Evaluating the impact of network delay on user quality of experience of an interactive virtual reality industry 4.0 application

Abstract

The fourth industrial revolution is the name given to the paradigm shift towards further integrating the relationship between industrial technologies and the world of computing. The Industry 4.0 ecosystem includes a range of technologies such as the Internet of Things, robotics & automation, big data, machine learning and human-machine interaction. Industry 4.0 demands the development of new fundamental concepts, such as smart manufacturing, to improve efficiency and productivity in the industrial sector. Technologies such as virtual reality (VR) enable human-in-the-loop roles in Industry 4.0. Building human-centred interactive systems will enhance efficiency, human well-being and satisfaction, while at the same time counteracting potential health, safety and performance hazards. Critical to the success of such human-in-the-loop systems is the understanding of the user perceived quality of experience (QoE). QoE is a discipline concerned with understanding the usability and utility of a technology in a particular context or environment. The focus of this MSc research is to understand the impact of network delay on a user’s QoE whilst interacting with a VR application. In the environment, the user interacts with a virtual representation of a Fanuc injection moulding machine The Industry 4.0 aspect of interest for this research is teleoperation, which is the real-time control of a mechanical unit over a network. A user could, in theory, control a Fanuc across the Internet using a VR environment that is designed to replicate the operation of such a machine. To evaluate the QoE of remotely operating a Fanuc, participants in this research carried out a basic, beginner-level operation task on the Fanuc in a virtual reality environment under both subjective and objective evaluation. The participants were provided with instructions in the VR environment, introducing the participants to the machine and assisting them via instructions as they completed the task. To understand the impact of network delay, one group experienced no network delay, while all other subject groups experienced artificially introduced network delays. Various objective implicit parameters were measured, such as time-to-task-completion, number of controller clicks and biometric measurements, such as heart rate, electro-dermal activity and eye gaze data. Subjective explicit metrics were captured post-test, using a questionnaire, to measure self-reported immersion and engagement with the environment. The results suggest that, although participants experience a mild drop in QoE as a result of network delay, they are tolerant of delays of up to 3000ms, with no significant deterioration in perceived usability of the virtual environment.