Augmented reality device for first response scenarios

A prototype of a wearable computer system is proposed and implemented using commercial off-shelf components. The system is designed to allow the user to access location-specific information about an environment, and to provide capability for user tracking. Areas of applicability include primarily first response scenarios, with possible applications in maintenance or construction of buildings and other structures. Necessary preparation of the target environment prior to system\u27s deployment is limited to noninvasive labeling using optical fiducial markers. The system relies on computational vision methods for registration of labels and user position. With the system the user has access to on-demand information relevant to a particular real-world location. Team collaboration is assisted by user tracking and real-time visualizations of team member positions within the environment. The user interface and display methods are inspired by Augmented Reality1 (AR) techniques, incorporating a video-see-through Head Mounted Display (HMD) and fingerbending sensor glove.*. 1Augmented reality (AR) is a field of computer research which deals with the combination of real world and computer generated data. At present, most AR research is concerned with the use of live video imagery which is digitally processed and augmented by the addition of computer generated graphics. Advanced research includes the use of motion tracking data, fiducial marker recognition using machine vision, and the construction of controlled environments containing any number of sensors and actuators. (Source: Wikipedia) *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Adobe Acrobat; Microsoft Office; Windows MediaPlayer or RealPlayer

A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms

In this paper a review is presented of the research on eye gaze estimation techniques and applications, that has progressed in diverse ways over the past two decades. Several generic eye gaze use-cases are identified: desktop, TV, head-mounted, automotive and handheld devices. Analysis of the literature leads to the identification of several platform specific factors that influence gaze tracking accuracy. A key outcome from this review is the realization of a need to develop standardized methodologies for performance evaluation of gaze tracking systems and achieve consistency in their specification and comparative evaluation. To address this need, the concept of a methodological framework for practical evaluation of different gaze tracking systems is proposed.Comment: 25 pages, 13 figures, Accepted for publication in IEEE Access in July 201

A Desktop Networked Haptic VR Interface for Mechanical Assembly

This paper presents the development of a PC-based 3D human computer interface for virtual assembly applications. This system is capable of importing complex CAD (Computer Aided Design) models, rendering them in stereo, and implementing haptic force feedback for realistic part interaction in virtual environments. Such an application will facilitate wider acceptance of the use of a VR interface for prototyping assembly tasks. This interface provides both visual and haptic feedback to the user, while allowing assembly tasks to be performed on a desktop virtual environment. The network module has the ability to communicate with multiple VR systems (such as CAVE etc.) at geographically dispersed locations using a non-dedicated network channel. The potential benefits of such a system include identification of assembly issues early in the design process where changes can be made easily, resulting in a more efficient and less costly product design process

EdgeGlass: Exploring Tapping Performance on Smart Glasses while Sitting and Walking

Department of Human Factors EngineeringCurrently, smart glasses allow only touch sensing area which supports front mounted touch pads. However, touches on top, front and bottom sides of glass mounted touchpad is not yet explored. We made a customized touch sensor (length: 5-6 cm, height: 1 cm, width: 0.5 cm) featuring the sensing on its top, front, and bottom surfaces. For doing that, we have used capacitive touch sensing technology (MPR121 chips) with an electrode size of ~4.5 mm square, which is typical in the modern touchscreens. We have created a hardware system which consists of a total of 48 separate touch sensors. We investigated the interaction technique by it for both the sitting and walking situation, using a single finger sequential tapping and a pair finger simultaneous tapping. We have divided each side into three equal target areas and this separation made a total of 36 combinations. Our quantitative result showed that pair finger simultaneous tapping touches were faster, less error-prone in walking condition, compared to single finger sequential tapping into walking condition. Whereas, single finger sequence tapping touches were slower, but less error-prone in sitting condition, compared to pair simultaneous tapping in sitting condition. However, single finger sequential tapping touches were slower, much less error-prone in sitting condition compared to walking. Interestingly, double finger tapping touches had similar performance result in terms of both, error rate and completion time, in both sitting and walking conditions. Mental, physical, performance, effort did not have any effect on any temporal tapping???s and body poses experience of workload. In case of the parameter of temporal demand, for single finger sequential tapping mean temporal (time pressure) workload demand was higher than pair finger simultaneous tapping but body poses did not affect temporal (time pressure) workload for both of the sequential and simultaneous tapping type. In case of the parameter of frustration, the result suggested that mean frustration workload was higher for single finger sequential tapping experienced by the participants compared to pair finger simultaneous tapping and among body poses, walking experienced higher frustration mean workload than sitting. The subjective measure of overall workload during the performance study showed no significant difference between both independent variable: body pose (sitting and walking) and temporal tapping (single finger sequential tapping and pair finger simultaneous tapping).ope

Smart Assistive Technology for People with Visual Field Loss

Visual field loss results in the lack of ability to clearly see objects in the surrounding environment, which affects the ability to determine potential hazards. In visual field loss, parts of the visual field are impaired to varying degrees, while other parts may remain healthy. This defect can be debilitating, making daily life activities very stressful. Unlike blind people, people with visual field loss retain some functional vision. It would be beneficial to intelligently augment this vision by adding computer-generated information to increase the users' awareness of possible hazards by providing early notifications. This thesis introduces a smart hazard attention system to help visual field impaired people with their navigation using smart glasses and a real-time hazard classification system. This takes the form of a novel, customised, machine learning-based hazard classification system that can be integrated into wearable assistive technology such as smart glasses. The proposed solution provides early notifications based on (1) the visual status of the user and (2) the motion status of the detected object. The presented technology can detect multiple objects at the same time and classify them into different hazard types. The system design in this work consists of four modules: (1) a deep learning-based object detector to recognise static and moving objects in real-time, (2) a Kalman Filter-based multi-object tracker to track the detected objects over time to determine their motion model, (3) a Neural Network-based classifier to determine the level of danger for each hazard using its motion features extracted while the object is in the user's field of vision, and (4) a feedback generation module to translate the hazard level into a smart notification to increase user's cognitive perception using the healthy vision within the visual field. For qualitative system testing, normal and personalised defected vision models were implemented. The personalised defected vision model was created to synthesise the visual function for the people with visual field defects. Actual central and full-field test results were used to create a personalised model that is used in the feedback generation stage of this system, where the visual notifications are displayed in the user's healthy visual area. The proposed solution will enhance the quality of life for people suffering from visual field loss conditions. This non-intrusive, wearable hazard detection technology can provide obstacle avoidance solution, and prevent falls and collisions early with minimal information

Applications of Computer Vision Technologies of Automated Crack Detection and Quantification for the Inspection of Civil Infrastructure Systems

Many components of existing civil infrastructure systems, such as road pavement, bridges, and buildings, are suffered from rapid aging, which require enormous nation\u27s resources from federal and state agencies to inspect and maintain them. Crack is one of important material and structural defects, which must be inspected not only for good maintenance of civil infrastructure with a high quality of safety and serviceability, but also for the opportunity to provide early warning against failure. Conventional human visual inspection is still considered as the primary inspection method. However, it is well established that human visual inspection is subjective and often inaccurate. In order to improve current manual visual inspection for crack detection and evaluation of civil infrastructure, this study explores the application of computer vision techniques as a non-destructive evaluation and testing (NDE&T) method for automated crack detection and quantification for different civil infrastructures. In this study, computer vision-based algorithms were developed and evaluated to deal with different situations of field inspection that inspectors could face with in crack detection and quantification. The depth, the distance between camera and object, is a necessary extrinsic parameter that has to be measured to quantify crack size since other parameters, such as focal length, resolution, and camera sensor size are intrinsic, which are usually known by camera manufacturers. Thus, computer vision techniques were evaluated with different crack inspection applications with constant and variable depths. For the fixed-depth applications, computer vision techniques were applied to two field studies, including 1) automated crack detection and quantification for road pavement using the Laser Road Imaging System (LRIS), and 2) automated crack detection on bridge cables surfaces, using a cable inspection robot. For the various-depth applications, two field studies were conducted, including 3) automated crack recognition and width measurement of concrete bridges\u27 cracks using a high-magnification telescopic lens, and 4) automated crack quantification and depth estimation using wearable glasses with stereovision cameras. From the realistic field applications of computer vision techniques, a novel self-adaptive image-processing algorithm was developed using a series of morphological transformations to connect fragmented crack pixels in digital images. The crack-defragmentation algorithm was evaluated with road pavement images. The results showed that the accuracy of automated crack detection, associated with artificial neural network classifier, was significantly improved by reducing both false positive and false negative. Using up to six crack features, including area, length, orientation, texture, intensity, and wheel-path location, crack detection accuracy was evaluated to find the optimal sets of crack features. Lab and field test results of different inspection applications show that proposed compute vision-based crack detection and quantification algorithms can detect and quantify cracks from different structures\u27 surface and depth. Some guidelines of applying computer vision techniques are also suggested for each crack inspection application

Assessment of VR Technology and its Applications to Engineering Problems

Virtual reality applications are making valuable contributions to the field of product realization. This paper presents an assessment of the hardware and software capabilities of VR technology needed to support a meaningful integration of VR applications in the product life cycle analysis. Several examples of VR applications for the various stages of the product life cycle engineering are presented as case studies. These case studies describe research results, fielded systems, technical issues, and implementation issues in the areas of virtual design, virtual manufacturing, virtual assembly, engineering analysis, visualization of analysis results, and collaborative virtual environments. Current issues and problems related to the creation, use, and implementation of virtual environments for engineering design, analysis, and manufacturing are also discussed