30,889 research outputs found
Pre-Interaction Identification by Dynamic Grip Classification
We present a novel authentication method to identify users as they pick up a mobile device. We use a combination of back-of-device capacitive sensing and accelerometer measurements to perform classification, and obtain increased performance compared to previous accelerometer-only approaches. Our initial results suggest that users can be reliably identified during the pick-up movement before interaction commences
Recovering from External Disturbances in Online Manipulation through State-Dependent Revertive Recovery Policies
Robots are increasingly entering uncertain and unstructured environments.
Within these, robots are bound to face unexpected external disturbances like
accidental human or tool collisions. Robots must develop the capacity to
respond to unexpected events. That is not only identifying the sudden anomaly,
but also deciding how to handle it. In this work, we contribute a recovery
policy that allows a robot to recovery from various anomalous scenarios across
different tasks and conditions in a consistent and robust fashion. The system
organizes tasks as a sequence of nodes composed of internal modules such as
motion generation and introspection. When an introspection module flags an
anomaly, the recovery strategy is triggered and reverts the task execution by
selecting a target node as a function of a state dependency chart. The new
skill allows the robot to overcome the effects of the external disturbance and
conclude the task. Our system recovers from accidental human and tool
collisions in a number of tasks. Of particular importance is the fact that we
test the robustness of the recovery system by triggering anomalies at each node
in the task graph showing robust recovery everywhere in the task. We also
trigger multiple and repeated anomalies at each of the nodes of the task
showing that the recovery system can consistently recover anywhere in the
presence of strong and pervasive anomalous conditions. Robust recovery systems
will be key enablers for long-term autonomy in robot systems. Supplemental info
including code, data, graphs, and result analysis can be found at [1].Comment: 8 pages, 8 figures, 1 tabl
LFP beta amplitude is predictive of mesoscopic spatio-temporal phase patterns
Beta oscillations observed in motor cortical local field potentials (LFPs)
recorded on separate electrodes of a multi-electrode array have been shown to
exhibit non-zero phase shifts that organize into a planar wave propagation.
Here, we generalize this concept by introducing additional classes of patterns
that fully describe the spatial organization of beta oscillations. During a
delayed reach-to-grasp task in monkey primary motor and dorsal premotor
cortices we distinguish planar, synchronized, random, circular, and radial
phase patterns. We observe that specific patterns correlate with the beta
amplitude (envelope). In particular, wave propagation accelerates with growing
amplitude, and culminates at maximum amplitude in a synchronized pattern.
Furthermore, the occurrence probability of a particular pattern is modulated
with behavioral epochs: Planar waves and synchronized patterns are more present
during movement preparation where beta amplitudes are large, whereas random
phase patterns are dominant during movement execution where beta amplitudes are
small
Decoding social intentions in human prehensile actions: Insights from a combined kinematics-fMRI study
Consistent evidence suggests that the way we reach and grasp an object is modulated not
only by object properties (e.g., size, shape, texture, fragility and weight), but also by the
types of intention driving the action, among which the intention to interact with another agent
(i.e., social intention). Action observation studies ascribe the neural substrate of this `intentional'
component to the putative mirror neuron (pMNS) and the mentalizing (MS) systems.
How social intentions are translated into executed actions, however, has yet to be addressed.
We conducted a kinematic and a functional Magnetic Resonance Imaging (fMRI)
study considering a reach-to-grasp movement performed towards the same object positioned
at the same location but with different intentions: passing it to another person (social
condition) or putting it on a concave base (individual condition). Kinematics showed that individual
and social intentions are characterized by different profiles, with a slower movement
at the level of both the reaching (i.e., arm movement) and the grasping (i.e., hand aperture)
components. fMRI results showed that: (i) distinct voxel pattern activity for the social and the
individual condition are present within the pMNS and the MS during action execution; (ii)
decoding accuracies of regions belonging to the pMNS and the MS are correlated, suggesting
that these two systems could interact for the generation of appropriate motor commands.
Results are discussed in terms of motor simulation and inferential processes as part of a
hierarchical generative model for action intention understanding and generation of appropriate
motor commands
Functional Imaging of Autonomic Regulation: Methods and Key Findings.
Central nervous system processing of autonomic function involves a network of regions throughout the brain which can be visualized and measured with neuroimaging techniques, notably functional magnetic resonance imaging (fMRI). The development of fMRI procedures has both confirmed and extended earlier findings from animal models, and human stroke and lesion studies. Assessments with fMRI can elucidate interactions between different central sites in regulating normal autonomic patterning, and demonstrate how disturbed systems can interact to produce aberrant regulation during autonomic challenges. Understanding autonomic dysfunction in various illnesses reveals mechanisms that potentially lead to interventions in the impairments. The objectives here are to: (1) describe the fMRI neuroimaging methodology for assessment of autonomic neural control, (2) outline the widespread, lateralized distribution of function in autonomic sites in the normal brain which includes structures from the neocortex through the medulla and cerebellum, (3) illustrate the importance of the time course of neural changes when coordinating responses, and how those patterns are impacted in conditions of sleep-disordered breathing, and (4) highlight opportunities for future research studies with emerging methodologies. Methodological considerations specific to autonomic testing include timing of challenges relative to the underlying fMRI signal, spatial resolution sufficient to identify autonomic brainstem nuclei, blood pressure, and blood oxygenation influences on the fMRI signal, and the sustained timing, often measured in minutes of challenge periods and recovery. Key findings include the lateralized nature of autonomic organization, which is reminiscent of asymmetric motor, sensory, and language pathways. Testing brain function during autonomic challenges demonstrate closely-integrated timing of responses in connected brain areas during autonomic challenges, and the involvement with brain regions mediating postural and motoric actions, including respiration, and cardiac output. The study of pathological processes associated with autonomic disruption shows susceptibilities of different brain structures to altered timing of neural function, notably in sleep disordered breathing, such as obstructive sleep apnea and congenital central hypoventilation syndrome. The cerebellum, in particular, serves coordination roles for vestibular stimuli and blood pressure changes, and shows both injury and substantially altered timing of responses to pressor challenges in sleep-disordered breathing conditions. The insights into central autonomic processing provided by neuroimaging have assisted understanding of such regulation, and may lead to new treatment options for conditions with disrupted autonomic function
Fingers of a Hand Oscillate Together: Phase Syncronisation of Tremor in Hover Touch Sensing
When using non-contact finger tracking, fingers can be classified
as to which hand they belong to by analysing the phase
relation of physiological tremor. In this paper, we show how
3D capacitive sensors can pick up muscle tremor in fingers
above a device. We develop a signal processing pipeline
based on nonlinear phase synchronisation that can reliably
group fingers to hands and experimentally validate our technique.
This allows significant new gestural capabilities for
3D finger sensing without additional hardware
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