1,913 research outputs found
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Methods, systems, and devices for pairing vagus nerve stimulation with motor therapy in stroke patients
A method of treating motor deficits in a stroke patient, comprising assessing a patient's motor deficits, determining therapeutic goals for the patient, based on the patient's motor deficits, selecting therapeutic tasks based on the therapeutic goals, performing each of the selected therapeutic tasks repetitively, observing the performance of the therapeutic tasks, initiating the stimulation of the vagus nerve manually at approximately a predetermined moment during the performance of the therapeutic tasks, stimulating the vagus nerve of the patient during the performance of the selected therapeutic tasks, and improving the patient's motor deficits.Board of Regents, University of Texas Syste
Recommended from our members
Methods, systems, and devices for pairing vagus nerve stimulation with motor therapy in stroke patients
A method of treating motor deficits in a stroke patient, comprising assessing a patient's motor deficits, determining therapeutic goals for the patient, based on the patient's motor deficits, selecting therapeutic tasks based on the therapeutic goals, performing each of the selected therapeutic tasks repetitively, observing the performance of the therapeutic tasks, initiating the stimulation of the vagus nerve manually at approximately a predetermined moment during the performance of the therapeutic tasks, stimulating the vagus nerve of the patient during the performance of the selected therapeutic tasks, and improving the patient's motor deficits.Board of Regents, University of Texas Syste
Data-Driven Grasp Synthesis - A Survey
We review the work on data-driven grasp synthesis and the methodologies for
sampling and ranking candidate grasps. We divide the approaches into three
groups based on whether they synthesize grasps for known, familiar or unknown
objects. This structure allows us to identify common object representations and
perceptual processes that facilitate the employed data-driven grasp synthesis
technique. In the case of known objects, we concentrate on the approaches that
are based on object recognition and pose estimation. In the case of familiar
objects, the techniques use some form of a similarity matching to a set of
previously encountered objects. Finally for the approaches dealing with unknown
objects, the core part is the extraction of specific features that are
indicative of good grasps. Our survey provides an overview of the different
methodologies and discusses open problems in the area of robot grasping. We
also draw a parallel to the classical approaches that rely on analytic
formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic
The Teenager's Problem: Efficient Garment Decluttering With Grasp Optimization
This paper addresses the ''Teenager's Problem'': efficiently removing
scattered garments from a planar surface. As grasping and transporting
individual garments is highly inefficient, we propose analytical policies to
select grasp locations for multiple garments using an overhead camera. Two
classes of methods are considered: depth-based, which use overhead depth data
to find efficient grasps, and segment-based, which use segmentation on the RGB
overhead image (without requiring any depth data); grasp efficiency is measured
by Objects per Transport, which denotes the average number of objects removed
per trip to the laundry basket. Experiments suggest that both depth- and
segment-based methods easily reduce Objects per Transport (OpT) by ;
furthermore, these approaches complement each other, with combined hybrid
methods yielding improvements of . Finally, a method employing
consolidation (with segmentation) is considered, which manipulates the garments
on the work surface to increase OpT; this yields an improvement of over
the baseline, though at a cost of additional physical actions
Visual Perception of Garments for their Robotic Manipulation
Tématem předložené práce je strojové vnímání textilií založené na obrazové informaci a využité pro jejich robotickou manipulaci. Práce studuje několik reprezentativních textilií v běžných kognitivně-manipulačních úlohách, jako je například třídění neznámých oděvů podle typu nebo jejich skládání. Některé z těchto činností by v budoucnu mohly být vykonávány domácími robotickými pomocníky. Strojová manipulace s textiliemi je poptávaná také v průmyslu. Hlavní výzvou řešeného problému je měkkost a s tím související vysoká deformovatelnost textilií, které se tak mohou nacházet v bezpočtu vizuálně velmi odlišných stavů.The presented work addresses the visual perception of garments applied for their robotic manipulation. Various types of garments are considered in the typical perception and manipulation tasks, including their classification, folding or unfolding. Our work is motivated by the possibility of having humanoid household robots performing these tasks for us in the future, as well as by the industrial applications. The main challenge is the high deformability of garments, which can be posed in infinitely many configurations with a significantly varying appearance
Continuous perception for deformable objects understanding
We present a robot vision approach to deformable object classification, with direct application to autonomous service robots. Our approach is based on the assumption that continuous perception provides robots with greater visual competence for deformable objects interpretation and classification. Our approach thus classifies the category of clothing items by continuously perceiving the dynamic interactions of the garment’s material and shape as it is being picked up. Our proposed solution consists of extracting continuously visual features of a RGB-D video sequence and fusing features by means of the Locality Constrained Group Sparse Representation (LGSR) algorithm. To evaluate the performance of our approach, we created a fully annotated database featuring 150 garment videos in random configurations. Experiments demonstrate that by continuously observing an object deform, our approach achieves a classification score of 66.7%, outperforming state-of-the-art approaches by a ∼ 27.3% increase
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