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  1. AU="Dehghani, Sedigheh"
  2. AU="Ishibashi, Kenji"
  3. AU="Xu, Yanhua"
  4. AU="Matera, Katarzyna"
  5. AU="Ait-Ouarab, Slimane"
  6. AU="Nicola, Coppede"
  7. AU="Dewitt, John M"
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  9. AU="Tanusha D. Ramdin"
  10. AU="Hao, Zehui"
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  1. Article ; Online: How does the CNS control arm reaching movements? Introducing a hierarchical nonlinear predictive control organization based on the idea of muscle synergies.

    Dehghani, Sedigheh / Bahrami, Fariba

    PloS one

    2020  Volume 15, Issue 2, Page(s) e0228726

    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 ... ...

    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.
    MeSH term(s) Arm/physiology ; Central Nervous System/physiology ; Humans ; Models, Neurological ; Movement/physiology ; Muscle, Skeletal/physiology ; Nonlinear Dynamics
    Language English
    Publishing date 2020-02-05
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0228726
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: 3D human arm reaching movement planning with principal patterns in successive phases.

    Dehghani, Sedigheh / Bahrami, Fariba

    Journal of computational neuroscience

    2020  Volume 48, Issue 3, Page(s) 265–280

    Abstract: There are observations indicating that the central nervous system (CNS) decomposes a movement into several successive sub-movements as an effective strategy to control the motor task. In this study, we propose an algorithm in which, Arm Reaching Movement ...

    Abstract There are observations indicating that the central nervous system (CNS) decomposes a movement into several successive sub-movements as an effective strategy to control the motor task. In this study, we propose an algorithm in which, Arm Reaching Movement (ARM) in 3D space is decomposed into several successive phases using zero joint angle jerk features of the arm kinematic data. The presented decomposition algorithm for 3D motions is, in fact, an improved and generalized version of the decomposition method proposed earlier by Emadi and Bahrami in 2012 for 2D movements. They assumed that the motion is coordinated by minimum jerk characteristics in joint angles space in each phase. However, at the first glance, it seems that in 3D ARM joint angles are not coordinated based on the minimum jerk features. Therefore, we defined a resultant variable in the joint space and showed that one can use its jerk properties together with those of the elbow joint in movement decomposition. We showed that phase borders determined with the proposed algorithm in 3D ARM, are defined with jerk characteristics of ARM's performance variable. We observed the same results in the Sit-to-Stand (STS) movement, too. Thus, based on our results, we suggested that any 3D motion can be decomposed into several phases, such that in each phase a set of principal patterns (PPs) extracted by Principal Component Analysis (PCA) method are linearly recruited to regenerate angle trajectories of each joint. Our results also suggest that the CNS, as the primary policy, may simplify the control of the ARMs by reducing the dimension of the control space. This dimension reduction might be accomplished by decomposing the movement into successive phases in which the movement satisfies the minimum joint angle jerk constraint. Then, in each phase, a set of PPs are recruited in the joint space to regenerate angle trajectory of each joint. Then, the dimension of the control space will be the number of the recruitment coefficients.
    MeSH term(s) Adult ; Algorithms ; Arm/physiology ; Biomechanical Phenomena/physiology ; Humans ; Male ; Models, Neurological ; Movement/physiology ; Psychomotor Performance/physiology ; Young Adult
    Language English
    Publishing date 2020-05-25
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1230659-9
    ISSN 1573-6873 ; 0929-5313
    ISSN (online) 1573-6873
    ISSN 0929-5313
    DOI 10.1007/s10827-020-00749-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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