Academic literature on the topic 'Blink detection'

In the electroencephalography (EEG) study, eye blinks are a commonly known type of ocular artifact that appears most frequently in any EEG measurement. The artifact can be seen as spiking electrical potentials in which their time-frequency properties are varied across individuals. Their presence can negatively impact various medical or scientific research or be helpful when applying to brain-computer interface applications. Hence, detecting eye-blink signals is beneficial for determining the correlation between the human brain and eye movement in this paper. The paper presents a simple, fast, and automated eye-blink detection algorithm that did not require user training before algorithm execution. EEG signals were smoothed and filtered before eye-blink detection. We conducted experiments with ten volunteers and collected three different eye-blink datasets over three trials using Emotiv EPOC+ headset. The proposed method performed consistently and successfully detected spiking activities of eye blinks with a mean accuracy of over 96%.

The aim of this study is to build a linear decoding model that reveals the relationship between the movement information and the EOG (electrooculogram) data to online control a cursor continuously with blinks and eye pursuit movements. First of all, a blink detection method is proposed to reject a voluntary single eye blink or double-blink information from EOG. Then, a linear decoding model of time series is developed to predict the position of gaze, and the model parameters are calibrated by the RLS (Recursive Least Square) algorithm; besides, the assessment of decoding accuracy is assessed through cross-validation procedure. Additionally, the subsection processing, increment control, and online calibration are presented to realize the online control. Finally, the technology is applied to the volitional and online control of a cursor to hit the multiple predefined targets. Experimental results show that the blink detection algorithm performs well with the voluntary blink detection rate over 95%. Through combining the merits of blinks and smooth pursuit movements, the movement information of eyes can be decoded in good conformity with the average Pearson correlation coefficient which is up to 0.9592, and all signal-to-noise ratios are greater than 0. The novel system allows people to successfully and economically control a cursor online with a hit rate of 98%.

Abstract The aim of this paper is to present methods for human eye blink recognition. The main function of blinking is to spread tears across the eye and remove irratants from the surface of the cornea and conjuctiva. Blinking can be associated with internal memory processing, fatigue or activation in central nervous system. There are currently many methods for automatic blink detection. The most reliable methods include EOG or EEG signals. These methods, however, are associated with a decrease in the comfort of the examined person. This paper presents a method to detect blinks with the eye-tracker device. There are currently many blink detection methods for this devices. Two popular eye-trackers were tested in this paper. In addition a method for improving detection efficiency was proposed.

Video-oculography (VOG) is a non-invasive detection method used for eye movement. However, during testing, if object blinks, VOG would be difficult to acquire eye movement. A removing blink method based on Kalman Filter was presented. A cubic spline was employed to patch the removed data. Then simulation and experiment were done. The experimental results show that the method well predicts the next state. Compared to a threshold level, it eliminates blink artifact and patches the removed data. The method is a viable means of predicting pupil center for blink in VOG.

Eye blink detection plays a key role in many real-life applications such as Human-Computer Interaction (HCI), drowsy driving prevention and eye disease detection. Although traditional camera-based techniques are promising, multiple issues hinder their wide adoption including the privacy concern, strict lighting condition and line-of-sight (LoS) requirements. On the other hand, wireless sensing without a need for dedicated sensors gains a tremendous amount of attention in recent years. Among the wireless signals utilized for sensing, acoustic signals show a unique potential for fine-grained sensing owing to their low propagation speed in the air. Another trend favoring acoustic sensing is the wide availability of speakers and microphones in commodity devices. Promising progress has been achieved in fine-grained human motion sensing such as breathing using acoustic signals. However, it is still very challenging to employ acoustic signals for eye blink detection due to the unique characteristics of eye blink (i.e., subtle, sparse and aperiodic) and severe interference (i.e., from the human target himself and surrounding objects). We find that even the very subtle involuntary head movement induced by breathing can severely interfere with eye blink detection. In this work, for the first time, we propose a system called BlinkListener to sense the subtle eye blink motion using acoustic signals in a contact-free manner. We first quantitatively model the relationship between signal variation and the subtle movements caused by eye blink and interference. Then, we propose a novel method that exploits the "harmful" interference to maximize the subtle signal variation induced by eye blinks. We implement BlinkListener on both a research-purpose platform (Bela) and a commodity smartphone (iPhone 5c). Experiment results show that BlinkListener can achieve robust performance with a median detection accuracy of 95%. Our system can achieve high accuracies when the smartphone is held in hand, the target wears glasses/sunglasses and in the presence of strong interference with people moving around.

Accidents due to driver drowsiness can be prevented using eye blink sensors. The driver is supposed to wear the eye blink sensor frame throughout the course of driving and blink has to be for a couple of seconds to detect drowsiness. Any random changes in steering movement leads to reduction in wheel speed. The threshold of the vibration sensor can be varied and accordingly action can be taken. The outcome is that the vibrator attached to eye blink sensor’s frame vibrates if the driver falls asleep and also the LCD displays the warning messages. The wheel is slowed or stopped depending on the condition. This is accompanied by the owner being notified through the GSM module, so the owner can retrieve the driver’s location, photograph and police station list near to driver’s location. This is how the driver can be alerted during drowsiness and the owner can be notified simultaneously

Accidents caused by drivers’ drowsiness behind the steering wheel have a high fatality rate because of the discernible decline in the driver’s abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing such accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level of alertness of drivers and delivering effective feedback to maintain maximum performance. The main objective of this research study is to develop a reliable metric and system for the detection of driver impairment due to drowsiness. More specifically, the goal of the research is to develop the best possible metric for detection of drowsiness, based on measures that can be detected during driving. This thesis describes the new studies that have been performed to develop, validate, and refine such a metric. A computer vision system is used to monitor the driver’s physiological eye blink behaviour. The novel application of green LED illumination overcame one of the major difficulties of the eye sclera segmentation problem due to illumination changes. Experimentation in a driving simulator revealed various visual cues, typically characterizing the level of alertness of the driver, and these cues were combined to infer the drowsiness level of the driver. Analysis of the data revealed that eye blink duration and eye blink frequency were important parameters in detecting drowsiness. From these measured parameters, a continuous measure of drowsiness, the New Drowsiness Scale (NDS), is derived. The NDS ranges from one to ten, where a decrease in NDS corresponds to an increase in drowsiness. Based upon previous research into the effects of drowsiness on driving performance, measures relating to the lateral placement of the vehicle within the lane are of particular interest in this study. Standard deviations of average deviations were measured continuously throughout the study. The NDS scale, based upon the gradient of the linear regression of standard deviation of average blink frequency and duration, is demonstrated as a reliable method for identifying the development of drowsiness in drivers. Deterioration of driver performance (reflected by increasingly severe lane deviation) is correlated with a decreasing NDS score. The final experimental results show the validity of the proposed model for driver drowsiness detection.

The thesis deals with eye blink detection, which is part of complex topic of face detection and recognition. The work intents on digital image processing. There is analyse of the topic and description of image databases for testing. Two main chapters describe design of eye blink detection with digital image processing with IR technology and without IR technology.

This master thesis deals with the issues of the eye blink recognition from video. The main task is to analyse algorithms dealing with a detection of persons and make a program that could recognize the eye blink. Analysis of these algorithms and their problems are in the first part of this thesis. In the second part design and properties of my program are described. The realization of the program is based on the method of move detection using the accumulated difference frame, which helps to identify the eye areas. The eye blink detection algorithm tests a match between a tresholded pattern of the eye area taken from the actual frame and the frame before. The resolution whether the eye blink happened or not, is based on the level of the match. The algorithm is designed for watching a sitting man, which is slightly moving. The background can be a little dynamic as well. An average quality video with a moderator and dynamic backround was used as a tested subject.

BACKGROUND.One group of theories aiming at providing a framework explaining the etiology, maintenance and phenomenology of anxiety disorders is classified as cognitive models of anxiety. These approaches assume that distortions in specific levels of information processing are relevant for the onset and maintenance of the disorder. A detailed knowledge about the nature of these distortions would have important implications for the therapy of anxiety, as the implementation of confrontative or cognitive elements precisely fitting the distortions might enhance efficacy. Still, these models and related empirical evidence provide conflicting assumptions about the nature of disorder-linked processing distortions. Many cognitive models of anxiety (e.g., Fox, Russo, & Dutton, 2002; Mathews & Mackintosh, 1998; Williams, Watts, MacLeod, & Mathews, 1997) postulate that anxiety-linked biases of attention imply hypervigilance to threat and distractibility from other stimuli in the presence of feared materials. This is convincingly confirmed by various experimentalclinical studies assessing attention for threat in anxious participants compared to non-anxious controls (for a review, seeMathews &MacLeod, 2005). In contrast, assumptions concerning anxiety-linked biased memory for threat are less convincing; based on the shared tendency for avoidance of deeper elaboration in anxiety disorders, some models predict memory biases only for implicit memory tasks (Williams et al., 1997) or even disclaim the relevance of memory in anxiety at all (e.g., Mogg, Bradley, Miles, & Dixon, 2004). Other theories restrict the possibility of measuring disorder-specific memory biases to tasks that require merely perceptual encoding of the materials instead of verbal-conceptual memory (e.g., Fox et al., 2002; Mathews &Mackintosh, 1998). On the one hand, none of these models has integrated all the inconsistencies in empirical data on the topic. On the other hand, the numerous empirical studies on memory in anxiety that have been conducted with varying materials, anxiety disorders, encoding and retrieval conditions do not allow final conclusions about the prerequisites for finding memory biases (for a review, see MacLeod & Mathews, 2004). A more detailed investigation of the complete spectrum of memory for threat utilizing carefully controlled variations of depth of encoding and materials is needed. In view of these inconsistencies, it is all the more surprising that one important part of this spectrum has so far remained completely uninvestigated: visual working memory (VWM). No study has ever differentially addressed VWM for threat in anxious vs. nonanxious participants and none of the cognitive models of anxiety provides any predictions concerning this stage of information processing. Research on cognitive biases in anxiety has thus far only addressed the two extremes of the processing continuum: attention and longer-term memory. In between, a gap remains, the bridging of which might bring us closer to defining the prerequisites of memory biases in anxiety. As empirical research has provided substantial and coherent knowledge concerning attention in anxiety, and as attention and VWM are so closely linked (see, for instance, Cowan, 1995), the thorough investigation of VWM may provide important clues for models of anxiety. Is anxiety related to VWM biases favoring the processing of threatening information, or does the avoidance presumed by cognitive models of anxiety already begin at this stage? RESEARCH AIMS. To investigate the relevance of biased VWM in anxiety, the present research focused in eight experiments on the following main research questions: (1) Is threat preferably stored in VWM in anxious individuals? (2) Does threat preference occur at the cost of the storage of other items, or is extra storage capacity provided? (3) Would the appearance of threat interrupt ongoing encoding of non-threatening items? (4) Does prioritized encoding of threat in anxiety occur strategically or automatically? (5) Are disorder-specific VWM biases also materials-specific? (6) Are VWM biases in anxiety modifiable through cognitive-behavioral therapy? METHODS. In Experiments 1-4, a spatial-sequential cueing paradigm was used. A subset of real-object display items was successively cued on each trial by a sudden change of the picture background for 150 ms each. After the cueing, one of the display pictures was hidden and probed for a memory test. On most trials, a cued item was tested, and memory accuracy was determined depending on the item’s position within the cue string and depending on its valence. In some cases, memory for an uncued item was tested. Experiment 1 and 2 were directed at discovering whether spider fearfuls and non-anxious controls would differ with respect to the accuracy in memorizing cued spiders and uncued spiders and, thus, reveal disorder-specific biases of VWM. In addition, the question whether the presence of a spider image is related to costs for the memorization of other images was tested. Experiment 3 addressed whether any disorder-specific VWM biases found earlier were specific to the feared spiders. Therefore, the critical stimuli here were a snake and a spider. Participants were spider fearfuls and non-anxious controls, both without snake anxiety. In Experiment 4, it was tested whether disorder-specific biases found in Experiment 1 and 2 were modifiable through cognitive-behavioral treatment. The critical stimulus was a spider image. Spider fearfuls were tested three times. Half of them received a cognitive-behavioral intervention after the first test, the other half only after the second test. In two additional experiments, VWM was assessed with a change-detection paradigm. The main aim was to clarify whether disorder-specific effects found in the previous experiments were associated with automatic or with strategic selective encoding of threatening materials, and whether any group differences in spider change detection were materials-specific to spiders, but not to snakes. In Experiment 5, several images were presented simultaneously in a study display for either 100 or 500 milliseconds. After a short interruption, a test display was presented including either the same items as the first one or one changed item. Participants’ accuracy in determining whether displays were the same or different was measured depending on the valence of the changed item, set size, and presentation time of the display. There were trials with and without spiders. If a change was made, it could involve either a non-spider or a spider item. Of specific interest was the condition in which a spider image was presented initially, but not in the test phase, as noticing this specific change would require storage of that image in VWM. Would group differences be particularly pronounced in the shorter encoding condition suggesting automatic encoding of threat, or would they occur in the longer encoding condition, suggesting strategic encoding of spiders? In Experiment 6, change detection accuracy for spiders vs. snakes was tested. The participants in both experiments were spider fearfuls vs. controls, but those of Experiment 6 were additionally required to lack snake anxiety. Moreover, a temporal VWM paradigm - an attentional blink task - was applied to assess whether a biased encoding of spider images in spider fearfuls would occur at the expense of non-threatening items undergoing concurrent processing, and whether this effect was specific to spiders, but not to snakes. Series of real-object pictures were presented at rates of 80 ms at the display center. The observer’s task was to identify and report the two target pictures indicated by a brighter background. In Experiment 7, the first target always depicted a neutral item. The valence of the second target was varied - either negative depicting a spider, positive, or neutral. Participants varied with respect to their spider anxiety. In Experiment 8, spider fearfuls and non-anxious controls, both without snake anxiety, were tested. The experiment was nearly the same as the previous one, but two negative target types were tested: disorder-relevant spiders and negative but not feared snakes. Of specific interest was whether the appearance of a threatening target would reduce the report probability of the earlier attended target, indicating the interruption of its VWM encoding in favor of the threat item. RESULTS. (1) Both anxious and non-anxious controls, showed VWM advantages for negative materials such as spider or snake images. (2) In addition, there were disorderspecific VWM biases: some effects were larger in spider fearfuls than in non-anxious controls and some effects occurred exclusively in spider fearfuls. (3) Group differences and, thus, disorder-specificity were particularly pronounced under competitive circumstances, that is, under the condition of numerous stimuli competing for processing resources: when only little orientation time was allowed, when only little time was provided for selecting and encoding items from a crowd, and when VWMfor the critical item required reflexive instead of voluntary attention. (4) Pronounced memory for task-relevant, voluntarily attended spiders was related to difficulties in disengaging attention from these items in the fearful group, reflected in reduced memory accuracy for the item following it. (5) Disorder-specific VWM biases seem to be based on attentional biases to threatening materials resulting in a very quick, automatic memory consolidation. However, this preferential encoding was not at the cost of neutral materials currently undergoing encoding processes. (6) All disorder-specific VWM biases occured only with fear-related materials, not with other negative materials. (7) Automatic and highly disorder-specific fear-related VWM biases – but not strategic VWM biases occuring in both groups - were modifiable through cognitive-behavioral intervention. CONCLUSIONS. This work provides additional information about informationprocessing distortions related to specific anxiety. With the experimental investigation of biased VWM, this work has been performed to fill a gap within research on cognitive biases in anxiety. Moreover, this dissertation contributes to cognitive theories of anxiety by proposing several recommendations for refinements of current theoretical approaches. Most important, it was suggested to extend existing models by a more detailed consideration of attention and memory. In view of numerous previous empirical studies on the topic and the conclusions of this dissertation, a differentiation of the attentional engagement and disengagement component appears inevitable. Even more important, in view of the data presented here predictions concerning VWM for threatening materials need to be taken into account. In addition, suggestions are provided for the differential consideration of biases occuring from prepotent threat value of negative stimuli vs. individual threat value. A proposal for a cognitive model of anxiety extended by all these aspects is provided to serve as an invitation of further research in the investigation of the nature of memory biases in anxiety disorders. REFERENCES: Cowan, N. (1995). Attention and Memory. An integrated framework.New York: Oxford University Press. Fox, E., Russo, R., & Dutton, K. (2002). Attentional bias for threat: Evidence for delayed disengagement from emotional faces. Cognition and Emotion, 16, 355-379. MacLeod, C., & Mathews, A. (2004). Selective memory effects in anxiety disorders: An overview of research findings and their implications. In D. Reisberg & P. Hertel (eds.), Memory and Emotion. Oxford: Oxford University Press. Mathews, A., & Mackintosh, B. (1998). A cognitive model of selective processing in anxiety. Cognitive Therapy and Research, 22 (6), 539-560. Mathews, A., & MacLeod, C. (2005). Cognitive vulnerability to emotional disorders. Annual Review of Clinical Psychology, 1, 167-195.Mathews, Mogg, May, & Eysenck (1989). Mogg, K., Bradley, B.P., Miles, F., & Dixon, R. (2004). Time course of attentional bias for threat scenes: Testing the vigilance avoidance hypothesis. Cognition and Emotion, 18(5), 689-700. Williams, J.M.G., Watts, F.N., MacLeod, C., & Mathews, A. (1997). Cognitive psychology and emotional disorders. Chichester: John Wiley.

This thesis compared the performance of a blind relay communication system with that of a non-blind relay communication system. For a blind system, a differential space-time block code (DSTBC) or a blind Viterbi detection were used, and for a non-blind system, a space-time block code (STBC) was used. Also, this thesis took another blind detection mechanism, the Zhang’s code, and compared its performance with that of the blind Viterbi detection under a Jakes’ fading environment. It was found that the case of a relay closer to the destination shows better performance than the other case of a relay closer to the destination. It was also observed that the blind Viterbi decoder shows better performance than the Zhang’s code under the Jakes’ fading environment.
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science.

Denna rapport är resultatet av ett examensarbete, på 20 högskolepoäng, som har utförts på Scania CV AB, Tekniskt centrum, Södertälje. Examensarbetet behandlar Side Blind Spot Detection och har resulterat i ett prototypsystem som detekterar objekt i döda vinklarna på sidorna av en lastbil. Systemet är ett aktivt säkerhetssystem som syftar till att förhindra olyckor och ge ökad trafiksäkerhet på vägarna.

Examensarbetet har varit tvådelat för att uppnå detta mål. Denna rapport behandlar främst val av sensorteknik för att upptäcka objekt i de döda vinklarna på sidan av en lastbil. Den behandlar också hårdvara till användargränssnittet samt installation av användargränssnittet och sensorerna i en lastbil. Den andra delen av examensarbetet har bestått av utformning av användargränssnittet och programmering av systemet. Detta kan läsas i rapporten "Side Blind Spot Detection - System och användargränssnitt" författad av Jenny Hedenberg och Hanna Torell, Chalmers Tekniska högskola, 2005.

I rapporten har sex olika sensortekniker studerats och utvärderats. De sex sensorteknikerna är ultraljud, passiv IR, lidar (aktiv IR), kamera, IR kamera och radar. Resultatet av utvärderingen visade att radar är den mest lämpade sensortekniken för den här typen av applikationer och det är därför radar används som sensor i prototypsystemet.

Systemet har tre olika lägen beroende på hur mycket information föraren önskar få när ett objekt befinner sig i döda vinkeln vilket styrs av en systemknapp. Föraren får informationen från användargränssnittet som består av två LED- displayer som är placerade i dörrkarmarna på vardera sida. Förutom att visa varningarna visuellt i LED-displayen används också ljud vid varning. Resultatet blev som förväntat och visar de funktioner som användargränssnittet har på ett bra sätt. Vad gäller resultatet av hela prototypsystemet så visar det att radar är ett bra val för denna applikation för att den klarar av de krav som ställs. Tester av systemet visar dock att den införskaffade radarn har begränsningar som försvårar filtreringen. Detta leder till att systemet inte är helt tillförlitligt eftersom systemet ibland missar objekt och ibland felvarnar för objekt som inte finns eller för objekt som inte är relevanta.