Collision Avoidance Interface for Safe Piloting of Unmanned Vehicles using a Mobile Device

ABSTRACT Autonomous robots and vehicles can perform tasks that are unsafe or undesirable for humans to do themselves, such as investigate safety in nuclear reactors or assess structural damage to a building or bridge after an earthquake. In addition, improvements in autonomous modes of such vehicles are making it easier for minimally-trained individuals to operate the vehicles. As the autonomous capabilities advance, the user's role shifts from a direct teleoperator to a supervisory control role. Since the human operator is often better suited to make decisions in uncertain situations, it is important for the human operator to have awareness of the environment in which the vehicle is operating in order to prevent collisions and damage to the vehicle as well as the structures and people in the vicinity. In this paper, we present the Collision and Obstacle Detection and Alerting (CODA) display, a novel interface to enable safe piloting of a Micro Aerial Vehicle with a mobile device in real-world settings

Designing a passive brain computer interface using real time classification of functional near-infrared spectroscopy

Passive brain-computer interfaces consider brain activity as an additional source of information, to augment and adapt the interface instead of controlling it. We have developed a software system that allows for real time brain signal analysis and machine learning classification of affective and workload states measured with functional near-infrared spectroscopy (fNIRS) called the online fNIRS analysis and classification (OFAC). Our system reproduces successful offline procedures, adapting them for real-time input to a user interface. Our first evaluation compares a previous offline analysis with our online analysis. While results show an accuracy decrease, they are outweighed by the new ability of interface adaptation. The second study demonstrates OFAC's online features through real-time classification of two tasks, and interface adaptation according to the predicted task. Accuracy averaged over 85%. We have created the first working real time passive BCI using fNIRS, opening the door to build adaptive user interfaces. Copyrigh

Brainput: Enhancing Interactive Systems with Streaming fNIRS Brain Input

This paper describes the Brainput system, which learns to identify brain activity patterns occurring during multitasking. It provides a continuous, supplemental input stream to an interactive human-robot system, which uses this information to modify its behavior to better support multitasking. This paper demonstrates that we can use non-invasive methods to detect signals coming from the brain that users naturally and effortlessly generate while using a computer system. If used with care, this additional information can lead to systems that respond appropriately to changes in the user's state. Our experimental study shows that Brainput significantly improves several performance metrics, as well as the subjective NASA-Task Load Index scores in a dualtask human-robot activity. Author Keywords fNIRS; near-infrared spectroscopy; multitasking; brain computer interface; human-robot interaction ACM Classification Keywords H.5.m. Information interfaces and presentation (e.g., HCI)

Brain, Body and Bytes: Psychophysiological User Interaction

The human brain and body are prolific signal generators. Recent technologies and computing techniques allow us to measure, process and interpret these signals. We can now infer such things as cognitive and emotional states to create adaptive interactive systems and to gain an understanding of user experience. This workshop brings together researchers from the formerly separated communities of physiological computing (PC), and brain-computer interfaces (BCI) to discuss psychophysiological computing. We set out to identify key research challenges, potential global synergies, and emerging technological contributions

Background

Abstract The human brain and body are prolific signal generators. Recent technologies and computing techniques allow us to measure, process and interpret these signals. We can now infer such things as cognitive and emotional states to create adaptive interactive systems and to gain an understanding of user experience. This workshop brings together researchers from the formerly separated communities of physiological computing (PC), and brain-computer interfaces (BCI) to discuss psychophysiological computing. We set out to identify key research challenges, potential global synergies, and emerging technological contributions

Author Keywords

We created Smart Blocks, an augmented mathematical manipulative that allows users to explore the concepts of volume and surface area of 3-dimensional (3D) objects. This interface supports physical manipulation for exploring spatial relationships and it provides continuous feedback for reinforcing learning. By leveraging the benefits of physicality with the advantages of digital information, this tangible interface provides an engaging environment for learning about surface area and volume of 3D objects