Effectiveness of VR-based training on improving construction workers’ knowledge,skills, and safety behavior in robotic teleoperation

The emergence of construction robotics and automation has produced an urgent and vast need for construction workers to reskill and upskill for the future of work. Virtual Reality (VR)-based training has been considered and investigated as a safe and cost-effective training method that allows workers to be exposed to hazardous tasks with negligible actual safety risks in comparison to existing training methods (hands-on, lecture-based, apprenticeship training). This paper aims to investigate the impact of VR-based training on construction workers’ knowledge acquisition, operational skills, and safety behavior during robotic teleoperation compared to the traditional in-person training method. Fifty construction workers were randomly assigned to complete either VR-based or in-person training for operating a demolition robot. We used quantitative and qualitative data analyses to answer our research questions. Our results indicate that VR-based training was associated with a significant increase in knowledge, operational skills, and safety behavior compared to in-person training. Our findings suggest that VR-based training not only provides a viable and effective option for future training programs but a valuable option for construction robotics safety and skill training.     

A mobile VR-based respiratory biofeedback game to foster diaphragmatic breathing

Virtual reality (VR) has become popular in mental health research. Several studies have explored the use of VR in the context of biofeedback protocols. In the present paper, we report on the development and evaluation of a VR-based respiratory biofeedback game to foster diaphragmatic breathing. The game integrates respiratory biofeedback, restorative VR and gamification. The game is designed to run on a mobile, all-in-one VR headset. Notably, an integrated VR hand controller is utilized as a sensor to detect respiration-induced movements of the diaphragm. In a longitudinal within-subjects study, we explored the feasibility of the game and tested the effectiveness of six training sessions. Participants reported a pleasant user experience. Moreover, the results show that the brief VR-based breathing training increased perceived breath awareness, improved diaphragmatic breathing, increased relaxation, decreased perceived stress, reduced symptoms of burnout and boosted relaxation-related self-efficacy. Future studies need to address the generalizability and long-term stability of the results, compare the approach with existing treatments and fine-tune the training components.

     

Participants matter: Effectiveness of VR-based training on the knowledge,trust in the robot,and self-efficacy of construction workers and university students

Virtual Reality (VR)-based training has gained attention from the scientific community in the Architecture, Engineering, and Construction (AEC) industry as a cost-effective and safe method that eliminates the safety risks that may impose on workers during the training compared to traditional training methods (e.g., in-person hands-on training, apprenticeship). Although researchers have developed VR-based training for construction workers, some have recruited students rather than workers to understand the effect of their VR-based training. However, students are different from construction workers in many ways, which can threaten the validity of such studies. Hence, research is needed to investigate the extent to which the findings of a VR-based training study are contingent on whether students or construction workers were used as the study sample. This paper strives to compare the effectiveness of VR-based training on university students’ and construction workers’ knowledge acquisition, trust in the robot, and robot operation self-efficacy in remote operation of a construction robot. Twenty-five construction workers and twenty-five graduate construction engineering students were recruited to complete a VR-based training for remote operating a demolition robot. We used quantitative analyses to answer our research questions. Our study shows that the results are dependent on the target sample in that students gained more knowledge, whereas construction workers gained more trust in the robot and more self-efficacy in robot operation. These findings suggest that the effectiveness of VR-based training on students may not necessarily associate with its effectiveness on construction workers.     

Perceived usefulness of,engagement with,and effectiveness of virtual reality environments in learning industrial operations: the moderating role of openness to experience

The development of virtual reality (VR) in enhancing the effectiveness of the learning process, with its interactive, immersive, and intuitive pedagogical environment, has become a necessity for corporations with increasingly complex operations. However, VR users’ perceptions, openness and learning effectiveness are seldom comprehensively evaluated, particularly in learning complex industrial operations. In this study, grounded in the technology acceptance model, a moderated mediation model of perceived usefulness, ease of use, openness to experience, and engagement in VR-based learning was developed. The model was empirically validated using responses collected from 321 users who were trained on aircraft and cargo terminal operations powered by a novel VR-based learning platform. A survey to measure openness to experience and a pre-training performance test were carried out, followed by a post-training survey of learners’ intrinsic factors, including the influence of perceived usefulness, openness to experience, and attitude towards learning. The study revealed that learners with an open attitude towards experiencing new technology tend to perceive VR technology as a useful platform for training. In addition, the learners with more positive views of VR technology-supported training were more engaged in learning.

     

Real-time Unsupervised Segmentation of human whole-body motion and its application to humanoid robot acquisition of motion symbols

An interactive loop between motion recognition and motion generation is a fundamental mechanism for humans and humanoid robots. We have been developing an intelligent framework for motion recognition and generation based on symbolizing motion primitives. The motion primitives are encoded into Hidden Markov Models (HMMs), which we call “motion symbols”. However, to determine the motion primitives to use as training data for the HMMs, this framework requires a manual segmentation of human motions. Essentially, a humanoid robot is expected to participate in daily life and must learn many motion symbols to adapt to various situations. For this use, manual segmentation is cumbersome and impractical for humanoid robots. In this study, we propose a novel approach to segmentation, the Real-time Unsupervised Segmentation (RUS) method, which comprises three phases. In the first phase, short human movements are encoded into feature HMMs. Seamless human motion can be converted to a sequence of these feature HMMs. In the second phase, the causality between the feature HMMs is extracted. The causality data make it possible to predict movement from observation. In the third phase, movements having a large prediction uncertainty are designated as the boundaries of motion primitives. In this way, human whole-body motion can be segmented into a sequence of motion primitives. This paper also describes an application of RUS to AUtonomous Symbolization of motion primitives (AUS). Each derived motion primitive is classified into an HMM for a motion symbol, and parameters of the HMMs are optimized by using the motion primitives as training data in competitive learning. The HMMs are gradually optimized in such a way that the HMMs can abstract similar motion primitives. We tested the RUS and AUS frameworks on captured human whole-body motions and demonstrated the validity of the proposed framework.     

Hybrid motion graph for character motion synthesis

ObjectiveThis paper proposes a novel framework of Hybrid Motion Graph (HMG) for creating character animations, which enhances the graph-based structural control by motion field representations for efficient motion synthesis of diverse and interactive character animations.MethodsIn HMG framework, the motion template of each class is automatically derived from the training motions for capturing the general spatio-temporal characteristics of an entire motion class. Typical motion field for each class is then constructed. The smooth transitions among motion classes are then generated by interpolating the related motion templates with spacetime constraints. Finally, a hybrid motion graph is built by integrating the separate motion fields for each motion class into the global structural control of motion graph through smooth transition.ResultsIn motion synthesis stage, a character may freely ‘switch’ among different motion classes in the hybrid motion graph via smooth transitions between motion templates and ‘flow’ within each class through the continuous space of motion field with agile and the continuous control process.ConclusionExperimental results show that our framework realizes the fast connectivity among different motion classes and high responsiveness and interactivity for creating realistic character animation of rich behaviors with limited motion data and computational resources.     

Virtual reality in K-12 and higher education: A systematic review of the literature from 2000 to 2019

This study is a systematic review of 20 years of research on the usage of virtual reality (VR) in K-12 and higher education settings, which aims to consolidate, evaluate, and communicate evidence that can inform both the theory and practice of VR-based instruction. A total of 149 articles were selected from three major academic databases using search strings and manual screening protocols. The literature analysis emphasized four interrelated aspects of VR-based instruction: instructional context, instructional design, technological affordances, and research findings. The results revealed evolving trends in the VR literature in terms of publication patterns, pedagogical assumptions, equipment usage, and research methodologies, as well as the contextual factors behind VR adoption in education. Additionally, a meta-analysis was conducted to examine the efficacy of VR-based instruction, with results indicating an overall medium effect and several moderating factors. Finally, practical implications and a future research agenda for VR-based instruction are discussed.     

How does desktop virtual reality enhance learning outcomes? A structural equation modeling approach

This study examined how desktop virtual reality (VR) enhances learning and not merely does desktop VR influence learning. Various relevant constructs and their measurement factors were identified to examine how desktop VR enhances learning and the fit of the hypothesized model was analyzed using structural equation modeling. The results supported the indirect effect of VR features to the learning outcomes, which was mediated by the interaction experience and the learning experience. Learning experience which was individually measured by the psychological factors, that is, presence, motivation, cognitive benefits, control and active learning, and reflective thinking took central stage in affecting the learning outcomes in the desktop VR-based learning environment. The moderating effect of student characteristics such as spatial ability and learning style was also examined. The results show instructional designers and VR software developers how to improve the learning effectiveness and further strengthen their desktop VR-based learning implementation. Through this research, an initial theoretical model of the determinants of learning effectiveness in a desktop VR-based learning environment is contributed.     

A study of cybersickness and sensory conflict theory using a motion-coupled virtual reality system

Sensory conflict theory explains that motion sickness in virtual reality (VR) systems can be caused due to the mismatch between visual and vestibular senses. This study examines whether coupling physical motions to visual stimuli in VR could reduce this discomfort. A motion-coupled VR system developed on a motion platform, providing vestibular cues to supplement visual roll from a head-mounted display (HMD), was used. Three conditions were tested: visual rotation only (stationary), visual-physical motion synchronised (synchronous), and vestibular motion with a self-referenced visual environment. Results show that when users are placed under a visual-vestibular synchronised condition, their subjective miserable score of cybersickness decreased while their comfort level of the overall experience increased. This indicates that a motion-coupled system, if integrated seamlessly in VR, could mitigate cybersickness symptoms.     

Methods engineering: Using rapid prototype and virtual reality techniques

Methods engineering (ME) is the most traditional area of industrial engineering. In spite of its importance, the tools and techniques in use have hardly changed in the last 30 years. Lately, tools based on rapid prototyping (RP) and virtual reality (VR) techniques have been developed. This article presents the concept of incorporating these technologies with the ME process. A framework for an RP and VR-based ME system is introduced. Two preliminary attempts to implement RP technology in ME are reported. © 1997 John Wiley & Sons, Inc.     

基于约束物理系统和运动捕捉的人体运动仿真

为了能在基于PC平台的虚拟现实中实现更具真实感的人体运动仿真,该文提出一种改进的约束求解方法,将人体建模为基于约束关节树的骨骼结构。约束模型的求解采用基于线性互补问题的求解方法,同时结合相应的运动捕捉数据。实验结果表明,该方法所获得的运动数据不仅能满足交互式要求的帧速率,而且由于算法基于人体运动捕捉原始数据,使得虚拟人体与环境进行实时交互运动的过程更加真实。仿真结果以每帧的三维图像表示,可以形象地显示每秒60帧的人体运动。     

集成虚拟现实环境在工程设计分析中的应用 *

虚拟现实技术在工程设计与分析上有很大的应用潜力。可是 ,虚拟环境的创建时间长、成本高,极大地限制了虚拟现实技术在工程上的应用。为此 ,提出了虚拟现实脚本生成器的概念,将虚拟现实技术与过程的设计、分析集成化,创造性地提出了一种自上而下的设计方法———VR-IEDA,以便高效、快速地创建虚拟原型,提高计算机虚拟复杂系统工程的性价比。在 VR-IEDA中复杂系统的结构和行为被计算机捕捉并自动生成可执行的 VR仿真代码 ,从而减少了创建虚拟环境的时间,便利了基于仿真的过程分析和设计。最后 ,以武器系统的维修过程为     

An operational matrix based on Chelyshkov polynomials for solving multi-order fractional differential equations

Recent advancement in learning and teaching methodology experimented with virtual reality (VR)-based presentation form to create immersive learning and training environment. The quality of such educational VR applications not only relies on the virtual model, but the 2D presentation materials such as text, diagrams and figures. However, manual designing or seeking these educational resources is both labor intensive and time-consuming. In this paper, we introduce a new automatic algorithm to detect and extract presentation slides in educational videos, which will provide abundant resources for creating slide-based immersive presentation environment. The proposed approach mainly involves five core components: shot boundary detection, training instances collection, shot classification, slide region detection and slide transition detection. We conducted comparison experiment to evaluate the performance of the proposed method. The results indicate that, in comparison with peer method, the proposed method improves the precision of slide detection from 81.6 to 92.6% and recall from 74.7 to 86.3% on average. With the detected slides, content analyzer can be employed to further extract reusable elements, which can be used for developing VR-based educational applications.

     

污水处理厂虚拟现实教学软件开发及应用

为解决学生在现实中对污水处理厂缺乏设备操作、观察工程细节的机会而使得实 践教学效果达不到人才培养目标的问题,基于虚拟现实(VR)技术,利用 UE4 和 C4D,开发一套 完整污水处理厂 VR 教学软件,包含 1 个虚拟实验中心及 13 个污水处理构筑物案例。学生通过 软件可体验污水处理的完整流程,掌握污水处理工艺原理、构筑物构造和设备布置,操作工程 设备,控制主要工艺参数。通过网络+课堂教学、VR 实践教学和考核反馈,完成对污水处理厂 的全面认知。基于 VR 技术的污水处理厂教学软件与传统教学形成优势互补,为环境工程实践 教学效果不佳的问题提供良好的解决途径。根据调查结果分析,该 VR 教学软件达到环境工程 实践教学目的。     

Fun and Accurate Static Balance Training to Enhance Fall Prevention Ability of Aged Adults: A Preliminary Study

Fall prevention is critical for aged adults as falling down has been cited as the number one cause of accidental death in aged adults. Static balance training is an effective method of increasing the balancing capacity in aged adults and reducing their fall risk. This article discusses a method that combines virtual reality (VR) technology with motion‐capture devices for aged adults to practice static balance training in a fun and accurate environment. The aged adults wore motion‐capture devices to interactively manipulate a three‐dimensional (3D) human model (HM) in a virtual environment to follow a 3D posture model (PM). The proposed method was implemented using commercial, off‐the‐shelf 3D tools. A preliminary usability study was conducted, and the results show that the parameters of PM design will affect the aged adult's posture control performance. Our method has several advantages over the currently existing approaches. First, by adjusting the PM parameters, different posture control programs could be designed to support customized static balance training. Second, collision detections between the HM and PM could be used to accurately monitor the posture control process and to interactively provide feedback to the trainee. Third, posture control quality could be quantitatively evaluated by analyzing the collision data between the HM and PM. The proposed method could be used to develop a fun, accurate, and customized static balancing capacity training environment for aged adults. © 2012 Wiley Periodicals, Inc.     

Spatio-temporal structure of human motion primitives and its application to motion prediction

This paper proposes a novel approach to structuring behavioral knowledge based on symbolization of human whole body motions, hierarchical classification of the motions, and extraction of the causality among the motions. The motion patterns are encoded into parameters of corresponding Hidden Markov Models (HMMs), where each HMM abstracts the dynamics of motion pattern, and hereafter is referred to as “motion symbol”. The motion symbols allow motion recognition and synthesis. The motion symbols are organized into a hierarchical tree structure representing the property of spatial similarity among the motion patterns, and this tree is referred to as “motion symbol tree”. Seamless motion is segmented into a sequence of motion primitives, each of which is classified as a motion symbol based on the motion symbol tree. The seamless motion results in a sequence of the motion symbols, which is stochastically represented as transitions between the motion symbols by an N-gram model. The motion symbol N-gram model is referred to as “motion symbol graph”. The motion symbol graph extracts the temporal causality among the human behaviors. The integration of the motion symbol tree and the motion symbol graph makes it possible to recognize motion patterns fast and predict human behavior during observation. The experiments on a motion dataset of radio calisthenics and on a large motion dataset provided by CMU motion database validate the proposed framework.     

Evaluating the attitudes of different trainee groups towards eye tracking enhanced safety training methods

The primary approach to prevent injuries and fatalities on construction sites is to provide effective safety training to all site personnel. In order to develop an effective safety training method, it is critical to consider the attitudes of participants towards the training received. In this regard, the eye movements of three groups of trainees during both a conventional safety training method and a Virtual Reality (VR)-based training method were monitored. The eye-tracking data from five engineers, five workers, and five engineering students were collected to measure their time to first fixation and fixation durations of the three groups of AOIs (Areas of Interests). Accordingly, we identified their reaction to the training stimulus and measured their concentration and adaptation level. The data were analyzed using Mixed ANOVA with the between-subject variable of three different trainee groups and the within-subject variable of training types. The statistical findings showed that the engineers concentrate faster than the workers and the students during all sessions of safety training, and all participants spend more time exploring different AOIs during VR-based safety training. Therefore, it is concluded that the different backgrounds of study groups (e.g., field experience, site role, game experience, and education) play a crucial role in the adaptation and concentration level of the trainees during safety training sessions.     

Crowd simulation for emergency response using BDI agents based on immersive virtual reality

This paper presents a novel methodology involving a Virtual Reality (VR)-based Belief, Desire, and Intention (BDI) software agent to construct crowd simulation and demonstrates the use of the same for crowd evacuation management under terrorist bomb attacks in public areas. The proposed BDI agent framework allows modeling of human behavior with a high degree of fidelity. The realistic attributes that govern the BDI characteristics of the agent are reverse-engineered by conducting human-in-the-loop experiments in the VR-based Cave Automatic Virtual Environment (CAVE). To enhance generality and interoperability of the proposed crowd simulation modeling scheme, input data models have been developed to define environment attributes (e.g., maps, demographics, evacuation management parameters). The validity of the proposed data models are tested with two different evacuation scenarios. Finally, experiments are conducted to demonstrate the effect of various crowd evacuation management parameters on the key performance indicators in the evacuation scenario such as crowd evacuation rate and densities. The results reveal that constructed simulation can be used as an effective emergency management tool.     

People tracking based on motion model and motion constraints with automatic initialization

Human motion analysis is currently one of the most active research topics in computer vision. This paper presents a model-based approach to recovering motion parameters of walking people from monocular image sequences in a CONDENSATION framework. From the semi-automatically acquired training data, we learn a motion model represented as Gaussian distributions, and explore motion constraints by considering the dependency of motion parameters and represent them as conditional distributions. Then both of them are integrated into a dynamic model to concentrate factored sampling in the areas of the state-space with most posterior information. To measure the observation density with accuracy and robustness, a pose evaluation function (PEF) combining both boundary and region information is proposed. The function is modeled with a radial term to improve the efficiency of the factored sampling. We also address the issue of automatic acquisition of initial model pose and recovery from severe failures. A large number of experiments carried out in both indoor and outdoor scenes demonstrate that the proposed approach works well     

虚拟现实技术在柔性上肢康复机器人中的应用

根据末端牵引式和外骨骼式上肢康复机器人的特性,研制了一种新型的柔性上肢康复机器人。机器人的康复训练系统结合了主动和被动模式的要素,将虚拟现实（VR）技术引入上肢康复机器人,通过现实环境中的光学3D位置捕获以及VR环境中的3D位置感知,设计了虚拟动态模型交互性节点和碰撞检测实验,实现虚拟现实交互,提高虚拟模型运动实时效果和上肢康复训练精度。VR和新颖的康复机器人的集成为具有特定任务的患者提供了有效的训练。