Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

An inertial motion capture framework for constructing body sensor networks

Motion capture is the process of measuring and subsequently reconstructing the movement of an animated object or being in virtual space. Virtual reconstructions of human motion play an important role in numerous application areas such as animation, medical science, ergonomics, etc. While optical motion capture systems are the industry standard, inertial body sensor networks are becoming viable alternatives due to portability, practicality and cost. This thesis presents an innovative inertial motion capture framework for constructing body sensor networks through software environments, smartphones and web technologies. The first component of the framework is a unique inertial motion capture software environment aimed at providing an improved experimentation environment, accompanied by programming scaffolding and a driver development kit, for users interested in studying or engineering body sensor networks. The software environment provides a bespoke 3D engine for kinematic motion visualisations and a set of tools for hardware integration. The software environment is used to develop the hardware behind a prototype motion capture suit focused on low-power consumption and hardware-centricity. Additional inertial measurement units, which are available commercially, are also integrated to demonstrate the functionality the software environment while providing the framework with additional sources for motion data. The smartphone is the most ubiquitous computing technology and its worldwide uptake has prompted many advances in wearable inertial sensing technologies. Smartphones contain gyroscopes, accelerometers and magnetometers, a combination of sensors that is commonly found in inertial measurement units. This thesis presents a mobile application that investigates whether the smartphone is capable of inertial motion capture by constructing a novel omnidirectional body sensor network. This thesis proposes a novel use for web technologies through the development of the Motion Cloud, a repository and gateway for inertial data. Web technologies have the potential to replace motion capture file formats with online repositories and to set a new standard for how motion data is stored. From a single inertial measurement unit to a more complex body sensor network, the proposed architecture is extendable and facilitates the integration of any inertial hardware configuration. The Motion Cloud’s data can be accessed through an application-programming interface or through a web portal that provides users with the functionality for visualising and exporting the motion data

Integration of FM and asset management expertise in digital 3D building models

Purpose: The research establishes a Conceptual Process Model (CPM) as shown in Figure 1 which shows how Facility Management (FM) and Asset Management (AM) know-how, 3D laser scanning and Building Information Modelling (BIM) can be combined with virtual design and simulation techniques to help managers make better decisions about feasibility report options and to add value and optimize existing buildings performance and quality. Design methodology and approach: Mixed methods were used including a review of BIM literature and industry best practice. Seven semi-structured interviews were held with stakeholders from different stages in the BIM process. The initial CPM was subsequently refined during the research project based on feedback from the interviews. The 3D laser scanning element of the CPM was tested using two ZHAW university buildings and the findings triangulated with a feedback mechanism to further improve the model. Originality and findings: The findings helped to develop a model which can be used by key stakeholders as a guide when considering the integration of FM and AM know-how, with 3D scanning in the creation of a BIM model for existing buildings, which constitute approximately 98% of the building stock. The focus is on combining existing know-how with the BIM process and simulation techniques to identify, simulate and evaluate the best building improvement options for feasibility reports prior to a decision to proceed. The CPM meets the need to develop a workflow with a focus on digitalisation of the existing built environment and creation of appropriate BIM model(s). The models can then be used for simulation purposes looking at cost benefit optimisation, energy efficiency, life cycle costing (LCC) etc. as well as creating virtual walk through models that can be viewed by end users, Facility Managers (FMs) and Asset Managers (AMs) to improve workplace environments and FM and AM operation

Human motion analysis and simulation tools: a survey

Computational systems to identify objects represented in image sequences and tracking their motion in a fully automatic manner, enabling a detailed analysis of the involved motion and its simulation are extremely relevant in several fields of our society. In particular, the analysis and simulation of the human motion has a wide spectrum of relevant applications with a manifest social and economic impact. In fact, usage of human motion data is fundamental in a broad number of domains (e.g.: sports, rehabilitation, robotics, surveillance, gesture-based user interfaces, etc.). Consequently, many relevant engineering software applications have been developed with the purpose of analyzing and/or simulating the human motion. This chapter presents a detailed, broad and up to date survey on motion simulation and/or analysis software packages that have been developed either by the scientific community or commercial entities. Moreover, a main contribution of this chapter is an effective framework to classify and compare motion simulation and analysis tools

A Framework to Generate and Label Synthetic/Real Video Data to Feed Temporal Segment Networks

In this project, we propose an action prediction and a data generation pipeline. While, the former makes use of Deep Learning, the latter results in a pipeline that makes possible the generation of real and synthetic data. Moreover, to feed the deep learning method a large amount of annotated data is needed. For this purpose an action tagging tool is also featured. Furthermore, in order to supply the lack of data, we have also proposed a video data augmentation pipeline for action recognition purposes. While the 3DPLab team developed a photorealistic synthetic data generator called UnrealRox, we will use this system working with some sequences recorded with a mocap to generate the necessary synthetic data. We have generated a total of 5 different useful sequences with a complex setup of 3 kinects and a capture motion suit. Finally, we have deployed and tested the novel Temporal Segment Network with the state of the art Action Recognition dataset UCF-101

Paralympic VR Game Immersive Game using Virtual Reality Technology

Throughout the years virtual reality has been used for a wide range of applications, and several types of research have been made in order to improve its techniques and technology. In the last few years, the interest in virtual reality has been increasing partially due to the emergence of cheaper and more accessible hardware, and the increase in content available. One of the possible applications for virtual reality is to lead people into seeing situations from a different perspective, which can help change opinions. This thesis uses virtual reality to help people better understand paralympic sports by allowing them to experience the sports’ world from the athletes’ perspective. For the creation of the virtual environment, both computer-generated elements and 360 video are used. The integration of these two components presented a challenge to explore. This thesis focused on wheelchair basketball, and a simulator of this sport was created resorting to the use of a game engine (Unity 3D). For the development of this simulator, computer-generated elements were built, and the interaction with them implemented. Besides allowing the users to play the sport as if they are in the athlete’s shoes, users can also watch 360 videos in which explanations of the modality (rules and classification) are presented. They are also capable of interacting with some of these videos through virtual elements that are placed over the videos. User studies were conducted to evaluate the sense of presence, motion sickness and usability of the system developed. The results were positive although there are still some aspects that should be improved