274 research outputs found

    Time variant system identification of human limb dynamics using wavelets

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    The dynamic behavior (i.e. admittance) of a human limb results from the interaction between limb inertia, muscles and the central nervous system. System identification techniques assess the dynamic behavior of a limb by analyzing the limb’s response to certain perturbations. Most identification techniques require the system to behave linear and time invariant, i.e. the system’s response to the perturbation must remain unchanged during observation. However it is known that neuromuscular properties change for example with fatigue. Furthermore it has been found that the strength of afferent feedback (e.g. from muscle spindles and Golgi tendon organs) adapts to conditions like task instruction and mechanical load. So far, research mainly focused on the the steady state behavior after the system had been adapted but not on the adaptation process itself. In this study we developed a closed-loop time-variant identification technique based on wavelet cross spectra to continuously identify the admittance, i.e. the dynamic relation between input force (or torque) and the output displacement. This identification technique allowed for measurement of the human joint dynamics as a function of time while the human interacts with a mechanical load. As a second step the afferent feedback strengths were quantified by fitting a neuromuscular control model onto the admittance for each time instant. The model fit produced physiological relevant parameters, like muscle visco-elasticity resulting from (co-)contraction, afferent feedback from muscle spindles and Golgi tendon organs including neural time delays. Simulations demonstrated that the developed method is able to track time-variant behavior. Preliminary results of experimental data showed that human subjects adapt their admittance to an instantaneous change of a viscous load. In particular, the gain of the afferent feedback changed within seconds. The estimated dynamic behavior of the human joint before and after the change of the viscous load resembled the behavior as identified using traditional time-invariant techniques in two separate experiments with constant viscous loads. However, the accuracy of the estimated adaptation time of the system is yet to be determined as the method in its current form is less able to track fast changes in system behavior. Further research into time-variant closed-loop identification is recommended to improve the temporal accuracy

    Quantifying Proprioceptive Reflexes During Position Control of the Human Arm

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    Kidney Dysfunction Increases Mortality and Incident Events after Young Stroke: The FUTURE Study.

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    BACKGROUND: In about 30% of young stroke patients, no cause can be identified. In elderly patients, kidney dysfunction has been suggested as a contributing risk factor for mortality as well as stroke. There are hypotheses that novel non-traditional risk factors, like chronic inflammation and oxidative stress, are involved in chronic kidney disease, affecting the cerebral microvasculature that would in turn lead to stroke. Our objective is to investigate the influence of kidney dysfunction on long-term mortality and incident vascular events after stroke in young adults aged 18 through 50 and if this relationship would be independent of other cardiovascular risk factors. METHODS: We prospectively included 460 young stroke patients with an ischemic stroke or transient ischemic attack admitted to our department between January 1, 1980 and November 1, 2010. Follow-up was done between 2014 and 2015. Estimated glomerular filtration rate (eGFR) was calculated from baseline creatinine levels and was divided in 3 subgroups: eGFR 120 ml/min/1.73 m2. Cox proportional hazard models were used to determine the effect of kidney dysfunction on mortality and incident vascular events, adjusting for cardiovascular risk factors. RESULTS: An eGFR <60 (HR 4.6; 95% CI 2.6-8.2) was associated with an increased risk of death and an increased risk of incident stroke (HR 4.1; 95% CI 1.9-9.0) independent of cardiovascular risk factors, but it was not associated with other vascular events. The point estimate for the 15-year cumulative mortality was 70% (95% CI 46-94) for patients with a low eGFR, 24% (95% CI 18-30) for patients with a normal eGFR and 30% (95% CI 12-48) for patients with a high eGFR. The point estimate for the 15-year cumulative risk of incident stroke was 45% (95% CI 16-74) for patients with a low eGFR, 13% (95% CI 9-17) for patients with a normal eGFR and 8% (95% CI 0-18) for patients with a high eGFR. CONCLUSIONS: Kidney dysfunction is related to long-term mortality and stroke recurrence, but not to incident cardiovascular disease, on average 11 years after young stroke. This warrants a more intensive follow-up of young stroke patients with signs of kidney dysfunction in the early phase. In addition, the clear association between kidney dysfunction and incident stroke seen in our young stroke population might be a first step in the recognition of kidney dysfunction as a new risk factor for the development of stroke at young age. Also, it can lead to new insights in the etiological differences between cardiovascular and cerebrovascular disease.This study was funded by the Dutch Epilepsy Fund (grant number 10-18)

    The relation between neuromechanical parameters and Ashworth score in stroke patients

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    Quantifying increased joint resistance into its contributing factors i.e. stiffness and viscosity ("hypertonia") and stretch reflexes ("hyperreflexia") is important in stroke rehabilitation. Existing clinical tests, such as the Ashworth Score, do not permit discrimination between underlying tissue and reflexive (neural) properties. We propose an instrumented identification paradigm for early and tailor made interventions.BioMechanical EngineeringMechanical, Maritime and Materials Engineerin

    Analysis of reflex modulation with a biologically realistic neural network

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    In this study, a neuromusculoskeletal model was built to give insight into the mechanisms behind the modulation of reflexive feedback strength as experimentally identified in the human shoulder joint. The model is an integration of a biologically realistic neural network consisting of motoneurons and interneurons, modeling 12 populations of spinal neurons, and a one degree-of-freedom musculoskeletal model, including proprioceptors. The model could mimic the findings of human postural experiments, using presynaptic inhibition of the Ia afferents to modulate the feedback gains. In a pathological case, disabling one specific neural connection between the inhibitory interneurons and the motoneurons could mimic the experimental findings in complex regional pain syndrome patients. It is concluded that the model is a valuable tool to gain insight into the spinal contributions to human motor control. Applications lay in the fields of human motor control and neurological disorders, where hypotheses on motor dysfunction can be tested, like spasticity, clonus, and tremor
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