4,102 research outputs found
Detecting Relevance during Decision-Making from Eye Movements for UI Adaptation
This paper proposes an approach to detect information relevance during
decision-making from eye movements in order to enable user interface
adaptation. This is a challenging task because gaze behavior varies greatly
across individual users and tasks and groundtruth data is difficult to obtain.
Thus, prior work has mostly focused on simpler target-search tasks or on
establishing general interest, where gaze behavior is less complex. From the
literature, we identify six metrics that capture different aspects of the gaze
behavior during decision-making and combine them in a voting scheme. We
empirically show, that this accounts for the large variations in gaze behavior
and out-performs standalone metrics. Importantly, it offers an intuitive way to
control the amount of detected information, which is crucial for different UI
adaptation schemes to succeed. We show the applicability of our approach by
developing a room-search application that changes the visual saliency of
content detected as relevant. In an empirical study, we show that it detects up
to 97% of relevant elements with respect to user self-reporting, which allows
us to meaningfully adapt the interface, as confirmed by participants. Our
approach is fast, does not need any explicit user input and can be applied
independent of task and user.Comment: The first two authors contributed equally to this wor
Diverse Contributions to Implicit Human-Computer Interaction
Cuando las personas interactúan con los ordenadores, hay mucha
información que no se proporciona a propósito. Mediante el estudio de estas
interacciones implícitas es posible entender qué características de la interfaz
de usuario son beneficiosas (o no), derivando así en implicaciones para el
diseño de futuros sistemas interactivos.
La principal ventaja de aprovechar datos implícitos del usuario en
aplicaciones informáticas es que cualquier interacción con el sistema puede
contribuir a mejorar su utilidad. Además, dichos datos eliminan el coste de
tener que interrumpir al usuario para que envíe información explícitamente
sobre un tema que en principio no tiene por qué guardar relación con la
intención de utilizar el sistema. Por el contrario, en ocasiones las
interacciones implícitas no proporcionan datos claros y concretos. Por ello,
hay que prestar especial atención a la manera de gestionar esta fuente de
información.
El propósito de esta investigación es doble: 1) aplicar una nueva visión tanto
al diseño como al desarrollo de aplicaciones que puedan reaccionar
consecuentemente a las interacciones implícitas del usuario, y 2)
proporcionar una serie de metodologías para la evaluación de dichos
sistemas interactivos. Cinco escenarios sirven para ilustrar la viabilidad y la
adecuación del marco de trabajo de la tesis. Resultados empíricos con
usuarios reales demuestran que aprovechar la interacción implícita es un
medio tanto adecuado como conveniente para mejorar de múltiples maneras
los sistemas interactivos.Leiva Torres, LA. (2012). Diverse Contributions to Implicit Human-Computer Interaction [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17803Palanci
A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles
Vehicle to Vehicle (V2V) communication has a great potential to improve
reaction accuracy of different driver assistance systems in critical driving
situations. Cooperative Adaptive Cruise Control (CACC), which is an automated
application, provides drivers with extra benefits such as traffic throughput
maximization and collision avoidance. CACC systems must be designed in a way
that are sufficiently robust against all special maneuvers such as cutting-into
the CACC platoons by interfering vehicles or hard braking by leading cars. To
address this problem, a Neural- Network (NN)-based cut-in detection and
trajectory prediction scheme is proposed in the first part of this paper. Next,
a probabilistic framework is developed in which the cut-in probability is
calculated based on the output of the mentioned cut-in prediction block.
Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed
which incorporates this cut-in probability to enhance its reaction against the
detected dangerous cut-in maneuver. The overall system is implemented and its
performance is evaluated using realistic driving scenarios from Safety Pilot
Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I
Description and application of the correlation between gaze and hand for the different hand events occurring during interaction with tablets
People’s activities naturally involve the coordination of gaze and hand. Research in Human-Computer Interaction (HCI) endeavours to enable users to exploit this multimodality for enhanced interaction. With the abundance of touch screen devices, direct manipulation of an interface has become a dominating interaction technique. Although touch enabled devices are prolific in both public and private spaces, interactions with these devices do not fully utilise the benefits from the correlation between gaze and hand. Touch enabled devices do not employ the richness of the continuous manual activity above their display surface for interaction and a lot of information expressed by users through their hand movements is ignored. This thesis aims at investigating the correlation between gaze and hand during natural interaction with touch enabled devices to address these issues. To do so, we set three objectives. Firstly, we seek to describe the correlation between gaze and hand in order to understand how they operate together: what is the spatial and temporal relationship between these modalities when users interact with touch enabled devices? Secondly, we want to know the role of some of the inherent factors brought by the interaction with touch enabled devices on the correlation between gaze and hand, because identifying what modulates the correlation is crucial to design more efficient applications: what are the impacts of the individual differences, the task characteristics and the features of the on-screen targets? Thirdly, as we want to see whether additional information related to the user can be extracted from the correlation between gaze and hand, we investigate the latter for the detection of users’ cognitive state while they interact with touch enabled devices: can the correlation reveal the users’ hesitation? To meet the objectives, we devised two data collections for gaze and hand. In the first data collection, we cover the manual interaction on-screen. In the second data collection, we focus instead on the manual interaction in-the-air. We dissect the correlation between gaze and hand using three common hand events users perform while interacting with touch enabled devices. These events comprise taps, stationary hand events and the motion between taps and stationary hand events. We use a tablet as a touch enabled device because of its medium size and the ease to integrate both eye and hand tracking sensors. We study the correlation between gaze and hand for tap events by collecting gaze estimation data and taps on tablet in the context of Internet related tasks, representative of typical activities executed using tablets. The correlation is described in the spatial and temporal dimensions. Individual differences and effects of the task nature and target type are also investigated. To study the correlation between gaze and hand when the hand is in a stationary situation, we conducted a data collection in the context of a Memory Game, chosen to generate enough cognitive load during playing while requiring the hand to leave the tablet’s surface. We introduce and evaluate three detection algorithms, inspired by eye tracking, based on the analogy between gaze and hand patterns. Afterwards, spatial comparisons between gaze and hands are analysed to describe the correlation. We study the effects on the task difficulty and how the hesitation of the participants influences the correlation. Since there is no certain way of knowing when a participant hesitates, we approximate the hesitation with the failure of matching a pair of already seen tiles. We study the correlation between gaze and hand during hand motion between taps and stationary hand events from the same data collection context than the case mentioned above. We first align gaze and hand data in time and report the correlation coefficients in both X and Y axis. After considering the general case, we examine the impact of the different factors implicated in the context: participants, task difficulty, duration and type of the hand motion. Our results show that the correlation between gaze and hand, throughout the interaction, is stronger in the horizontal dimension of the tablet rather than in its vertical dimension, and that it varies widely across users, especially spatially. We also confirm the eyes lead the hand for target acquisition. Moreover, we find out that the correlation between gaze and hand when the hand is in the air above the tablet’s surface depends on where the users look at on the tablet. As well, we show that the correlation during eye and hand during stationary hand events can indicate the users’ indecision, and that while the hand is moving, the correlation depends on different factors, such as the degree of difficulty of the task performed on the tablet and the nature of the event before/after the motion
Co-adaptive control strategies in assistive Brain-Machine Interfaces
A large number of people with severe motor disabilities cannot access any of the
available control inputs of current assistive products, which typically rely on residual
motor functions. These patients are therefore unable to fully benefit from existent
assistive technologies, including communication interfaces and assistive robotics. In
this context, electroencephalography-based Brain-Machine Interfaces (BMIs) offer a
potential non-invasive solution to exploit a non-muscular channel for communication
and control of assistive robotic devices, such as a wheelchair, a telepresence
robot, or a neuroprosthesis. Still, non-invasive BMIs currently suffer from limitations,
such as lack of precision, robustness and comfort, which prevent their practical
implementation in assistive technologies.
The goal of this PhD research is to produce scientific and technical developments
to advance the state of the art of assistive interfaces and service robotics based on
BMI paradigms. Two main research paths to the design of effective control strategies
were considered in this project. The first one is the design of hybrid systems, based on
the combination of the BMI together with gaze control, which is a long-lasting motor
function in many paralyzed patients. Such approach allows to increase the degrees
of freedom available for the control. The second approach consists in the inclusion
of adaptive techniques into the BMI design. This allows to transform robotic tools and
devices into active assistants able to co-evolve with the user, and learn new rules of
behavior to solve tasks, rather than passively executing external commands.
Following these strategies, the contributions of this work can be categorized
based on the typology of mental signal exploited for the control. These include:
1) the use of active signals for the development and implementation of hybrid eyetracking
and BMI control policies, for both communication and control of robotic
systems; 2) the exploitation of passive mental processes to increase the adaptability
of an autonomous controller to the user\u2019s intention and psychophysiological state,
in a reinforcement learning framework; 3) the integration of brain active and passive
control signals, to achieve adaptation within the BMI architecture at the level of
feature extraction and classification
An end-to-end review of gaze estimation and its interactive applications on handheld mobile devices
In recent years we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality. This trend is driven by the enhanced computational power of these devices, higher resolution and capacity of their cameras, and improved gaze estimation accuracy obtained from advanced machine learning techniques, especially in deep learning. As the literature is fast progressing, there is a pressing need to review the state of the art, delineate the boundary, and identify the key research challenges and opportunities in gaze estimation and interaction. This paper aims to serve this purpose by presenting an end-to-end holistic view in this area, from gaze capturing sensors, to gaze estimation workflows, to deep learning techniques, and to gaze interactive applications.PostprintPeer reviewe
Dissemination of aerospace science and technology to highway safety and highway transportation programs Final report, 1 Jan. - 1 Apr. 1968
Feasibility of NASA literature application to highway safety and transportation engineerin
Neutral coding - A report based on an NRP work session
Neural coding by impulses and trains on single and multiple channels, and representation of information in nonimpulse carrier
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