An empirical examination of feedback : user control and performance in a hapto-audio-visual training environment

Utilising advanced technologies, such as virtual environments (VEs), is of importance to training and education. The need to develop and effectively apply interactive, immersive 3D VEs continues to grow. As with any emerging technology, user acceptance of new software and hardware devices is often difficult to measure and guidelines to introduce and ensure adequate and correct usage of such technologies are lacking. It is therefore imperative to obtain a solid understanding of the important elements that play a role in effective learning through VEs. In particular, 3D VEs may present unusual and varied interaction and adoption considerations. The major contribution of this study is to investigate a complex set of interrelated factors in the relatively new sphere of VEs for training and education. Although many of the factors appears to be important from past research, researcher have not explicitly studied a comprehensive set of inter-dependant, empirically validated factors in order to understand how VEs aid complex procedural knowledge and motor skill learning. By integrating theory from research on training, human computer interaction (HCI), ergonomics and cognitive psychology, this research proposes and validates a model that contributes to application-specific VE efficacy formation. The findings of this study show visual feedback has a significant effect on performance. For tactile/force feedback and auditory feedback, no significant effect were found. For satisfaction, user control is salient for performance. Other factors such as interactivity and system comfort, as well as level of task difficulty, also showed effects on performance.<br /

Usability Evaluation in Virtual Environments: Classification and Comparison of Methods

Virtual environments (VEs) are a relatively new type of human-computer interface in which users perceive and act in a three-dimensional world. The designers of such systems cannot rely solely on design guidelines for traditional two-dimensional interfaces, so usability evaluation is crucial for VEs. We present an overview of VE usability evaluation. First, we discuss some of the issues that differentiate VE usability evaluation from evaluation of traditional user interfaces such as GUIs. We also present a review of VE evaluation methods currently in use, and discuss a simple classification space for VE usability evaluation methods. This classification space provides a structured means for comparing evaluation methods according to three key characteristics: involvement of representative users, context of evaluation, and types of results produced. To illustrate these concepts, we compare two existing evaluation approaches: testbed evaluation [Bowman, Johnson, & Hodges, 1999], and sequential evaluation [Gabbard, Hix, & Swan, 1999]. We conclude by presenting novel ways to effectively link these two approaches to VE usability evaluation

Rocket Testing and Integrated System Health Management

Integrated System Health Management (ISHM) describes a set of system capabilities that in aggregate perform: determination of condition for each system element, detection of anomalies, diagnosis of causes for anomalies, and prognostics for future anomalies and system behavior. The ISHM should also provide operators with situational awareness of the system by integrating contextual and timely data, information, and knowledge (DIaK) as needed. ISHM capabilities can be implemented using a variety of technologies and tools. This chapter provides an overview of ISHM contributing technologies and describes in further detail a novel implementation architecture along with associated taxonomy, ontology, and standards. The operational ISHM testbed is based on a subsystem of a rocket engine test stand. Such test stands contain many elements that are common to manufacturing systems, and thereby serve to illustrate the potential benefits and methodologies of the ISHM approach for intelligent manufacturing

Measuring user experience for virtual reality

In recent years, Virtual Reality (VR) and 3D User Interfaces (3DUI) have seen a drastic increase in popularity, especially in terms of consumer-ready hardware and software. These technologies have the potential to create new experiences that combine the advantages of reality and virtuality. While the technology for input as well as output devices is market ready, only a few solutions for everyday VR - online shopping, games, or movies - exist, and empirical knowledge about performance and user preferences is lacking. All this makes the development and design of human-centered user interfaces for VR a great challenge. This thesis investigates the evaluation and design of interactive VR experiences. We introduce the Virtual Reality User Experience (VRUX) model based on VR-specific external factors and evaluation metrics such as task performance and user preference. Based on our novel UX evaluation approach, we contribute by exploring the following directions: shopping in virtual environments, as well as text entry and menu control in the context of everyday VR. Along with this, we summarize our findings by design spaces and guidelines for choosing optimal interfaces and controls in VR.In den letzten Jahren haben Virtual Reality (VR) und 3D User Interfaces (3DUI) stark an Popularität gewonnen, insbesondere bei Hard- und Software im Konsumerbereich. Diese Technologien haben das Potenzial, neue Erfahrungen zu schaffen, die die Vorteile von Realität und Virtualität kombinieren. Während die Technologie sowohl für Eingabe- als auch für Ausgabegeräte marktreif ist, existieren nur wenige Lösungen für den Alltag in VR - wie Online-Shopping, Spiele oder Filme - und es fehlt an empirischem Wissen über Leistung und Benutzerpräferenzen. Dies macht die Entwicklung und Gestaltung von benutzerzentrierten Benutzeroberflächen für VR zu einer großen Herausforderung. Diese Arbeit beschäftigt sich mit der Evaluation und Gestaltung von interaktiven VR-Erfahrungen. Es wird das Virtual Reality User Experience (VRUX)- Modell eingeführt, das auf VR-spezifischen externen Faktoren und Bewertungskennzahlen wie Leistung und Benutzerpräferenz basiert. Basierend auf unserem neuartigen UX-Evaluierungsansatz leisten wir einen Beitrag, indem wir folgende interaktive Anwendungsbereiche untersuchen: Einkaufen in virtuellen Umgebungen sowie Texteingabe und Menüsteuerung im Kontext des täglichen VR. Die Ergebnisse werden außerdem mittels Richtlinien zur Auswahl optimaler Schnittstellen in VR zusammengefasst

Haptic technology for micro-robotic cell injection training systems — a review

Currently, the micro-robotic cell injection procedure is performed manually by expert human bio-operators. In order to be proficient at the task, lengthy and expensive dedicated training is required. As such, effective specialized training systems for this procedure can prove highly beneficial. This paper presents a comprehensive review of haptic technology relevant to cell injection training and discusses the feasibility of developing such training systems, providing researchers with an inclusive resource enabling the application of the presented approaches, or extension and advancement of the work. A brief explanation of cell injection and the challenges associated with the procedure are first presented. Important skills, such as accuracy, trajectory, speed and applied force, which need to be mastered by the bio-operator in order to achieve successful injection, are then discussed. Then an overview of various types of haptic feedback, devices and approaches is presented. This is followed by discussion on the approaches to cell modeling. Discussion of the application of haptics to skills training across various fields and haptically-enabled virtual training systems evaluation are then presented. Finally, given the findings of the review, this paper concludes that a haptically-enabled virtual cell injection training system is feasible and recommendations are made to developers of such systems

Development and Testing of A Wearable Vibrotactile Haptic Feedback System For Proprioceptive Rehabilitation

The human sense of touch is an integral part of daily life. For tasks involving grasping and manipulation of objects, force feedback is a key requirement. Most of the systems give contact point or complete grasping force feedback; for precision grasping and other physical interactions, finger awareness and force feedback from independent fingers is essential. In this study a novel, wearable proprioceptive rehabilitation system is designed which restores the ability of identifying and distinguishing between individual fingers of a prosthetic hand or an exoskeleton in a non-invasive manner. Moreover, it provides different levels of force feedback from every finger as well, which enables the user to distinguish and control force in precision grasping activities. For testing the system accuracy, classical psychophysical methods were used on a group of 14 voluntary disabled subjects. The tests were conducted in both, ideal and real-world conditions i.e. without and with distractions and accuracies were calculated accordingly. A p-test was also conducted to observe significance between the samples of with and without distraction datasets. The system performed with an overall accuracy of 82.04% which was well above the min. performance measure of 60%. Vi-HaB is standalone system and can be mounted on any upper limb rehabilitation (prosthesis, exoskeleton) system for finger awareness and force feedback

Addressing training data sparsity and interpretability challenges in AI based cellular networks

To meet the diverse and stringent communication requirements for emerging networks use cases, zero-touch arti cial intelligence (AI) based deep automation in cellular networks is envisioned. However, the full potential of AI in cellular networks remains hindered by two key challenges: (i) training data is not as freely available in cellular networks as in other fields where AI has made a profound impact and (ii) current AI models tend to have black box behavior making operators reluctant to entrust the operation of multibillion mission critical networks to a black box AI engine, which allow little insights and discovery of relationships between the configuration and optimization parameters and key performance indicators. This dissertation systematically addresses and proposes solutions to these two key problems faced by emerging networks. A framework towards addressing the training data sparsity challenge in cellular networks is developed, that can assist network operators and researchers in choosing the optimal data enrichment technique for different network scenarios, based on the available information. The framework encompasses classical interpolation techniques, like inverse distance weighted and kriging to more advanced ML-based methods, like transfer learning and generative adversarial networks, several new techniques, such as matrix completion theory and leveraging different types of network geometries, and simulators and testbeds, among others. The proposed framework will lead to more accurate ML models, that rely on sufficient amount of representative training data. Moreover, solutions are proposed to address the data sparsity challenge specifically in Minimization of drive test (MDT) based automation approaches. MDT allows coverage to be estimated at the base station by exploiting measurement reports gathered by the user equipment without the need for drive tests. Thus, MDT is a key enabling feature for data and artificial intelligence driven autonomous operation and optimization in current and emerging cellular networks. However, to date, the utility of MDT feature remains thwarted by issues such as sparsity of user reports and user positioning inaccuracy. For the first time, this dissertation reveals the existence of an optimal bin width for coverage estimation in the presence of inaccurate user positioning, scarcity of user reports and quantization error. The presented framework can enable network operators to configure the bin size for given positioning accuracy and user density that results in the most accurate MDT based coverage estimation. The lack of interpretability in AI-enabled networks is addressed by proposing a first of its kind novel neural network architecture leveraging analytical modeling, domain knowledge, big data and machine learning to turn black box machine learning models into more interpretable models. The proposed approach combines analytical modeling and domain knowledge to custom design machine learning models with the aim of moving towards interpretable machine learning models, that not only require a lesser training time, but can also deal with issues such as sparsity of training data and determination of model hyperparameters. The approach is tested using both simulated data and real data and results show that the proposed approach outperforms existing mathematical models, while also remaining interpretable when compared with black-box ML models. Thus, the proposed approach can be used to derive better mathematical models of complex systems. The findings from this dissertation can help solve the challenges in emerging AI-based cellular networks and thus aid in their design, operation and optimization

Mobility management in IP-Based Networks

Mobile communication networks experience a tremendous development clearly evident from the wide variety of new applications way beyond classical phone services. The tremendous success of the Internet along with the demand for always-on connectivity has triggered the development of All-IP mobile communication networks. Deploying these networks requires, however, overcoming many challenges. One of the main challenges is how to manage the mobility between cells connecting through an IP core in a way that satisfies real-time requirements. This challenge is the focus of this dissertation. This dissertation delivers an in-depth analysis of the mobility management issue in IP-based mobile communication networks. The advantages and disadvantages of various concepts for mobility management in different layers of the TCP/IP protocol stack are investigated. In addition, a classification and brief description of well-known mobility approaches for each layer are provided. The analysis concludes that network layer mobility management solutions seem to be best suited to satisfy the requirements of future All-IP networks. The dissertation, therefore, provides a comprehensive review of network layer mobility management protocols along with a discussion of their pros and cons. Analyses of previous work in this area show that the proposed techniques attempt to improve the performance by making constraints either on access networks (e.g. requiring a hierarchical topology, introducing of intermediate nodes, etc.) or mobile terminals (e.g. undertaking many measurements, location tracking, etc.). Therefore, a new technique is required that completes handoffs quickly without affecting the end-to-end performance of ongoing applications. In addition, it should place restrictions neither on access networks nor on mobiles. To meet these requirements, a new solution named Mobile IP Fast Authentication protocol (MIFA) is proposed. MIFA provides seamless mobility and advances the state of the art. It utilizes the fact that mobiles movements are limited to a small set of neighboring subnets. Thus, contacting these neighbors and providing them in advance with sufficient data related to the mobiles enable them to fast re-authenticate the mobiles after the handoff. The dissertation specifies the proposal for both IPv4 and IPv6. The specification of MIFA considers including many error recovery mechanisms to cover the most likely failures. Security considerations are studied carefully as well. MIFA does not make any restrictions on the network topology. It makes use of layer 2 information to optimize the performance and works well even if such information is not available.In order to analyze our new proposal in comparison to a wide range of well-known mobility management protocols, this dissertation proposes a generic mathematical model that supports the evaluation of figures such as average handoff latency, average number of dropped packets, location update cost and packet delivery cost. The generic model considers dropped control messages and takes different network topologies and mobility scenarios into account. This dissertation also validates the generic mathematical model by comparing its results to simulation results as well as results of real testbeds under the same assumptions. The validation proves that the generic model delivers an accurate evaluation of the performance in low-loaded networks. The accuracy of the model remains acceptable even under high loads. The validation also shows that simulation results lie in a range of 23 %, while results of real testbeds lie in a range of 30 % of the generic model?s results. To simplify the analysis using the generic mathematical model, 4 new tools are developed in the scope of this work. They automate the parameterization of mobility protocols, network topologies and mobility scenarios. This dissertation also evaluates the new proposal in comparison to well-known approaches (e.g. Mobile IP, Handoff-Aware Wireless Access Internet Infrastructure (HAWAII), etc.) by means of the generic mathematical model as well as simulation studies modeled in the Network Simulator 2. The evaluation shows that MIFA is a very fast protocol. It outperforms all studied protocols with respect to the handoff latency and number of dropped packets per handoff. MIFA is suitable for low as well as high speeds. Moreover, there is no significant impact of the network topology on its performance. A main advantage of MIFA is its robustness against the dropping of control messages. It remains able to achieve seamless handoffs even if a dropping occurs. The performance improvement is achieved, however, at the cost of introducing new control messages mainly to distribute data concerning mobile terminals to neighbor subnets. This results in more location update cost than that resulting from the other mobility management protocols studied. Due to excluding any constraints on the network topology, MIFA generates the same packet delivery cost as Mobile IP and less than other protocols.An additional focus of this dissertation is the development of an adaptive eLearning environment that personalizes eLearning contents conveying the topics of this dissertation depending on users? characteristics. The goal is to allow researchers to quickly become involved in research on mobility management, while learners such as students are able to gain information on the topics without excess detail. Analyses of existing eLearning environments show a lack of adaptivity support. Existing environments focus mainly on adapting either the navigation or the presentation of contents depending on one or more selected users? characteristics. There is no environment that supports both simultaneously. In addition, many user characteristics are disregarded during the adaptivity process. Thus, there is a need to develop a new adaptive eLearning environment able to eliminate these drawbacks. This dissertation, therefore, designs a new Metadata-driven Adaptive eLearning Environment (MAeLE). MAeLE generates personalized eLearning courses along with building an adequate navigation at run-time. Adaptivity depends mainly on providing contents with their describing metadata, which are stored in a separate database, thus enabling reusing of eLearning contents. The relation between the metadata that describe contents and those describing learners are defined accurately, which enables a dynamic building of personalized courses at run-time. A prototype for MAeLE is provided in this dissertation as well