EOG-based eye movements codification for human computer interaction

This paper describes a new eye-control method for eye-based computer interaction using EOG. This work aims to resolve some of the problems encountered in current systems when used for long periods of time and users become tired. For this purpose, a new electrooculographic eye model based on wavelet transform and neural networks is proposed. The results obtained demonstrate the system’s reliability in detecting eye movements and show an error of less than 2° during long periods of use. The system proposed may be used to control any graphical interface using eye movements detected by electrooculography.     

Computer interface evaluation using eye movements: methods and constructs

Eye movement-based analysis can enhance traditional performance, protocol, and walk-through evaluations of computer interfaces. Despite a substantial history of eye movement data collection in tasks, there is still a great need for an organized definition and evaluation of appropriate measures. Several measures based upon eye movement locations and scanpaths were evaluated here, to assess their validity for assessment of interface quality. Good and poor interfaces for a drawing tool selection program were developed by manipulating the grouping of tool icons. These were subsequently evaluated by a collection of 50 interface designers and typical users. Twelve subjects used the interfaces while their eye movements were collected. Compared with a randomly organized set of component buttons, well-organized functional grouping resulted in shorter scanpaths, covering smaller areas. The poorer interface resulted in more, but similar duration, fixations than the better interface. Whereas the poor interface produced less efficient search behavior, the layout of component representations did not influence their interpretability. Overall, data obtained from eye movements can significantly enhance the observation of users' strategies while using computer interfaces, which can subsequently improve the precision of computer interface evaluations.

Relevance to industry

The software development industry requires improved methods for the objective analysis and design of software interfaces. This study provides a foundation for using eye movement analysis as part of an objective evaluation tool for many phases of interface analysis. The present approach is instructional in its definition of eye movement-based measures, and is evaluative with respect to the utility of these measures.     

Surface crack detection using deep learning with shallow CNN architecture for enhanced computation

Neural Computing and Applications - Surface cracks on the concrete structures are a key indicator of structural safety and degradation. To ensure the structural health and reliability of the...     

Traffic classification for efficient load balancing in server cluster using deep learning technique

The Journal of Supercomputing - Extensive use of multimedia services and Internet Data Center applications demand distributed deployment of these applications. It is implemented using edge...     

Diabetic retinopathy detection and stage classification in eye fundus images using active deep learning

Multimedia Tools and Applications - Retinal fundus image analysis (RFIA) for diabetic retinopathy (DR) screening can be used to reduce the risk of blindness among diabetic patients. The RFIA...     

An optimized hybrid deep learning model using ensemble learning approach for human walking activities recognition

The Journal of Supercomputing - Recent advancements in edge computing devices motivate us to develop a sustainable and reliable technique for multiple gait activities recognition using wearable...     

Visual routines for eye location using learning and evolution

Eye location is used as a test bed for developing navigation routines implemented as visual routines within the framework of adaptive behavior-based AI. The adaptive eye location approach seeks first where salient objects are, and then what their identity is. Specifically, eye location involves: 1) the derivation of the saliency attention map, and 2) the possible classification of salient locations as eve regions. The saliency (“where”) map is derived using a consensus between navigation routines encoded as finite-state automata exploring the facial landscape and evolved using genetic algorithms (GAs). The classification (“what”) stage is concerned with the optimal selection of features, and the derivation of decision trees, using GAs, to possibly classify salient locations as eyes. The experimental results, using facial image data, show the feasibility of our method, and suggest a novel approach for the adaptive development of task-driven active perception and navigational mechanisms     

Somatosensory computation for man-machine interface from motion-capture data and musculoskeletal human model

We discuss the computation of somatosensory information from motion-capture data. The efficient computational algorithms previously developed by the authors for multibody systems, such as humanoid robots, are applied to a musculoskeletal model of the human body. The somatosensory information includes tension, length, and velocity of the muscles, tension of the tendons and ligaments, pressure of the cartilages, and stress of the bones. The inverse dynamics of the musculoskeletal human model is formulated as an optimization problem subject to equality and inequality conditions. We analyzed the solutions obtained by linear and quadratic programming methods, and showed that linear programming has better performance. The technological development aims to define a higher dimensional man-machine interface and to open the door to the cognitive-level communication of humans and machines.     

Analysis of high-level dance movements under deep learning and internet of things

The Journal of Supercomputing - The purpose is to improve the design effect of high-level dance movements and help dancers to better master these movements. The body changes with advanced dance...     

On designing the adaptive computation framework of distributed deep learning models for Internet-of-Things applications

The Journal of Supercomputing - Deep learning methods have been gradually adopted in the Internet-of-Things (IoT) applications. Nevertheless, the large demands for the computation and memory...     

Cost-effective real-time recognition for human emotion-age-gender using deep learning with normalized facial cropping preprocess

Multimedia Tools and Applications - Because of technological advancement, human face recognition has been commonly applied in various fields. There are some HCI-related applications, such as...     

3D point cloud registration denoising method for human motion image using deep learning algorithm

Aiming at the problem of 3D point cloud noise affecting the efficiency and precision of human body 3D reconstruction in complex scenes, a 3D point cloud registration denoising method for human motion image using depth learning algorithm is proposed. First, two Kinect sensors are used to collect the three-dimensional data of the human body in the scene, and the spatial alignment under the Bursa linear model is used to pre-process the background point cloud data. The depth image of the point cloud is calculated, and the depth image pair is extracted by the convolutional neural network. Furthermore, the feature difference of the depth image pair is taken as the input of the fully connected network and the point cloud registration parameter is calculated, and the above operation is performed iteratively until the registration error is less than the acceptable threshold. Then, the improved C-means algorithm is used to remove the outlier, the noise is clustered, and the large-scale outlier noise is removed. Finally, the high-frequency information is processed by the depth data bilateral filtering method. The experimental results show that compared with the traditional bilateral filtering algorithm and fuzzy C-means algorithm, the proposed method can effectively remove noise of different scales and maintain good performance on the basis of maintaining human body features. In the point cloud model of A, B, and C, the average error of the proposed method is lower than that of the traditional bilateral filtering algorithm with 15.7%, 15.9%, and 19.8%, respectively, and it is lower than that of the fuzzy C-means algorithm with 25.8%, 26.9%, and 30.2%, respectively.     

The performance and the promise of evaluation in computer based learning

This paper will first consider the characteristics of computer based learning which have caused it to attract so much attention from evaluators, and will then give a brief overview of current approaches to evaluation. Some well known evaluation studies in CBL will be considered against that background, two of them in detail. Finally, some questions will be raised asking what service those involved with computer based learning now require from their evaluators, and how that might best be achieved.     

A deformable model of the human iris for measuring small three-dimensional eye movements

This paper describes a deformable model of the human iris which forms part of a system for accurate off-line measurement of binocular three-dimensional eye movements, particularly cyclotorsion (torsion), from video image sequences. At least two existing systems measure torsion from infrared video images by pupil tracking followed by cross correlation using arcs of bandpass-filtered iris texture. Unfortunately, pupil expansion and contraction reduces the accuracy of this method unless drugs are used to constrict the pupil, which causes temporary blurred vision. A five-parameter deformable model of the iris is therefore developed for analysing images obtained without the use of drugs. This model can translate (horizontal and vertical eye motion), rotate (torsion) and scale both uniformly and radially (pupil changes). Torsion measurements obtained with the model are repeatable and accurate to within 0.1°; this performance is illustrated by analysing binocular torsion during fixation on a stationary target. Received: 27 August 1997 / Accepted: 15 January 1998     

No evidence for prolonged latency of saccadic eye movements due to intermittent light of a CRT computer screen

Despite previous studies it remains unclear, whether saccadic eye movements across computer screens may be adversely affected by the intermittency of light of cathode ray tubes (CRT). We measured the latency of simple saccades to peripheral targets presented on a CRT-screen, operated at refresh rates of 50, 100 and 150 Hz, compared with a special fluorescent lamp display (FLD). Our results suggest that the intermittent light of CRT screens does not prolong the latency of saccades not even relative to a control condition of unmodulated steady light at the FLD. Further, there was no evidence for any individual effect in possibly susceptible subjects, e.g. at high critical flicker frequencies (CFF).     

No evidence for prolonged latency of saccadic eye movements due to intermittent light of a CRT computer screen

Despite previous studies it remains unclear, whether saccadic eye movements across computer screens may be adversely affected by the intermittency of light of cathode ray tubes (CRT). We measured the latency of simple saccades to peripheral targets presented on a CRT-screen, operated at refresh rates of 50, 100 and 150 Hz, compared with a special fluorescent lamp display (FLD). Our results suggest that the intermittent light of CRT screens does not prolong the latency of saccades not even relative to a control condition of unmodulated steady light at the FLD. Further, there was no evidence for any individual effect in possibly susceptible subjects, e.g. at high critical flicker frequencies (CFF).     

Visual and memory search in complex environments: determinants of eye movements and search performance

Previous research on visual and memory search revealed various top down and bottom up factors influencing performance. However, utilising abstract stimuli (e.g. geometrical shapes or letters) and focussing on individual factors has often limited the applicability of research findings. Two experiments were designed to analyse which attributes of a product facilitate search in an applied environment. Participants scanned displays containing juice packages while their eye movements were recorded. The familiarity, saliency, and position of search targets were systematically varied. Experiment 1 involved a visual search task, whereas Experiment 2 focussed on memory search. The results showed that bottom up (target saliency) and top down (target familiarity) factors strongly interacted. Overt visual attention was influenced by cultural habits, purposes, and current task demands. The results provide a solid database for assessing the impact and interplay of fundamental top down and bottom up determinants of search processes in applied fields of psychology. Practitioner Summary: Our study demonstrates how a product (or a visual item in general) needs to be designed and placed to ensure that it can be found effectively and efficiently within complex environments. Corresponding product design should result in faster and more accurate visual and memory based search processes.     

A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning

Multimedia Tools and Applications - Diabetic Retinopathy (DR) is a health condition caused due to Diabetes Mellitus (DM). It causes vision problems and blindness due to disfigurement of human...     

Enhancement of student learning performance using personalized diagnosis and remedial learning system

Although conventional student assessments are extremely convenient for calculating student scores, they do not conceptualize how students organize their knowledge. Therefore, teachers and students rarely understand how to improve their future learning progress. The limitations of conventional testing methods indicate the importance of accurately assessing and representing student knowledge structures. The personalized diagnosis and remedial learning system (PDRLS) proposed in this study enhances the effectiveness of the Pathfinder network by providing remedial learning paths for individual learners based on their knowledge structure. The sample was 145 students enrolled in introductory JAVA programming language courses at a Central Taiwan technology university. The experimental results demonstrate that learners who received personalized remedial learning guidance via PDRLS achieved improved learning performance, self-efficacy, and PDRLS use intention. The experimental results also indicated that students with lower knowledge level gain more benefits from the PDRLS than those with higher level of knowledge and that field dependence (FD) students obtain a greater benefit from PDRLS than field independence (FI) students do.     

Segmentation and classification of breast cancer using novel deep learning architecture

Neural Computing and Applications - Breast cancer is one of the most frequent cancers in women, and it has a higher mortality rate than other cancers. As a result, early detection is critical. In...