Spatial tangible user interfaces for cognitive assessment and training

Abstract. This paper discusses Tangible User Interfaces (TUIs) and their potential impact on cognitive assessment and cognitive training. We believe that TUIs, and particularly a subset that we dub spatial TUIs, can extend human computer interaction beyond some of its current limitations. Spatial TUIs exploit human innate spatial and tactile ability in an intuitive and direct manner, affording interaction paradigms that are practically impossible using current interface technology. As proof-of-concept we examine implementations in the field of cognitive assessment and training. In this paper we use Cognitive Cubes, a novel TUI we developed, as an applied test bed for our beliefs, presenting promising experimental results for cognitive assessment of spatial ability, and possibly for training purposes.

The Gaia -ESO Survey: Separating disk chemical substructures with cluster models 17: Evidence of a separate evolution in the metal-poor thin disk

Context. Recent spectroscopic surveys have begun to explore the Galactic disk system on the basis of large data samples, with spatial distributions sampling regions well outside the solar neighborhood. In this way, they provide valuable information for testing spatial and temporal variations of disk structure kinematics and chemical evolution. Aims. The main purposes of this study are to demonstrate the usefulness of a rigorous mathematical approach to separate substructures of a stellar sample in the abundance-metallicity plane, and provide new evidence with which to characterize the nature of the metal-poor end of the thin disk sequence. Methods. We used a Gaussian mixture model algorithm to separate in the [Mg/Fe] vs. [Fe/H] plane a clean disk star subsample (essentially at RGC< 10 kpc) from the Gaia-ESO survey (GES) internal data release 2 (iDR2). We aim at decomposing it into data groups highlighting number density and/or slope variations in the abundance-metallicity plane. An independent sample of disk red clump stars from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) was used to cross-check the identified features. Results. We find that the sample is separated into five groups associated with major Galactic components; the metal-rich end of the halo, the thick disk, and three subgroups for the thin disk sequence. This is confirmed with the sample of red clump stars from APOGEE. The three thin disk groups served to explore this sequence in more detail. The two metal-intermediate and metal-rich groups of the thin disk decomposition ([Fe/H] > -0.25 dex) highlight a change in the slope at solar metallicity. This holds true at different radial regions of the Milky Way. The distribution of Galactocentric radial distances of the metal-poor part of the thin disk ([Fe/H] < -0.25 dex) is shifted to larger distances than those of the more metal-rich parts. Moreover, the metal-poor part of the thin disk presents indications of a scale height intermediate between those of the thick and the rest of the thin disk, and it displays higher azimuthal velocities than the latter. These stars might have formed and evolved in parallel and/or dissociated from the inside-out formation taking place in the internal thin disk. Their enhancement levels might be due to their origin from gas pre-enriched by outflows from the thick disk or the inner halo. The smooth trends of their properties (their spatial distribution with respect to the plane, in particular) with [Fe/H] and [Mg/Fe] suggested by the data indicates a quiet dynamical evolution, with no relevant merger events

Towards early diagnosis of dementia using a virtual environment

NoDementia is one of the biggest fears in the process of ageing and the most common cause is Alzheimer’s Disease(AD). Topographic disorientation is an early manifestation of AD and threatens activities of their daily lives. Finding solutions are essential in the early diagnosis of dementia if medical treatment and healthcare services to be deployed in time. Recent studies have shown that people with mild cognitive impairment (MCI) may convert to Alzheimer’s disease (AD) over time although not all MCI cases progress to dementia. The diagnosis of MCI is important to allow prompt treatment and disease management before the neurons degenerate to a stage beyond repair. Hence, the ability to obtain a method of identifying MCI is of great importance. This work presents a virtual environment which can be utilized as a quick, easy and friendly tool for early diagnosis of dementia. This tool was developed with an aim to investigate cognitive functioning in a group of healthy elderly and those with MCI. It focuses on the task of following a route, since Topographical Disorientation (TD) is common in AD. The results shows that this novel simulation was able to predict with about 90% overall accuracy using weighting function proposed to discriminate between MCI and healthy elderly