Automated pattern analysis in gesture research : similarity measuring in 3D motion capture models of communicative action

The question of how to model similarity between gestures plays an important role in current studies in the domain of human communication. Most research into recurrent patterns in co-verbal gestures – manual communicative movements emerging spontaneously during conversation – is driven by qualitative analyses relying on observational comparisons between gestures. Due to the fact that these kinds of gestures are not bound to well-formedness conditions, however, we propose a quantitative approach consisting of a distance-based similarity model for gestures recorded and represented in motion capture data streams. To this end, we model gestures by flexible feature representations, namely gesture signatures, which are then compared via signature-based distance functions such as the Earth Mover's Distance and the Signature Quadratic Form Distance. Experiments on real conversational motion capture data evidence the appropriateness of the proposed approaches in terms of their accuracy and efficiency. Our contribution to gesture similarity research and gesture data analysis allows for new quantitative methods of identifying patterns of gestural movements in human face-to-face interaction, i.e., in complex multimodal data sets

Automated pattern analysis in gesture research : similarity measuring in 3D motion capture models of communicative action

The question of how to model similarity between gestures plays an important role in current studies in the domain of human communication. Most research into recurrent patterns in co-verbal gestures – manual communicative movements emerging spontaneously during conversation – is driven by qualitative analyses relying on observational comparisons between gestures. Due to the fact that these kinds of gestures are not bound to well-formedness conditions, however, we propose a quantitative approach consisting of a distance-based similarity model for gestures recorded and represented in motion capture data streams. To this end, we model gestures by flexible feature representations, namely gesture signatures, which are then compared via signature-based distance functions such as the Earth Mover's Distance and the Signature Quadratic Form Distance. Experiments on real conversational motion capture data evidence the appropriateness of the proposed approaches in terms of their accuracy and efficiency. Our contribution to gesture similarity research and gesture data analysis allows for new quantitative methods of identifying patterns of gestural movements in human face-to-face interaction, i.e., in complex multimodal data sets

Sequential approach to joint flow-seismic inversion for improved characterization of fractured media

Seismic interpretation of subsurface structures is traditionally performed without any account of flow behavior. Here we present a methodology for characterizing fractured geologic reservoirs by integrating flow and seismic data. The key element of the proposed approach is the identification—within the inversion—of the intimate relation between fracture compliance and fracture transmissivity, which determine the acoustic and flow responses of a fractured reservoir, respectively. Owing to the strong (but highly uncertain) dependence of fracture transmissivity on fracture compliance, the modeled flow response in a fractured reservoir is highly sensitive to the geophysical interpretation. By means of synthetic models, we show that by incorporating flow data (well pressures and tracer breakthrough curves) into the inversion workflow, we can simultaneously reduce the error in the seismic interpretation and improve predictions of the reservoir flow dynamics. While the inversion results are robust with respect to noise in the data for this synthetic example, the applicability of the methodology remains to be tested for more complex synthetic models and field cases.Eni-MIT Energy Initiative Founding Member ProgramKorea (South). Ministry of Land, Transportation and Maritime Affairs (15AWMP-B066761-03

Distant delivery of a mindfulness-based intervention for people with Parkinson's disease: the study protocol of a randomised pilot trial

BACKGROUND: Psychological difficulties, especially depression and anxiety, are the most prevalent non-motor symptoms in Parkinson's disease. Pharmacological treatments for these conditions appear relatively ineffective in Parkinson's disease. Mindfulness courses are increasingly popular and recognised as effective for managing emotional states, and there is growing evidence for the effectiveness of mindfulness courses for people with long-term medical conditions. With this exploratory pilot trial, we want to assess the feasibility of the procedures and processes, including recruitment, most appropriate outcome measure(s), acceptability of type and number of measures, potential nocebo effects, and potential effectiveness and cost-effectiveness of a specially adapted distance-delivered mindfulness-based intervention in people affected by Parkinson's disease. METHODS/DESIGN: This is a pilot two-arm randomised parallel group controlled trial. Sixty participants who meet eligibility criteria will be randomly assigned either to an 8-week mindfulness-based intervention group or a wait-list control group. The mindfulness intervention will include 1-h weekly sessions delivered by a health psychologist trained to facilitate mindfulness courses. Participants in both groups will complete standardised questionnaires assessing anxiety, depression, pain, insomnia, fatigue, and daily activities at four time points (baseline, 4, 8, and 20 weeks). The analysis will also consider potential mechanisms of change, such as acceptance, self-compassion, and tolerance of uncertainty, as well as health economic outcomes. Participants' experiences of the mindfulness interventions will be explored via in-depth interviews. DISCUSSION: A mindfulness-based intervention for people with Parkinson's delivered remotely, through Skype group videoconferences, may represent a viable, more accessible, intervention for people with mobility limitations and people who live in rural areas. The trial will provide important information about the feasibility, potential efficacy and cost-effectiveness, and acceptability of the intervention as well as mechanisms of psychosocial adjustment. The results of this pilot trial will help us design a phase III trial to assess efficacy of an online mindfulness-based intervention in Parkinson's disease and evaluate significance. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02683330

The importance of understanding individual differences in Down syndrome

In this article, we first present a summary of the general assumptions about Down syndrome (DS) still to be found in the literature. We go on to show how new research has modified these assumptions, pointing to a wide range of individual differences at every level of description. We argue that, in the context of significant increases in DS life expectancy, a focus on individual differences in trisomy 21 at all levels—genetic, cellular, neural, cognitive, behavioral, and environmental—constitutes one of the best approaches for understanding genotype/phenotype relations in DS and for exploring risk and protective factors for Alzheimer’s disease in this high-risk population

Automated pattern analysis in gesture research : similarity measuring in 3D motion capture models of communicative action

The question of how to model similarity between gestures plays an important role in current studies in the domain of human communication. Most research into recurrent patterns in co-verbal gestures – manual communicative movements emerging spontaneously during conversation – is driven by qualitative analyses relying on observational comparisons between gestures. Due to the fact that these kinds of gestures are not bound to well-formedness conditions, however, we propose a quantitative approach consisting of a distance-based similarity model for gestures recorded and represented in motion capture data streams. To this end, we model gestures by flexible feature representations, namely gesture signatures, which are then compared via signature-based distance functions such as the Earth Mover's Distance and the Signature Quadratic Form Distance. Experiments on real conversational motion capture data evidence the appropriateness of the proposed approaches in terms of their accuracy and efficiency. Our contribution to gesture similarity research and gesture data analysis allows for new quantitative methods of identifying patterns of gestural movements in human face-to-face interaction, i.e., in complex multimodal data sets

Steady state lateral water flow through unsaturated soil layers

The effects of gravity on flow through variably saturated soil layers are investigated quantitatively using two new analytical solutions and a numerical model. In all cases, steady state flow occurs laterally through a tilted, rectangular layer of soil with a constant hydraulic head on either end. The first solution is for gravity-driven flow through a tilted layer with sublayering parallel to the slope. As intuitively suggested by earlier investigators, flow occurs parallel to the floor of the layer and the pressure head is a simple function of the normal distance from the floor of the layer. The second analytical solution is for a tilted soil layer described by a Gardner soil model with both gravity and pressure head gradients. Flow velocities remain parallel to the layer floor, which is confirmed by numerical simulations. This is in contrast with chosen non-Gardner examples for which the flow does not necessarily remain parallel to the floor. Copyright 2008 by the American Geophysical Union.Full Tex