Inter-Joint Coordination Deficits Revealed in the Decomposition of Endpoint Jerk During Goal-Directed Arm Movement After Stroke

It is well documented that neurological deficits after stroke can disrupt motor control processes that affect the smoothness of reaching movements. The smoothness of hand trajectories during multi-joint reaching depends on shoulder and elbow joint angular velocities and their successive derivatives as well as on the instantaneous arm configuration and its rate of change. Right-handed survivors of unilateral hemiparetic stroke and neurologically-intact control participants held the handle of a two-joint robot and made horizontal planar reaching movements. We decomposed endpoint jerk into components related to shoulder and elbow joint angular velocity, acceleration, and jerk. We observed an abnormal decomposition pattern in the most severely impaired stroke survivors consistent with deficits of inter-joint coordination. We then used numerical simulations of reaching movements to test whether the specific pattern of inter-joint coordination deficits observed experimentally could be explained by either a general increase in motor noise related to weakness or by an impaired ability to compensate for multi-joint interaction torque. Simulation results suggest that observed deficits in movement smoothness after stroke more likely reflect an impaired ability to compensate for multi-joint interaction torques rather than the mere presence of elevated motor noise

Human and robot arm control using the minimum variance principle

Many computational models of human upper limb movement successfully capture some features of human movement, but often lack a compelling biological basis. One that provides such a basis is Harris and Wolpert’s minimum variance model. In this model, the variance of the hand at the end of a movement is minimised, given that the controlling signal is subject to random noise with zero mean and standard deviation proportional to the signal’s amplitude. This criterion offers a consistent explanation for several movement characteristics. This work formulates the minimum variance model into a form suitable for controlling a robot arm. This implementation allows examination of the model properties, specifically its applicability to producing human-like movement. The model is subsequently tested in areas important to studies of human movement and robotics, including reaching, grasping, and action perception. For reaching, experiments show this formulation successfully captures the characteristics of movement, supporting previous results. Reaching is initially performed between two points, but complex trajectories are also investigated through the inclusion of via- points. The addition of a gripper extends the model, allowing production of trajectories for grasping an object. Using the minimum variance principle to derive digit trajectories, a quantitative explanation for the approach of digits to the object surface is provided. These trajectories also exhibit human-like spatial and temporal coordination between hand transport and grip aperture. The model’s predictive ability is further tested in the perception of human demonstrated actions. Through integration with a system that performs perception using its motor system offline, in line with the motor theory of perception, the model is shown to correlate well with data on human perception of movement. These experiments investigate and extend the explanatory and predictive use of the model for human movement, and demonstrate that it can be suitably formulated to produce human-like movement on robot arms.Open acces

The role of noise in sensorimotor control

Goal-directed arm movements show stereotypical trajectories, despite the infinite possible ways to reach a given end point. This thesis examines the hypothesis that this stereotypy arises because movements are optimised to reduce the consequences of signal-dependent noise on the motor command. Both experimental and modelling studies demonstrate that signal-dependent noise arises from the normal behaviour of the muscle and motor neuron pool, and has a particular distribution across muscles of different sizes. Specifically, noise decreases in a systematic fashion with increasing muscle strength and motor unit number. Simulations of obstacle avoidance performance in the presence of signal-dependent noise demonstrate that the optimal trajectory for reaching the target accurately and without collision matches the observed trajectories. Isometric force generation is also shown to have systematic changes in variability with posture, which can be explained by the presence of signal-dependent noise in the muscles of the arm. These results confirm the tested hypothesis and imply that consideration of the statistics of action is crucial to human movement planning. To investigate the importance of feedback in the motor system, the impact of static position on motor excitability was examined using transcranial magnetic stimulation and systematic changes in motor evoked potentials were observed. Force generated at the wrist following stimulation was analysed in terms of different possible movement representations, and the differences between force fields arising from stimulation over the cervical spinal cord and from stimulation over primary motor cortex are determined. These results demonstrate the structured influence of proprioceptive feedback on the human motor system. All the experiments are discussed in relation to current theories describing the control of human movements and the impact of noise in the motor system

Spatial and Temporal Considerations in Vehicle Path Tracking With an Emphasis on Spatial Robustness

This dissertation researches the task and path management of an autonomous vehicle with Ackerman-type steering. The task management problem was approached as a path training operation in which a human operator drives the desired path through an environment. A training trajectory is converted into a series of path segments that are driveable by the autonomous vehicle by first fitting a general path to the dataset. Next, transition segments are added to the general path to match the vehicle velocity and steering angle rate limit. The path management problem has been approached by first deriving a kine- matic model of the vehicle. The time domain model is expressed in the frequency domain and then converted into a spatial frequency domain. Next, a stability crite- rion is derived and used in the synthesis of a spatially-robust path controller

Remembering Forward: Neural Correlates of Memory and Prediction in Human Motor Adaptation

We used functional MR imaging (FMRI), a robotic manipulandum and systems identification techniques to examine neural correlates of predictive compensation for spring-like loads during goal-directed wrist movements in neurologically-intact humans. Although load changed unpredictably from one trial to the next, subjects nevertheless used sensorimotor memories from recent movements to predict and compensate upcoming loads. Prediction enabled subjects to adapt performance so that the task was accomplished with minimum effort. Population analyses of functional images revealed a distributed, bilateral network of cortical and subcortical activity supporting predictive load compensation during visual target capture. Cortical regions – including prefrontal, parietal and hippocampal cortices – exhibited trial-by-trial fluctuations in BOLD signal consistent with the storage and recall of sensorimotor memories or “states” important for spatial working memory. Bilateral activations in associative regions of the striatum demonstrated temporal correlation with the magnitude of kinematic performance error (a signal that could drive reward-optimizing reinforcement learning and the prospective scaling of previously learned motor programs). BOLD signal correlations with load prediction were observed in the cerebellar cortex and red nuclei (consistent with the idea that these structures generate adaptive fusimotor signals facilitating cancelation of expected proprioceptive feedback, as required for conditional feedback adjustments to ongoing motor commands and feedback error learning). Analysis of single subject images revealed that predictive activity was at least as likely to be observed in more than one of these neural systems as in just one. We conclude therefore that motor adaptation is mediated by predictive compensations supported by multiple, distributed, cortical and subcortical structures

Do muscle synergies reduce the dimensionality of behavior?

The muscle synergy hypothesis is an archetype of the notion of Dimensionality Reduction (DR) occurring in the central nervous system due to modular organisation. Towards validating this hypothesis, it is however important to understand if muscle synergies can reduce the state-space dimensionality while suitably achieving task control. In this paper we present a scheme for investigating this reduction, utilising the temporal muscle synergy formulation. Our approach is based on the observation that constraining the control input to a weighted combination of temporal muscle synergies instead constrains the dynamic behaviour of a system in trajectory-specific manner. We compute this constrained reformulation of system dynamics and then use the method of system balancing for quantifying the DR; we term this approach as Trajectory Specific Dimensionality Analysis (TSDA). We then use this method to investigate the consequence of minimisation of this dimensionality for a given task. These methods are tested in simulation on a linear (tethered mass) and a nonlinear (compliant kinematic chain) system; dimensionality of various reaching trajectories is compared when using idealised temporal synergies. We show that as a consequence of this Minimum Dimensional Control (MDC) model, smooth straight-line Cartesian trajectories with bell-shaped velocity profiles are obtained as the solution to reaching tasks in both of the test systems. We also investigate the effect on dimensionality due to adding via-points to a trajectory. The results indicate that a synergy basis and trajectory-specific DR of motor behaviours results from usage of muscle synergy control. The implications of these results for the synergy hypothesis, optimal motor control, developmental skill acquisition and robotics are then discussed

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

In this study, based on behavioral and neurophysiological facts, a new hierarchical multi-agent architecture is proposed to model the human motor control system. Performance of the proposed structure is investigated by simulating the control of sit to stand movement. To develop the model, concepts of mixture of experts, modular structure, and some aspects of equilibrium point hypothesis were brought together. We have called this architecture MODularized Experts Model (MODEM). Human motor system is modeled at the joint torque level and the role of the muscles has been embedded in the function of the joint compliance characteristics. The input to the motor system, i.e., the central command, is the reciprocal command. At the lower level, there are several experts to generate the central command to control the task according to the details of the movement. The number of experts depends on the task to be performed. At the higher level, a "gate selector” block selects the suitable subordinate expert considering the context of the task. Each expert consists of a main controller and a predictor as well as several auxiliary modules. The main controller of an expert learns to control the performance of a given task by generating appropriate central commands under given conditions and/or constraints. The auxiliary modules of this expert learn to scrutinize the generated central command by the main controller. Auxiliary modules increase their intervention to correct the central command if the movement error is increased due to an external disturbance. Each auxiliary module acts autonomously and can be interpreted as an agent. Each agent is responsible for one joint and, therefore, the number of the agents of each expert is equal to the number of joints. Our results indicate that this architecture is robust against external disturbances, signal-dependent noise in sensory information, and changes in the environment. We also discuss the neurophysiological and behavioral basis of the proposed model (MODEM

A Simple and Accurate onset Detection Method for a Measured Bell-shaped Speed Profile

Motor control neuroscientists measure limb trajectories and extract the onset of the movement for a variety of purposes. Such trajectories are often aligned relative to the onset of individual movement before the features of that movement are extracted and their properties are inspected. Onset detection is performed either manually or automatically, typically by selecting a velocity threshold. Here, we present a simple onset detection algorithm that is more accurate than the conventional velocity threshold technique. The proposed method is based on a simple regression and follows the minimum acceleration with constraints model, in which the initial phase of the bell-shaped movement is modeled by a cubic power of the time. We demonstrate the performance of the suggested method and compare it to the velocity threshold technique and to manual onset detection by a group of motor control experts. The database for this comparison consists of simulated minimum jerk trajectories and recorded reaching movements

Longitudinal flying qualities criteria for single-pilot instrument flight operations

Modern estimation and control theory, flight testing, and statistical analysis were used to deduce flying qualities criteria for General Aviation Single Pilot Instrument Flight Rule (SPIFR) operations. The principal concern is that unsatisfactory aircraft dynamic response combined with high navigation/communication workload can produce problems of safety and efficiency. To alleviate these problems. The relative importance of these factors must be determined. This objective was achieved by flying SPIFR tasks with different aircraft dynamic configurations and assessing the effects of such variations under these conditions. The experimental results yielded quantitative indicators of pilot's performance and workload, and for each of them, multivariate regression was applied to evaluate several candidate flying qualities criteria