In this paper, we propose a user experience environment model for quantifying user experience information in a virtual space. This model consists of a spatial model, a user activity model, and an object model. The spatial and the object models represent a space containing the objects the user is using. The user activity model contains information on the experience information that a user accumulates while using the space. Experiments to quantify the user experience in the high complexity space require a lot of time and money. Thus, this approach can reduce the cost of obtaining user experience information by composing a virtual space similar to the real space and predicting the user activity in this space. In conclusion, we propose a simulator for collecting information on user experience environments and for predicting user activity through knowledge gained using the collected data.

Introduction

The various objects surrounding human beings to smart production environment to control a large amount of production volume from everyday life is based on the network infrastructure, through the connection between the data, not only the industry, causing a variety of changes across society and that is being a hyper-connected society.[1,2] The following terms are used to explain this networked social phenomenon: machine-to-machine(M2M), Internet of Things(IoT), and Internet of Everything(IoE).[3-5] Among the above terms, IoE is gaining the most traction. These terms represent an overarching concept that offers advanced connectivity for devices, systems, and services that goes beyond the concept of mere technology.[6] The purpose of IoE is to deliver real-world spatial information to the user in real time, and to deliver categorized information to a target site quickly and accurately with four components: human, process, data, and objects. The goal of this paper is to realize people oriented value using the process described above. In order to do so, we need to understand the large amount of data generated from the experience environments surrounding humans.[7] This means that we should quantify the user experience for adequate analysis of both human behavior and spatial interactions. Therefore, we reviewed various studies on both the cognitive aspects of human-environment interactions and human-computer interactions. In doing this, we operated under the assumption that the concept of space cannot be explained without interactions with humans. These interactions can be explained using adequate amounts of data to understand human behavior. The contents and form of these data can correspond to the characteristics of each space. This means that each space may exist in a variety of forms through interactions with real-world information and human experience information.[8]