Dynamic Predictive Modeling Approach of User Behavior in Virtual Reality based Application
01 Jul 2019-pp 57-62
TL;DR: This paper addresses dynamic modeling of user behavior approach in an interactive VR based application and suggests both neural networks are suitable for performing prediction which can be used to achieve an improved feeling of presence while reducing required high computational power.
Abstract: Virtual Reality (VR) is considered to be a powerful modern medium for immersive data visualization and exploration. However, few studies have proposed solutions to complement data visualization in immersive environment considering the user's behavior. This paper addresses dynamic modeling of user behavior approach in an interactive VR based application. In this application, real-time data communication is employed to track accurate location and orientation of head mounted display device worn by the user. In our experiment, we use example of collected data and provide a methodology to predict next movements of the user by using nonlinear autoregressive (NAR) and location in the application by the nonlinear autoregressive neural network with exogenous inputs (NARX). Results suggest both neural networks are suitable for performing prediction which can be used to achieve an improved feeling of presence while reducing required high computational power. Data analysis part of the research is also linked to human behaviors to improve studies which are usually performed by traditional survey techniques.
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TL;DR: In this article, the authors compared two different approaches to track a person in indoor environments using a locating system based on BLE technology with a smartphone and a smartwatch as monitoring devices.
Abstract: In this work we performed a comparison between two different approaches to track a person in indoor environments using a locating system based on BLE technology with a smartphone and a smartwatch as monitoring devices. To do so, we provide the system architecture we designed and describe how the different elements of the proposed system interact with each other. Moreover, we have evaluated the system’s performance by computing the mean percentage error in the detection of the indoor position. Finally, we present a novel location prediction system based on neural embeddings, and a soft-attention mechanism, which is able to predict user’s next location with 67% accuracy.
11 citations
TL;DR: This paper uses the Kasteren dataset for intelligent environments, which is one of the most widely used datasets in the areas of activity recognition and behavior modeling and tests multiple model architectures to ascertain the best approach to using embeddings for behavior modeling.
Abstract: Behavior modeling has multiple applications in the intelligent environment domain. It has been used in different tasks, such as the stratification of different pathologies, prediction of the user actions and activities, or modeling the energy usage. Specifically, behavior prediction can be used to forecast the future evolution of the users and to identify those behaviors that deviate from the expected conduct. In this paper, we propose the use of embeddings to represent the user actions, and study and compare several behavior prediction approaches. We test multiple model (LSTM, CNNs, GCNs, and transformers) architectures to ascertain the best approach to using embeddings for behavior modeling and also evaluate multiple embedding retrofitting approaches. To do so, we use the Kasteren dataset for intelligent environments, which is one of the most widely used datasets in the areas of activity recognition and behavior modeling.
4 citations
22 Jun 2020
TL;DR: This paper addresses an architecture that is designed to implement a feedback mechanism and consists of a data communication tool, a relevant immersive environment and third party software, and a solution for observing the user’s behavior.
Abstract: Virtual Reality (VR) is a powerful modern medium. Immersive qualities of VR enables the design of efficient educational, industrial and training solutions. The advent of low-cost head-mounted display (HMD) devices made this technology accessible at large and featured VR with possibilities to monitor interactions and user’s motion. However, due to lack of real–time data communication and collection at present, it is still a challenge to obtain a feedback mechanism related the user’s behavior. This paper addresses an architecture that is designed to implement a feedback mechanism. The architecture consists of a data communication tool, a relevant immersive environment and third party software. The communication tool is used to transmit data with different sample rates in real-time. The feedback mechanism of designed architecture is presented through a number of case studies. A solution for observing the user’s behavior is also presented in this paper with the motivation to complement analysis of performed activities and subjective feedback towards VR based applications.
4 citations
Cites methods from "Dynamic Predictive Modeling Approac..."
...We also conduct an experiment with f = 10Hz sampling rate of data collection in order to provide a methodology to apply dynamic modeling [34]....
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TL;DR: In this article, the authors propose a solution to solve the problem of scalar scalar clustering, i.e., scalar-clustering, and linear clustering.
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05 Jul 2022
TL;DR: This paper proposes using two different quantum computing algorithms in order to predict human behavior: Quantum Kernel Alignment and Quantum Support Vector Machines and shows that those algorithms outperform other traditional machine learning algorithms in this task.
Abstract: As quantum computing technologies become more mature, their applicability increases. One of the main challenges in intelligent environments is to correctly model and ascertain the users' behavior in order to react to it and cater to their needs. One of the main challenges in human behavior modeling is predicting the users' next actions. In this paper we propose using two different quantum computing algorithms in order to predict human behavior: Quantum Kernel Alignment and Quantum Support Vector Machines. Our experiments show that those algorithms outperform other traditional machine learning algorithms in this task. We also present a study that analyzes the influence of qubit noise in the performance of the quantum approach. This helps to understand how the accuracy of the quantum computing algorithms will increase as the underlying hardware matures and qubit noise is reduced.
1 citations
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