Michael W. Kennedy (3176337), Charles R. Crowell (3176331), Michael Villano (3176340) et al. · Figshare · 2016
<p>Feedback provided to each subject during the study. As the subject’s position shifted left or right, the green CoP marker shifted left or right. The green CoP marker’s vertical position was fixed. The blue rectangle represents the target region that the subject is attempting to reach with his/her CoP, while the time display indicates how long the CoP has remained within the target region. The arrows added to the figure indicate the directions of marker movement and were not part of the visual feedback.</p>
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2021
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Álvaro Costa (659402), Enrique Hortal (659403), Eduardo Iáñez (430386) et al. · Figshare · 2015
<p>The full circle represents the direction identified by the algorithm. The robotic arm moves according to the direction shown on this interface.</p>
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Marine Van Hollebeke (9724154), Diego Poddighe (11337459), Tin Gojevic (11337462) et al. · Figshare · 2021
<p>Real-time visual feedback on breathing characteristics are provided by the Breathe-Link software. In the upper left corner, the amount of inspirations that have been performed against the set inspiratory load is indicated. The general settings of the training are depicted in in upper right corner. In the middle of the screen the pointer indicates the peak power that is performed in the last inspiration. On the left of the pointer the peak flow and on the right of the pointer the inspiratory tidal volume of the last inspiration are shown. In the bars below the mean power and the inspiratory tidal volume of all breaths performed in the session are depecited.</p>
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Bertolasi, Jessica · OSF Preprints (OSF Preprints) · 2025
raw data for the poster paer Isolating haptic feedback from visual feedback for size discrimination using mixed reality technology submited at ISMAR 2025.
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National Cancer Institute · Definitions · 2020
Ope n Pe e r Re v ie w on Qe ios
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Andrey Eliseyev (438089), Ian Jerome Gonzales (10023448), Anh Le (377448) et al. · Figshare · 2021
<p>(A) Auditory feedback. Each time the motion intention pattern was detected in the EEG signal, the pace of the sound (beets per second, bps) and sound frequency were increased (Δbps = 0.15, ΔkHz = 0.15) until 2 bps and 2 kHz were reached. If the pattern was not detected, both were decreased (Δbps = -0.15, ΔkHz = -0.15) until reaching 0.5 bps and 0.5 kHz. When reaching a pace of 2 bps and a 2 kHz frequency during the motion task the auditory feedback “you have succeeded” was provided to the patient. (B) Visual feedback. Each time the motion intention was detected, the size of the bar was increased by 0.1 until reaching of 1. Otherwise, it was decreased by 0.1, until reaching 0. Motion intention periods were indicated with a green arrow pointing up and a red hourglass was pictured when no motion intention patterns were detected. A smiley face was displayed when the bar reached 1 (full size).</p>
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Yurina Otaki (8981024), Hidehito Honda (8981027), Kazuhiro Ueda (298710) · Figshare · 2020
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Smagt, P. van der, Groen, F. · CogPrints (University of Southampton) · 1997
In this chapter we introduce a method for model-free monocular visual guidance of a robot arm. The robot arm, with a single camera in its end effector, should be positioned above a stationary target. It is shown that a trajectory can be planned in visual space by using components of the optic flow, and this trajector can be translated to joint torques by a self-learning neural network. No model of the robot, camera, or environment is used. The method reaches a high grasping accuracy after only a few trials.
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2007
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Jie Yi Lim · DR-NTU (Nanyang Technological University) · 2019
This project is one of the requirements for the Bachelor of Engineering degree at Nanyang Technological University. The is an Industrial Sponsored Project (ISP) with Opsis Pte Ltd. Opsis have developed software to estimate the emotional state of a person by studying the facial expressions captured by a camera. This project examines the possibility of integrating this software into a classroom environment by analyzing the facial expressions of students during the lectures and tutorials conducted in Nanyang Technological University. Results from this project have concluded that the emotion of student varies according to time, the position of the student in the classroom and the classroom activities.
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Smagt van der, P., Groen, F.(1), · elib (German Aerospace Center) · 1997
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Kevin Francis. Bowen · 1976
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Andrew B. Slifkin (12042434), Jeffrey R. Eder (17262522) · Figshare · 2023
<p>As shown in the left panels, all conditions contained an initial period, over movements 1 to 100, where the targets, a target marker (+) (marking the currently active target), and a mouse-driven cursor (+) were displayed on the video monitor. In addition, the computer sounded an error “beep” (not referenced in the figure) whenever a movement terminated outside the target boundaries. Then, from movements 101 to 500, in the visual feedback condition the targets and cursor remained visible (top right panel), but in the no-visual feedback condition only the targets remained visible (bottom right panel). (Furthermore, during movements 101 to 500, the error “beep” was disabled in all conditions.) Note that the elements in the current illustration have not been drawn to scale; the actual targets appeared as white rectangular outlines superimposed on a black video-screen background; both the target markers and cursor were identical white crosshairs.</p>
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Bjarre, Hanna, Richardsson, Matilda · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2019
Nya casinon dyker upp allt oftare och spelberoende är ett återkommande ämne i nyheterna. Trots alla nya onlinecasinon finns det inga studier idag som undersöker hur den visuella aspekten av dessa onlinespel påverkar spelarna. Denna studie undersöker hur visuell återkoppling påverkar individernas spelande och risktagande när de spelar på onlinecasinon. För att studera dessa effekter genomfördes en studie på 45 studenter som delades in i två grupper. Båda grupperna blev ombedda att spela en prototyp av en enarmad bandit skapad för denna studie som heter "Casino Slotmachine". En grupp såg en vanlig grå pop-up ruta med resultat, medan den andra gruppen utsattes för animeringar i form av fallande konfetti när de vann och en mer estetiskt tilltalande popup-box i en ljusgul ton med resultat. Under spelet registrerades deltagarnas satsningar per runda, tid per runda samt antalet klick per runda. Varje deltagare spelade 20 rundor av spelet. Efter spelet svarade deltagarna på en enkät om deras spelupplevelse. Resultaten visar att deltagarna som fick visuell återkoppling tog större risker under sessionen än gruppen som inte fick visuell återkoppling. Visuell återkoppling kan därför vara en underliggande faktor till varför vissa spelare i onlinecasinon tar större risker idag. Undersökningen visar också på att de som fick visuell återkoppling blev mindre stressade av spelet än de som inte gjorde det. Både män och kvinnor påverkades på samma sätt av den visuella återkopplingen.
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Laura Kauhanen, Tapio Palomäki, Pasi Jylänki et al. · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 2006
Feedback plays an important role when learning to use a Brain-Computer Interface (BCI). Here we compare visual and haptic feedback in a short experiment. By imagining left and right hand movements, six subjects tried to control a BCI with the help of either visual or haptic feedback every 1s. Alpha band EEG signals from C3 and C4 were classified. The classifier was updated after each prediction using correct class information. Thus feedback could be given throughout the experiment. Subjects got better at controlling the BCI during the experiment independent of the feedback modality. Haptic feedback did not present any artifacts to the signals. More research is required on haptic feedback for BCI-applications because it frees visual attention to other tasks.
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National Cancer Institute · Definitions · 2020
Ope n Pe e r Re v ie w on Qe ios Ope n Pe
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Mores Prachyabrued, Christoph W. Borst · 2014
We investigate visual feedback for virtual grasps, especially cues to improve behavior after real fingers enter a virtual object. To date, such visual cues have usually been developed in an ad-hoc manner, with minimal or no studies that can guide selection. Existing guidelines are based largely on other interaction types and provide inconsistent and potentially-misleading information when applied to grasping. We compare several different visual feedback types including those most commonly seen for virtual hand interaction and with some novel visual aspects. The visuals were tuned in a pilot study, and our main study evaluated results in terms of objective performance (finger penetration, release time, and precision) and subjective rankings. Performancewise, the most promising techniques all directly reveal penetrating hand configuration in some way. Subjectively, most techniques are better than simple interpenetrating visuals, with color changes being most promising. The results enable selection of the best cues based on the relevant tradeoffs. Results also provide a needed basis for more focused studies of specific visual cues and for better informing studies of multimodal feedback.
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Donald G. Miller · Landscapes: the arts, aesthetics, and education · 2014
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Mária Orendáčová (13162815), Eugen Kvašňák (12081932), Jana Vránová (13162818) · Figshare · 2022
<p>Figure 2 depicts the underlying principle of NFB rewarding visual feedbacks. When NFB-rewarded selected EEG activity kept below reward threshold, online EEG graph was grey. As soon as amplitude of EEG activity was at least as high as reward threshold value, EEG graph got blue. Horizontal green line depicts reward threshold. Black line divided into 12 segments (each segment represents one second) (axis X) depicts time window. Axis y represents amplitude of EEG in pV (microvolt). Blue arrow in Y axis serves for manual adjustment of NFB reward threshold. 6.2 values next to axis Y (left side) indicates minimal value in pV which is necessary to be reached to get NFB feedback (blue EEG activity). 7.6 1.1V (the upper right quadrant indicates the average reward value within the current time window. 4 EEG1 -SMR is the title of this target online EEG graph predefined by NFB software Deymed Diagnostic (version 11).</p>
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