32,403 research outputs found
Asymmetric Dual-Arm Task Execution using an Extended Relative Jacobian
Coordinated dual-arm manipulation tasks can be broadly characterized as
possessing absolute and relative motion components. Relative motion tasks, in
particular, are inherently redundant in the way they can be distributed between
end-effectors. In this work, we analyse cooperative manipulation in terms of
the asymmetric resolution of relative motion tasks. We discuss how existing
approaches enable the asymmetric execution of a relative motion task, and show
how an asymmetric relative motion space can be defined. We leverage this result
to propose an extended relative Jacobian to model the cooperative system, which
allows a user to set a concrete degree of asymmetry in the task execution. This
is achieved without the need for prescribing an absolute motion target.
Instead, the absolute motion remains available as a functional redundancy to
the system. We illustrate the properties of our proposed Jacobian through
numerical simulations of a novel differential Inverse Kinematics algorithm.Comment: Accepted for presentation at ISRR19. 16 Page
Improving Temporal Accuracy of Human Metabolic Chambers for Dynamic Metabolic Studies
Metabolic chambers are powerful tools for assessing human energy expenditure, providing flexibility and comfort for the subjects in a near free-living environment. However, the flexibility offered by the large living room size creates challenges in the assessment of dynamic human metabolic signals—such as those generated during high-intensity interval training and short-term involuntary physical activities—with sufficient temporal accuracy. Therefore, this paper presents methods to improve the temporal accuracy of metabolic chambers. The proposed methods include 1) adopting a shortest possible step size, here one minute, to compute the finite derivative terms for the metabolic rate calculation, and 2) applying a robust noise reduction method—total variation denoising—to minimize the large noise generated by the short derivative term whilst preserving the transient edges of the dynamic metabolic signals. Validated against 24-hour gas infusion tests, the proposed method reconstructs dynamic metabolic signals with the best temporal accuracy among state-of-the-art approaches, achieving a root mean square error of 0.27 kcal/min (18.8 J/s), while maintaining a low cumulative error in 24-hour total energy expenditure of less than 45 kcal/day (188280 J/day). When applied to a human exercise session, the proposed methods also show the best performance in terms of recovering the dynamics of exercise energy expenditure. Overall, the proposed methods improve the temporal resolution of the chamber system, enabling metabolic studies involving dynamic signals such as short interval exercises to carry out the metabolic chambers
Bayesian multi-modal model comparison: a case study on the generators of the spike and the wave in generalized spike–wave complexes
We present a novel approach to assess the networks involved in the generation of spontaneous pathological brain activity based on multi-modal imaging data. We propose to use probabilistic fMRI-constrained EEG source reconstruction as a complement to EEG-correlated fMRI analysis to disambiguate between networks that co-occur at the fMRI time resolution. The method is based on Bayesian model comparison, where the different models correspond to different combinations of fMRI-activated (or deactivated) cortical clusters. By computing the model evidence (or marginal likelihood) of each and every candidate source space partition, we can infer the most probable set of fMRI regions that has generated a given EEG scalp data window. We illustrate the method using EEG-correlated fMRI data acquired in a patient with ictal generalized spike–wave (GSW) discharges, to examine whether different networks are involved in the generation of the spike and the wave components, respectively. To this effect, we compared a family of 128 EEG source models, based on the combinations of seven regions haemodynamically involved (deactivated) during a prolonged ictal GSW discharge, namely: bilateral precuneus, bilateral medial frontal gyrus, bilateral middle temporal gyrus, and right cuneus. Bayesian model comparison has revealed the most likely model associated with the spike component to consist of a prefrontal region and bilateral temporal–parietal regions and the most likely model associated with the wave component to comprise the same temporal–parietal regions only. The result supports the hypothesis of different neurophysiological mechanisms underlying the generation of the spike versus wave components of GSW discharges
OWL-POLAR : A Framework for Semantic Policy Representation and Reasoning
Peer reviewedPreprin
Bayesian multi--dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings
We deal with estimation of multiple dipoles from combined MEG and EEG
time--series. We use a sequential Monte Carlo algorithm to characterize the
posterior distribution of the number of dipoles and their locations. By
considering three test cases, we show that using the combined data the method
can localize sources that are not easily (or not at all) visible with either of
the two individual data alone. In addition, the posterior distribution from
combined data exhibits a lower variance, i.e. lower uncertainty, than the
posterior from single device.Comment: 4 pages, 3 figures -- conference paper from EMBEC 2017, Tampere,
Finlan
Handling robot constraints within a Set-Based Multi-Task Priority Inverse Kinematics Framework
Set-Based Multi-Task Priority is a recent framework to handle inverse
kinematics for redundant structures. Both equality tasks, i.e., control
objectives to be driven to a desired value, and set-bases tasks, i.e., control
objectives to be satisfied with a set/range of values can be addressed in a
rigorous manner within a priority framework. In addition, optimization tasks,
driven by the gradient of a proper function, may be considered as well, usually
as lower priority tasks. In this paper the proper design of the tasks, their
priority and the use of a Set-Based Multi-Task Priority framework is proposed
in order to handle several constraints simultaneously in real-time. It is shown
that safety related tasks such as, e.g., joint limits or kinematic singularity,
may be properly handled by consider them both at an higher priority as
set-based task and at a lower within a proper optimization functional.
Experimental results on a 7DOF Jaco$^2
Marginal Effects and Significance Testing with Heckman’s Sample Selection Model: A Methodological Note
This paper illustrates two techniques for calculating the statistical significance of the marginal effects derived from Heckman’s sample selection model,an increasingly common econometric specification in political science. The discussion draws on an analysis by Sweeney (2003) of the incidence and intensity of interstate disputes. After replicating his results, the paper presents the delta method and the nonparametric bootstrap as alternative techniques for obtaining standard errors of the marginal effects, which themselves are calculated from a transformation of the model parameters.The analysis reveals two variables for which misleading inferences are drawn with respect to the precision of the estimated coefficients in the original study, suggesting that significance testing of the derived marginal effects is warranted.Heckman model, statistical significance, delta method; nonparametric bootstrap, dispute severity
Magnetoencephalography in Stroke Recovery and Rehabilitation
Magnetoencephalography (MEG) is a non-invasive neurophysiological technique used to study the cerebral cortex. Currently, MEG is mainly used clinically to localize epileptic foci and eloquent brain areas in order to avoid damage during neurosurgery. MEG might, however, also be of help in monitoring stroke recovery and rehabilitation. This review focuses on experimental use of MEG in neurorehabilitation. MEG has been employed to detect early modifications in neuroplasticity and connectivity, but there is insufficient evidence as to whether these methods are sensitive enough to be used as a clinical diagnostic test. MEG has also been exploited to derive the relationship between brain activity and movement kinematics for a motor-based brain-computer interface. In the current body of experimental research, MEG appears to be a powerful tool in neurorehabilitation, but it is necessary to produce new data to confirm its clinical utility
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