any given movement. In particular, we prove mathematically that each movement that satisfies the minimum joint angle jerk condition is reproducible by a linear combination of six functions. These functions are calculated independent of the type of the movement and are normalized in the time domain. Hence, we call these six universal functions the Movement Elements (ME). We also show that the kinematic information at the beginning and end of the movement determines the coefficients of the linear combination. On the other hand, in analyzing recorded data from sit-to-stand (STS) transfer, arm-reaching movement (ARM) and gait, we observed that minimum joint angle jerk condition is satisfied only during different successive phases of these movements and not for the entire movement. Driven by these observations, we assumed that any given ballistic movement may be decomposed into several successive phases without overlap, such that for each phase the minimum joint angle jerk condition is satisfied. At the boundaries of each phase the angular acceleration of each joint should obtain its extremum (zero third derivative). As a consequence, joint angles at each phase will be linear combinations of the introduced MEs. Coefficients of the linear combination at each phase are the values of the joint kinematics at the boundaries of that phase. Finally, we conclude that these observations may constitute the basis of a computational interpretation, put differently, of the strategy used by the Central Nervous System (CNS) for motor planning. We call this possible interpretation "Coordinated Minimum Angle jerk Policy" or COMAP. Based on this policy, the function of the CNS in generating the desired pattern of any given task (like STS, ARM or gait) can be described computationally using three factors: (1) the kinematics of the motor system at given body states, i.e., at certain movement events/instances, (2) the time length of each phase, and (3) the proposed MEs. From a computational point of view, this model significantly simplifies the processes of movement planning as well as feature abstraction for saving characterizing information of any given movement in memory.