Assessment of Cognitive skills via Human-robot Interaction and Cloud Computing

Technological advances are increasing the range of applications for artificial intelligence, especially through its embodiment within humanoid robotics platforms. This promotes the development of novel systems for automated screening of neurological conditions to assist the clinical practitioners in the detection of early signs of mild cognitive impairments. This article presents the implementation and the experimental validation of the first robotic system for cognitive assessment, based on one of the most popular platforms for social robotics, Softbank "Pepper", which administers and records a set of multi-modal interactive tasks to engage the user cognitive abilities. The robot intelligence is programmed using the state-of-the-art IBM Watson AI Cloud services, which provide the necessary capabilities for improving the social interaction and scoring the tests. The system has been tested by healthy adults (N = 35) and we found a significant correlation between the automated scoring and the MoCA, one of the most widely used paper-and-pencil tests. We conclude that the system can be considered as a screening instrument for cognitive assessment

Anatomy: The Relationship Between Internal and External Visualizations

This dissertation explored the relationship between internal and external visualizations and the implications of this relationship for comprehending visuospatial anatomical information. External visualizations comprised different computer representations of anatomical structures, including: static, animated, non-interactive, interactive, non-stereoscopic, and stereoscopic visualizations. Internal visualizations involved examining participants’ ability to apprehend, encode, and manipulate mental representations (i.e., spatial visualization ability or Vz). Comprehension was measured with a novel spatial anatomy task that involved mental manipulation of anatomical structures in three-dimensions and two-dimensional cross-sections. It was hypothesized that performance on the spatial anatomy task would involve a trade-off between internal and external visualizations available to the learner. Results from experiments 1, 2, and 3 demonstrated that in the absence of computer visualizations, spatial visualization ability (Vz) was the main contributor to variation in spatial anatomy task performance. Subjects with high Vz scored higher, spent less time, and were more accurate than those with low Vz. In the presence of external computer visualizations, variation in task performance was attributed to both Vz and visuospatial characteristics of the computer visualization. While static representations improved performance of high- and low-Vz subjects equally, animations particularly benefited high Vz subjects, as their mean score on the SAT was significantly higher than the mean score of low Vz subjects. The addition of interactivity and stereopsis to the displays offered no additional advantages over non-interactive and non-stereoscopic visualizations. Interactive, non-interactive, stereoscopic and non-stereoscopic visualizations improved the performance of high- and low-Vz subjects equally. It was concluded that comprehension of visuospatial anatomical information involved a trade-off between the perception of external visualizations and the ability to maintain and manipulate internal visualizations. There is an inherent belief that increasing the educational effectiveness of computer visualizations is a mere question of making them dynamic, interactive, and/or realistic. However, experiments 1, 2, and 3 clearly demonstrate that this is not the case, and that the benefits of computer visualizations vary according to learner characteristics, particularly spatial visualization ability

An Affordance-Based Framework for Human Computation and Human-Computer Collaboration

Visual Analytics is “the science of analytical reasoning facilitated by visual interactive interfaces” [70]. The goal of this field is to develop tools and methodologies for approaching problems whose size and complexity render them intractable without the close coupling of both human and machine analysis. Researchers have explored this coupling in many venues: VAST, Vis, InfoVis, CHI, KDD, IUI, and more. While there have been myriad promising examples of human-computer collaboration, there exists no common language for comparing systems or describing the benefits afforded by designing for such collaboration. We argue that this area would benefit significantly from consensus about the design attributes that define and distinguish existing techniques. In this work, we have reviewed 1,271 papers from many of the top-ranking conferences in visual analytics, human-computer interaction, and visualization. From these, we have identified 49 papers that are representative of the study of human-computer collaborative problem-solving, and provide a thorough overview of the current state-of-the-art. Our analysis has uncovered key patterns of design hinging on human- and machine-intelligence affordances, and also indicates unexplored avenues in the study of this area. The results of this analysis provide a common framework for understanding these seemingly disparate branches of inquiry, which we hope will motivate future work in the field