Changes in motor synergies for tracking movement and responses to perturbations depend on task-irrelevant dimension constraints

We investigated the changes in the motor synergies of target-tracking movements of hands and the responses to perturbation when the dimensionalities of target positions were changed. We used uncontrolled manifold (UCM) analyses to quantify the motor synergies. The target was changed from one to two dimensions, and the direction orthogonal to the movement direction was switched from task-irrelevant directions to task-relevant directions. The movement direction was task-relevant in both task conditions. Hence, we evaluated the effects of constraints on the redundant dimensions on movement tracking. Moreover, we could compare the two types of responses to the same directional perturbations in one- and two-dimensional target tasks. In the one-dimensional target task, the perturbation along the movement direction and the orthogonal direction were task-relevant and -irrelevant perturbations, respectively. In the two-dimensional target task, the both perturbations were task-relevant perturbations. The results of the experiments showed that the variabilities of the hand positions in the two-dimensional target-tracking task decreased, but the variances of the joint angles did not significantly change. For the task-irrelevant perturbations, the variances of the joint angles within the UCM that did not affect hand position (UCM component) increased. For the task-relevant perturbations, the UCM component tended to increase when the available UCM was large. These results suggest that humans discriminate whether the perturbations were task-relevant or -irrelevant and then adjust the responses of the joints by utilizing the available UCM

Task and Kinematic Parameters for Upper Limb Stroke Patient: A Review

The development of robotics technology has now been used to assist the rehabilitation therapy process of stroke patients.  This far, the progress of therapy patients has been observed qualitatively and quantitatively with several clinical assessments such as Fuegl Meyer, Barthel Index, Motor Function Index, etc. This paper aims to provide a review of stroke patient progress evaluation measurements using kinematic parameters using elbow and shoulder robotic therapy devices and provide an overview of the types of exercises performed on the robotic therapy interface on the motor and cognitive development of stroke patients. Thirty publications that used kinematic parameters as the basis for assessing the development of stroke patients were included, there were 81 kinematic parameters from all the studies reviewed, based on ICF 53 of which were included in the Body Functions and Structures (BFS) classification, and 28 others were included in the Activities and Participation (AP) classification. Several studies showed a good correlation between the measurement of kinematic parameters and clinical assessment (P0.7; P<0.05)

An Inverse Optimal Control Approach to Explain Human Arm Reaching Control Based on Multiple Internal Models

Human motor control is highly efficient in generating accurate and appropriate motor behavior for a multitude of tasks. This paper examines how kinematic and dynamic properties of the musculoskeletal system are controlled to achieve such efficiency. Even though recent studies have shown that the human motor control relies on multiple models, how the central nervous system (CNS) controls this combination is not fully addressed. In this study, we utilize an Inverse Optimal Control (IOC) framework in order to find the combination of those internal models and how this combination changes for different reaching tasks. We conducted an experiment where participants executed a comprehensive set of free-space reaching motions. The results show that there is a trade-off between kinematics and dynamics based controllers depending on the reaching task. In addition, this trade-off depends on the initial and final arm configurations, which in turn affect the musculoskeletal load to be controlled. Given this insight, we further provide a discomfort metric to demonstrate its influence on the contribution of different inverse internal models. This formulation together with our analysis not only support the multiple internal models (MIMs) hypothesis but also suggest a hierarchical framework for the control of human reaching motions by the CNS

Visual, Motor and Attentional Influences on Proprioceptive Contributions to Perception of Hand Path Rectilinearity during Reaching

We examined how proprioceptive contributions to perception of hand path straightness are influenced by visual, motor and attentional sources of performance variability during horizontal planar reaching. Subjects held the handle of a robot that constrained goal-directed movements of the hand to the paths of controlled curvature. Subjects attempted to detect the presence of hand path curvature during both active (subject driven) and passive (robot driven) movements that either required active muscle force production or not. Subjects were less able to discriminate curved from straight paths when actively reaching for a target versus when the robot moved their hand through the same curved paths. This effect was especially evident during robot-driven movements requiring concurrent activation of lengthening but not shortening muscles. Subjects were less likely to report curvature and were more variable in reporting when movements appeared straight in a novel “visual channel” condition previously shown to block adaptive updating of motor commands in response to deviations from a straight-line hand path. Similarly, compromised performance was obtained when subjects simultaneously performed a distracting secondary task (key pressing with the contralateral hand). The effects compounded when these last two treatments were combined. It is concluded that environmental, intrinsic and attentional factors all impact the ability to detect deviations from a rectilinear hand path during goal-directed movement by decreasing proprioceptive contributions to limb state estimation. In contrast, response variability increased only in experimental conditions thought to impose additional attentional demands on the observer. Implications of these results for perception and other sensorimotor behaviors are discussed

Decoding Imagined 3D Hand Movement Trajectories From EEG: Evidence to Support the Use of Mu, Beta, and Low Gamma Oscillations

Objective: To date, motion trajectory prediction (MTP) of a limb from non-invasive electroencephalography (EEG) has relied, primarily, on band-pass filtered samples of EEG potentials i.e., the potential time-series model. Most MTP studies involve decoding 2D and 3D arm movements i.e., executed arm movements. Decoding of observed or imagined 3D movements has been demonstrated with limited success and only reported in a few studies. MTP studies normally use EEG potentials filtered in the low delta (~1 Hz) band for reconstructing the trajectory of an executed or an imagined/observed movement. In contrast to MTP, multiclass classification based sensorimotor rhythm brain-computer interfaces aim to classify movements using the power spectral density of mu (8–12 Hz) and beta (12–28 Hz) bands.Approach: We investigated if replacing the standard potentials time-series input with a power spectral density based bandpower time-series improves trajectory decoding accuracy of kinesthetically imagined 3D hand movement tasks (i.e., imagined 3D trajectory of the hand joint) and whether imagined 3D hand movements kinematics are encoded also in mu and beta bands. Twelve naïve subjects were asked to generate or imagine generating pointing movements with their right dominant arm to four targets distributed in 3D space in synchrony with an auditory cue (beep).Main results: Using the bandpower time-series based model, the highest decoding accuracy for motor execution was observed in mu and beta bands whilst for imagined movements the low gamma (28–40 Hz) band was also observed to improve decoding accuracy for some subjects. Moreover, for both (executed and imagined) movements, the bandpower time-series model with mu, beta, and low gamma bands produced significantly higher reconstruction accuracy than the commonly used potential time-series model and delta oscillations.Significance: Contrary to many studies that investigated only executed hand movements and recommend using delta oscillations for decoding directional information of a single limb joint, our findings suggest that motor kinematics for imagined movements are reflected mostly in power spectral density of mu, beta and low gamma bands, and that these bands may be most informative for decoding 3D trajectories of imagined limb movements

Conditions for Interference Versus Facilitation During Sequential Sensorimotor Adaptation

We investigated how sensorimotor adaptation acquired during one experimental session influenced the adaptation in a subsequent session. The subjects' task was to track a visual target using a joystick-controlled cursor, while the relationship between joystick and cursor position was manipulated to introduce a sensorimotor discordance. Each subject participated in two sessions, separated by a pause of 2 min to 1 month duration. We found that adaptation was achieved within minutes, and persisted in the memory for at least a month, with only a small decay (experiment A). When the discordances administered in the two sessions were in mutual conflict, we found evidence for task interference (experiment B). However, when the discordances were independent, we found facilitation rather than interference (experiment C); the latter finding could not be explained by the use of an "easier" discordance in the second session (experiment D). We conclude that interference is due to an incompatibility between task requirements, and not to a competition of tasks for short-term memory. We further conclude that the ability to adapt to a sensorimotor discordance

Computational models and motor learning paradigms: Could they provide insights for neuroplasticity after stroke? An overview

Computational approaches for modelling the central nervous system (CNS) aim to develop theories on processes occurring in the brain that allow the transformation of all information needed for the execution of motor acts. Computational models have been proposed in several fields, to interpret not only the CNS functioning, but also its efferent behaviour. Computational model theories can provide insights into neuromuscular and brain function allowing us to reach a deeper understanding of neuroplasticity. Neuroplasticity is the process occurring in the CNS that is able to permanently change both structure and function due to interaction with the external environment. To understand such a complex process several paradigms related to motor learning and computational modeling have been put forward. These paradigms have been explained through several internal model concepts, and supported by neurophysiological and neuroimaging studies. Therefore, it has been possible to make theories about the basis of different learning paradigms according to known computational models. Here we review the computational models and motor learning paradigms used to describe the CNS and neuromuscular functions, as well as their role in the recovery process. These theories have the potential to provide a way to rigorously explain all the potential of CNS learning, providing a basis for future clinical studies