Identification of the contribution of the ankle and hip joints to multi-segmental balance control

Background\ud \ud Human stance involves multiple segments, including the legs and trunk, and requires coordinated actions of both. A novel method was developed that reliably estimates the contribution of the left and right leg (i.e., the ankle and hip joints) to the balance control of individual subjects. \ud \ud Methods\ud \ud The method was evaluated using simulations of a double-inverted pendulum model and the applicability was demonstrated with an experiment with seven healthy and one Parkinsonian participant. Model simulations indicated that two perturbations are required to reliably estimate the dynamics of a double-inverted pendulum balance control system. In the experiment, two multisine perturbation signals were applied simultaneously. The balance control system dynamic behaviour of the participants was estimated by Frequency Response Functions (FRFs), which relate ankle and hip joint angles to joint torques, using a multivariate closed-loop system identification technique. \ud \ud Results\ud \ud In the model simulations, the FRFs were reliably estimated, also in the presence of realistic levels of noise. In the experiment, the participants responded consistently to the perturbations, indicated by low noise-to-signal ratios of the ankle angle (0.24), hip angle (0.28), ankle torque (0.07), and hip torque (0.33). The developed method could detect that the Parkinson patient controlled his balance asymmetrically, that is, the right ankle and hip joints produced more corrective torque. \ud \ud Conclusion\ud \ud The method allows for a reliable estimate of the multisegmental feedback mechanism that stabilizes stance, of individual participants and of separate leg

EMG feedback tasks reduce reflexive stiffness during force and position perturbations

Force and position perturbations are widely applied to identify muscular and reflexive contributions to posture maintenance of the arm. Both task instruction (force vs. position) and the inherently linked perturbation type (i.e., force perturbations-position task and position perturbations-force tasks) affect these contributions and their mutual balance. The goal of this study is to explore the modulation of muscular and reflexive contributions in shoulder muscles using EMG biofeedback. The EMG biofeedback provides a harmonized task instruction to facilitate the investigation of perturbation type effects irrespective of task instruction. External continuous force and position perturbations with a bandwidth of 0.5–20 Hz were applied at the hand while subjects maintained prescribed constant levels of muscular co-activation using visual feedback of an EMG biofeedback signal. Joint admittance and reflexive impedance were identified in the frequency domain, and parametric identification separated intrinsic muscular and reflexive feedback properties. In tests with EMG biofeedback, perturbation type (position and force) had no effect on joint admittance and reflexive impedance, indicating task as the dominant factor. A reduction in muscular and reflexive stiffness was observed when performing the EMG biofeedback task relative to the position task. Reflexive position feedback was effectively suppressed during the equivalent EMG biofeedback task, while velocity and acceleration feedback were both decreased by approximately 37%. This indicates that force perturbations with position tasks are a more effective paradigm to investigate complete dynamic motor control of the arm, while EMG tasks tend to reduce the reflexive contribution

Modeling movement disorders - CRPS-related dystonia explained by abnormal proprioceptive reflexes

AbstractHumans control their movements using adaptive proprioceptive feedback from muscle afferents. The interaction between proprioceptive reflexes and biomechanical properties of the limb is essential in understanding the etiology of movement disorders. A non-linear neuromuscular model of the wrist incorporating muscle dynamics and neural control was developed to test hypotheses on fixed dystonia. Dystonia entails sustained muscle contractions resulting in abnormal postures. Lack of inhibition is often hypothesized to result in hyperreflexia (exaggerated reflexes), which may cause fixed dystonia. In this study the model-simulated behavior in case of several abnormal reflex settings was compared to the clinical features of dystonia: abnormal posture, sustained muscle contraction, increased stiffness, diminished voluntary control and activity-aggravation.The simulation results were rated to criteria based on characteristic features of dystonia. Three abnormal reflex scenarios were tested: (1) increased reflex sensitivity—increased sensitivity of both the agonistic and antagonistic reflex pathways; (2) imbalanced reflex offset—a static offset to the reflex pathways on the agonistic side only; and (3) imbalanced reflex sensitivity—increased sensitivity of only the agonistic reflex pathways.Increased reflex sensitivity did not fully account for the features of dystonia, despite distinct motor dysfunction, since no abnormal postures occurred. Although imbalanced reflex offset did result in an abnormal posture, it could not satisfy other criteria. Nevertheless, imbalanced reflex sensitivity with unstable force feedback in one of the antagonists closely resembled all features of dystonia. The developed neuromuscular model is an effective tool to test hypotheses on the underlying pathophysiology of movement disorders

Evaluation of EMG, force and joystick as control interfaces for active arm supports

Background:\ud The performance capabilities and limitations of control interfaces for the operation of active movement-assistive devices remain unclear. Selecting an optimal interface for an application requires a thorough understanding of the performance of multiple control interfaces. \ud \ud Methods:\ud In this study the performance of EMG-, force- and joystick-based control interfaces were assessed in healthy volunteers with a screen-based one-dimensional position-tracking task. The participants had to track a target that was moving according to a multisine signal with a bandwidth of 3 Hz. The velocity of the cursor was proportional to the interface signal. The performance of the control interfaces were evaluated in terms of tracking error, gain margin crossover frequency, information transmission rate and effort. \ud \ud Results:\ud None of the evaluated interfaces was superior in all four performance descriptors. The EMG-based interface was superior in tracking error and gain margin crossover frequency compared to the force- and the joystick-based interfaces. The force-based interface provided higher information transmission rate and lower effort than the EMG-based interface. The joystick-based interface did not present any significant difference with the force-based interface for any of the four performance descriptors. We found that significant differences in terms of tracking error and information transmission rate were present beyond 0.9 and 1.4 Hz respectively. \ud \ud Conclusions:\ud Despite the fact that the EMG-based interface is far from the natural way of interacting with the environment, while the force-based interface is closer, the EMG-based interface presented very similar and for some descriptors even a better performance than the force-based interface for frequencies below 1.4 Hz. The classical joystick presented a similar performance to the force-based interface and holds the advantage of being a well established interface for the control of many assistive devices. From these findings we concluded that all the control interfaces considered in this study can be regarded as a candidate interface for the control of an active arm support

Relating reflex gain modulation in posture control to underlying neural network properties using a neuromusculoskeletal model

During posture control, reflexive feedback allows humans to efficiently compensate for unpredictable mechanical disturbances. Although reflexes are involuntary, humans can adapt their reflexive settings to the characteristics of the disturbances. Reflex modulation is commonly studied by determining reflex gains: a set of parameters that quantify the contributions of Ia, Ib and II afferents to mechanical joint behavior. Many mechanisms, like presynaptic inhibition and fusimotor drive, can account for reflex gain modulations. The goal of this study was to investigate the effects of underlying neural and sensory mechanisms on mechanical joint behavior. A neuromusculoskeletal model was built, in which a pair of muscles actuated a limb, while being controlled by a model of 2,298 spiking neurons in six pairs of spinal populations. Identical to experiments, the endpoint of the limb was disturbed with force perturbations. System identification was used to quantify the control behavior with reflex gains. A sensitivity analysis was then performed on the neuromusculoskeletal model, determining the influence of the neural, sensory and synaptic parameters on the joint dynamics. The results showed that the lumped reflex gains positively correlate to their most direct neural substrates: the velocity gain with Ia afferent velocity feedback, the positional gain with muscle stretch over II afferents and the force feedback gain with Ib afferent feedback. However, position feedback and force feedback gains show strong interactions with other neural and sensory properties. These results give important insights in the effects of neural properties on joint dynamics and in the identifiability of reflex gains in experiments