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Anatomical evaluation of an infant's head shape is an important task in clinical practice to diagnose cranial deformities. In the first stage of diagnosis, the current practice mainly relies on visual head inspection. However, this is a difficult task and subjective between physicians. Thus, medical training is paramount to correctly perform the visual head shape assessment to guarantee a safe diagnosis. The main goal of this work is the development of a prototype of a medical game called Head Shape Inspector to train individuals to perform head shape analysis. During the game, the player categorizes 3D surfaces of infants' heads according to their cranial deformity. Moreover, the game also allows the user to visualize anthropometric measurements of the head, required in clinical practice to quantify the deformities, to aid the categorization process. Preliminary experiments showed that playing the game can improve the visual inspection skills, suggesting the potential of the game to be used for medical training.

In the next few years, new demanding applications will be supported on mobile platforms by reconciling two conflicting requirements: high performance (often with real-time limitations) and low power consumption. The objective of the vipGPU project is to develop hardware and software technology to provide efficient support for two such application scenarios, namely (a) simultaneous localization and mapping (SLAM) in mobile robotics systems, and (b) virtual reality (VR) in portable devices to simulate serious games with emphasis on simulating surgical interventions and medical training in general. In this project, we aim at developing a new heterogeneous platform consisting of hardware accelerators for low power embedded systems optimized (at the hardware and software level) for the implementation of the two applications mentioned above.

Augmented reality may benefit medical training by providing visual and haptic feedback as the user manipulates passive materials. We propose a haptic bracelet for feedback in a needle decompression training task, which allows the user to manipulate a physical needle represented in virtual space. As the user inserts the needle through virtual tissue layers, they receive haptic feedback on the wrist depending on the interaction forces at the needle tip. A virtual training environment was created to provide differing haptic feedback when the needle is inserted in locations with a hidden bone representing collision with a rib during the needle decompression task. A pilot study demonstrated that a user can recognize the location of the virtual rib via this haptic feedback; this motivates future work to use this feedback in a medical training system.

Augmented reality (AR) technology, as a computer simulation technology, combines various technologies such as virtual reality, computer vision, computer network, and human-computer interaction. AR has been widely used in medicine. The introduction of AR can effectively help the doctors to complete preoperative planning, intraoperative guidance, postoperative evaluation, and medical training. Oral medicine is a major branch of modern medicine. AR can enhance the doctor's visual system, making the internal structure of the oral clearer and effectively reducing the difficulty of oral repair/surgery. Real-time tracking, registration, display, and interactive technologies for AR will play an important role in oral medicine. Among them, registration technology has become an important indicator for evaluating the AR system, and it is also the main bottleneck restricting the stability and applicability of the current AR system. Therefore, we reviewed the registration technology of AR in oral medicine. First, we conducted a hot spot analysis of AR keywords based on Citespace. And then, the registration technology is divided into static registration and real-time registration according to the actual clinical application, among which static registration is divided into rigid registration and non-rigid registration. We discussed problems and limitations of static registration and real-time registration in oral applications at this stage. Finally, the future direction of AR registration technology in oral medicine is proposed.

Despite its importance in the real-world, manual (hand) dexterity is often ignored in medical-based virtual training environments that have traditionally focused on cognitive and affective skills development. Psychomotor (technical) skills, particularly those related to manual dexterity, are fundamental to various medical procedures and ignoring them in virtual based training tools can lead to a sub-optimal training experience. Here, we present a novel, consumer-level data glove that provides accurate user interactions involving the proximal and medial phalanges, interactions that are relevant in many manual dexterity tasks. We also outline how this novel data glove is being incorporated into an existing serious gaming platform for anesthesia training that currently focuses on cognitive and affective skills development only. The addition of psychomotor skills development through the adoption of simulated tactile feedback will provide a more complete serious gaming training platform.

Emerging Pattern Mining (EPM) is a data mining task that finds discriminative characteristics between classes or data sets. In this paper, several EPM algorithms were applied to a data set which contains the opinions of the medical staff from a Mexican hospital about the decrease of autopsies. We consider two attributes as class labels: motives for autopsy acceptance and motives for autopsy rejection in order to find aspects like medical training and medical experience that imply that physicians consider reasons for requesting or rejecting autopsies.

The study of human computer interaction in using immersive technologies has become very essential and critical. Immersive technologies have been used significantly in medical education. In the medical field, real-life training is not always possible. The main reasons are cost and safety. Thus, alternatives in providing and achieving a better educational medical training platform are needed. There is a growing interest in using virtual reality (VR) for training and learning in the medical field. The use of virtual reality in educational training may facilitate a successful medical learning process without involving a real human. The technology allows learners to adapt different learning styles in ways that are not suited to traditional teaching. This study aims to investigate efforts and opportunities for using VR technologies for medical training purposes. This paper identifies and summarizes VR tools used for surgical training especially in laparoscopic surgery, arthroscopy and orthopedic surgery. We also present the advantages of medical training using VR.

As a technology allowing an immersion in a digitally created artificial world, virtual reality has increasingly replaced experiments on real-world objects. Thus, the implementation of a virtual reality solution can be very beneficial in education and scientific research fields. Medical simulation fits perfectly into this dynamic of integrating digital tools in the health sciences teaching. Therefore, the Cheikh Zaid Foundation's Medical Simulation Center (CSMFCZ), has focused on educational innovation by developing its own 3D environment. This paper deals with the particular case of an operating room, allowing simulation of different surgical scenarios in order to enrich the digital platform of the CSMFCZ.

The ageing population has led to increasing demand for additional medical staff and training resources. However, the cost of medical training equipment is costly. Considering that augmented reality (AR) technology has been shown to enhance training experience and trainer-trainee interactions in various sectors, the aim of this project is to develop a low-cost AR mobile application framework, which is expandable for use in various medical training modules. On the displays of the trainers' and trainees' mobile devices, the application overlays AR effects that simulate physiological symptoms onto a training manikin. Under the control of the trainer, the effects can be changed for evaluating the trainee's timely responses. Initial trials focused on anesthetic injection-induced rash effects. The application was tested on three mobile platforms under different environmental settings, and was found to function satisfactorily.

SwastyaSIM is a simulation-based, multilingual, interactive learning environment designed to supplement the training of community health workers. It includes medical training and diagnostic tests relevant to common village ailments that are designed to be sensitive to socio-cultural perspectives. This platform supports training, reporting and assessing health workers. Findings from a pilot study with health workers are discussed.

BlueBox is an electronic recorder for code-blue events, a term for dire emergency that calls for an immediate cardiopulmonary resuscitation (CPR) following a cardiac or respiratory arrest. It records all relevant vital signs, including ECG and cardiac rhythm, verbal orders and their execution, chest compression, cardioversion and defibrillation procedures, medications, and labs. The prototype was built and successfully tested on a high-fidelity mannequin in the simulation center and on pigs in the animal lab. The results shows BlueBox to be able to support medical training and quality improvement in code-blue situations and to enhance safety for patients undergoing CPR in hospitals.

In the case of medical schools in Japan, clinical training has been focused on lately. Giving such training, the trainer can only take a short time for each trainee. Moreover, the trainer has to provide one-to-one teaching for each trainee, so the number of classes for practical training in conducting medical interviews is not still enough. In addition, the number of physicians who can provide teaching is not enough. Therefore, classes in practical training become a burden on the trainer. In this paper, we proposed a method for supporting medical-interview training, which is one kind of clinical training given in medical schools. The proposed method uses sensors and smart devices such as smart glasses and tablets. Its usefulness was demonstrated in experiments involving students and physicians at a medical school. The proposed method makes medical training more efficient, gives trainees more chances of conducting medical interviews, and alleviates the load on trainers.

Simulation systems in medical training are designed for residents and others to become more skilled in complex procedures such as surgery. The surgical context for training discussed in this paper is Condylar plating surgery which deals with treating fractures of the femur bone. This fully immersive environment has been developed using the Vive VR platform. Learning studies conducted with orthopedic residents and medical students underscored the potential of adopting such emerging platforms for simulation based training activities.

The traditional Chinese medicine massage has a long history and the efficacy has been wildly acknowledged, especially for childhood diarrhea treatment. As same as the traditional Chinese medicine, there is lack of quantitative evaluation methods for Chinese medicine staff training. With the development of information detection technology, a variety of equipment for the detection of massage techniques has been proposed, which makes it possible to quantify massage operation. However, since there are no embedded sensors, most of the manipulations cannot feedback the operation information of trainee. And the massage training still needs follow the directions of the supervisor. This situation caused the insufficient of medical training on mastering massage skills. In this paper, a massage manipulation with a novel sensor unit embedded is proposed. The sensor unit is composed of four thin film press sensors as a group. Take advantage of the sensor unit, not only the pressure but also the operation sequence can be detected. A testing apparatus mimicking the abdominal muscle tissue is carried out to examine the abilities of proposed sensor unit. Experiments with the apparatus show that the proposed idea of the sensor unit can fulfil the evaluation of massage operation with a simplified structure. All the results lead to the further researches on the quantitative medical education training methods of massage operation.

In video-based training, clinicians practice and advance their skills on surgeries performed by their colleagues and themselves. Although microsurgeries are recorded daily, training centers are lacking the workforce to manually annotate the segments important for practitioners, such as instrument presence and position. In this work, we propose intelligent instrument detection using Convolutional Neural Network (CNN) to augment microsurgical training. The network was trained on real microsurgical practice videos for which human annotators manually gathered a large corpus of instrument positions. Under challenging conditions of highly magnified and often blurred view, the CNN was capable to correctly detect a needle-holder (a dominant tool in suturing practice) with 78.3% accuracy (F-score = 0.84) with recognition speed above 15 FPS. The result is promising in the emerging domain of augmented medical training where instrument recognition presents benefits to the microsurgical training.

Hospital emergencies can be considered casuistic events. The traditional model of medical training to respond to these critical moments has limitations. Given the variety of technical skills and the high-risk environment in which they are applied, the use of simulation of real situations is increasingly used in medicine. This learning model is an opportunity to train simple or complex technical skills, as well as the opportunity to learn from mistakes, without risk for patients. With this article, we aim to quantify the most recurrent simulation based medical education and show its importance as an educational strategy and as a way for improving patient safety.

This paper presents a medical training system using augmented reality, AR. Recognizing an AR marker through a Web camera, computer generated images appear on a real space. We prepared 3D (three-dimensional) anatomical objects to show, and evaluated our system using AR platform. From the experimental result, we found that our system could overlay the digital information on the operator's surrounding real world appropriately.

This paper presents a user interface of 3D (three-dimensional) object converted from CT data in DICOM format using Leap Motion device that can be used as a medical training system for medical students and interns. The formed data can be controlled in a 3D development environment such as Unity. The system consists of Leap Motion device, desktop computer and the displaying software environment. The experimental results show that we can have a desirable control over the rendered object.

A stethoscope is an instrument used in auscultation to transmit signals from patient body to the ear of the healthcare provider. Depending on where the stethoscope is placed on the body, healthcare providers can hear the sounds of the bowels, the lungs and even blood going through an artery. In this paper, a novel efficient device is proposed to train health-care providers. Using the proposed device will enable the healthcare providers to properly use the medical stethoscope. A questionnaire is created to ask healthcare providers about the efficiency of the proposed device. Results show that more than 90% of the healthcare providers find the proposed device an excellent training tool.

Debriefing is the practice of after session review of training performance to enhance self-reflection through feedback. It has been considered as a vital and crucial part of simulation-based medical training. However an accurate, objective and in-depth evaluation of trainee performance has been a significant challenge. To address this we developed a knowledge-based framework in which the criteria of performance for clinical training are distilled into expert rules. These rules are then matched to data streams from a training session to evaluate the strengths and weaknesses of a trainee. We applied the evaluation technology to a dataset collected from two medical cohorts. The cohort characteristics are calculated, visualised, and validated by the medical experts. The cohort analysis results inform decision making at the levels of both the trainers and the enterprise. Trainers can compare and characterise the performance of different student cohorts or the same cohort over a period of time. The course coordinators can use the cohort analysis result to adjust the course design to target the identified common problems in trainee cohorts.

Extracorporeal membrane oxygenation (ECMO) is a high-complexity life-saving procedure where quick and coordinated interventions are required to avoid mortality. ECMO perfusionists and nurses must be well trained to respond to time-critical emergencies, using simulation-based education, which currently rely on rudimental ECMO simulation processes. Implementing a simulation system for such complex medical procedure necessitates a robust, well-coordinated underlying architecture. In this paper, the use of Internet of Things (IoT) service Firebase is presented in the case study of an ECMO simulator. Features of the proposed solution are discussed and justified in the given use case including latency, hardware compatibility, and data management. For the used case of ECMO simulation system, Firebase proved to be a proper communication solution that satisfied the urgent nature of medical training. 2017 IEEE. This publication was made possible by an internal Qatar University grant and UREP grant #19-062-2-026 from the Qatar National Research Fund (a member of Qatar foundation). The statements made herein are solely the responsibility of the authors. Scopus

Emergency Cricothyroidotomy (CCT) is a surgical procedure performed to secure a patient's airway. This high-stakes, but seldom-performed procedure is an ideal candidate for a virtual reality simulator to enhance physician training. For the first time, this study characterizes the force/torque characteristics of the cricothyroidotomy procedure, to guide development of a virtual reality CCT simulator for use in medical training. We analyze the upper force and torque thresholds experienced at the human-scalpel interface. We then group individual surgical cuts based on style of cut and cut medium and perform a regression analysis to create two models that allow us to predict the style of cut performed and the cut medium.

Computer simulation of soft tissues is rapidly becoming an important aspect of medical training, pre-operative planning and intra-operative navigation. Whereas in medical training, generic models are usually employed, both planing and navigation require patient-specific modeling. However, creating a patient-specific model is a challenging task, as many of the mechanical parameters of the organ tissues are unknown. One way of addressing the issue is to extend the deterministic simulation by methods based on stochastic modeling.

The use of medical training simulator is essential during the medical doctor studies. Currently, simulation provides software and hardware solutions to provide near realistic training scenarios to treat patients in controlled environments. At the hardware level there are manikin simulators with numerous vital signs, symptoms and simulated cases, the most advanced ones are even low fidelity when compared to a real person. Associated with characteristics most manikins requires a high investment to purchase, use and maintain in tailored practice laboratories. Similarly to real patients, a simulation manikin can only be treated by a small number of students from 20 people groups, this poses and access challenge as students need to make a line to get the opportunity to practice. This work presents the development a virtual manikin focused on the face that allows complimentary practice when assessing, treating and saving with game elements a convulsive patient. The main reason for this development is to ease the learning process by getting familiar with the procedure on a mobile device offering more realistic models within a serious game environment to engage users in practice and skill.

It has been a strong tendency to use Information Communication Technology (ICT) to construct various education/training systems to help students or other learners master necessary skills more easily, among which such systems with operational practice are particularly welcome in addition to the conventional E-learning ones mainly for obtaining textbook-like knowledge only. In this study, we proposed a medical training system for the operation of Heart-lung machine. Two training contents, i.e., for the basic operations and trouble shooting, respectively, are considered in the system, with more attention paid to how to deal with trouble shooting.

Neurologic examination takes an important role in the physical examination. It requires abundant knowledge with prominent skills. Normally, the medical staffs, especially novices are trained to master the skills and accumulate experiences with several methods such as watching video, training with the simulated patient (SP), and so on. However, the drawbacks of the above methods, such as lack of multi-symptoms, lack of active interactions, etc, limit the training effects. To make up them, several kinds of medical training simulators have been developed to improve training effectiveness. However, most of these simulators just focus on mimicking the symptoms. The could not simulate the pathology of diseases. In this paper, we will propose an elbow robot named WKE-2(Waseda Kyotokagaku Elbow Robot No.2) to simulate the symptoms of motor nerve system for neurologic examination training on elbow force examination. In this paper, the mechanism of the elbow robot and physiological neurological model is described. As a sample, the performance of lead-pipe symptoms is introduced. Taking advantage of the robot, the trainee can get a full training on the examination skills and knowledge as well as the understanding of disease effection. Finally, several experiments are performed to verify the proposed robot. The results lead to the consideration that the approach is worth following in further research. 1

Neurologic examination takes an important role in the physical examination. By now, several methods have been carried out for medical training which bring the benefits for trainee to master the skills and accumulate experiences. However, because of the limits of these methods, the training effectiveness is limited. With the developments of technology, more and more simulators have been launched to improve medical training effectiveness. However, most of these simulators only focus on mimicking the symptoms, not simulating the pathology of diseases. In this paper, we propose an improved motor nerve model which is used in the elbow robot named WKE-2(Waseda Kyotokagaku Elbow Robot No.2). This robot is designed to simulate the disorders of motor nerves to give the trainee a full training on the elbow examination. The motor nerve model mimics the real motor nerve structure. And the functions of each part are designed from analysis of the real functions of each organ. In this robot, the effects of motor nerve system, and various symptoms related to the examination of elbow force, biceps tendon reflex, involuntary action are simulated. Making use of this robot, a systematic training on the skills as well as the understanding of knowledge is provided. Finally, several experiments are performed to verify our proposed system. The experimental results lead to the consideration that the approach is worth following in further research.

Manikin-based medical simulation has been shown to benefit the knowledge, skills and attitudes of the learner, and to impart favourable patient effects. A vital component of any training simulation is the after-session discussion with trainees to debrief their performance. In this study we develop a rule-based debriefing tool for improving the efficacy of medical training sessions. Unlike most existing de-briefing tools, the tool presented here has been designed to reduce medical trainer assessment time and to improve evaluation accuracy through a largely automated evaluation of trainee performance. The developed tool is acknowledged by the School of Medicine of Deakin University as an important advancement in assisting medical trainers carry out the debriefing process effectively and efficiently.

Virtual patient (VP) systems are interactive computer simulations of real-life clinical scenarios for the purpose of medical training, education and assessment. There are limitations with the existing foreign VP systems which make them unsuitable for training indigenous Nigerian medical students. In this study, an Aboriginal Virtual Patient (AVP) system was developed to train medical students. In order to ascertain the feasibility of this research, we validated the effectiveness of aboriginal virtual patient cases over life patient cases, the following metrics were used: accessibility, privacy, accuracy, security, availability and skill-acquired. Questionnaire, developed based on these metrics, was used to interview 80 medical students and 30 doctors from Ladoke Akintola University of Technology (LAUTECH), Ogbomoso. A common generic web-based architecture was then developed for the creation, management and evaluation of web-based aboriginal virtual patient cases. In order to evaluate the performance of the developed Web AVP system over existing foreign Web-based VP system, the following performance measures were used: privacy, security, accessibility, user-friendliness, ease of use and efficiency. Forty five (45) medical students and 25 doctors from LAUTECH, Ogbomoso tested the developed Web AVP system and an existing Web-based VP (called Web SP). Results of the initial feasibility study show that medical learners and trainers considered VP more effective in acquiring clinical skills in all the measures used except for accuracy where real patients have higher rating than VP. Furthermore, the developed Web AVP system has higher performance when compared to Web SP in training Nigerian medical students. The developed system was found to be more suitable for training and assessing medical students; which can eliminate challenges that could arise from using foreign VP systems.

Simulating the deformation of human tissue during medical procedures that employ needles is important for representing the tissue's elastic behavior, thus providing visual realism to simulated medical training. This article presents a method for simulating the deformation of three-dimensional objects that represent human organs through many tissue layers, in which the following physical parameters are included: modulus of elasticity, girth and density. The use of layers allows for the attribution of physical parameters for each type of tissue that composes a specific organ. Furthermore, it is possible to employ some layers to simulate visual appearance of the deformation and others just to simulate tissue resistance when the needle is inserted. Experiments designed to validate this method were performed in a Virtual Reality Framework for puncture procedures. The results were satisfactory compared to the time of response, which was found to be suitable for hap tic interaction. Consequently, it was possible to simulate human tissue deformation with the appropriate visual realism and hap tic realism for medical training.

Virtual vascular interventional operation is a virtual reality technology application in medical training. In this paper, we propose a real-time approach to vascular beating modeling, which can be used in virtual interventional simulation system. First of all, we read out the VTK file of vascular model through VTK function. Then, we choose a center point in a small space and structure the mass-spring model between the center point and other points discreted on the vascular model. After that, we give the center point a force to pull other points. Finally, we set a time callback function to give a beating frequency. Thus, the vascular will beat according to human heart beating rate. Our experimental results show that the method is simple and effective, it can also provide a visually plausible vascular beating effect in a real time way. So it can become an important part of virtual interventional simulation system.

Medical knowledge has increased exponentially in the last decades. Healthcare professionals face a life- time challenge in medical education right from the beginning of medical school. They experience serious difficulties to keep updated. Traditional adult education, largely used in medical training, shows little effectiveness. Problem-based-learning has been proposed as a student-centred pedagogy to overcome failure of traditional medical instruction. A more recent trend is related to the use of serious games. Although, there are still few examples of serious games for medical education, the increased interest shown in the latest years shows that this is an important area and with a lot of potential to explore. In this paper we present the serious game Critical Transport, targeted at medical students with the aim to teach and train the recommendations for the transport of critically ill patients. This game was designed and validated with physicians of an academic hospital and tested during a formal class.

Several approaches for on-line training evaluation in virtual reality simulators have been proposed for performance classification of a trainee in pre-defined classes of training. However, how to choose the best method to evaluate a particular kind of training remains as an unsolved problem. Some evaluation methods are based on neural networks. Particularly, two of them use backpropagation trained multilayer perceptron neural network and evolving fuzzy neural networks. The present paper provides a comparison between these methods using a real case of training simulator for bone marrow harvest. Results obtained are analysed using classification matrices, graphical of classifications mistakes and the Kappa Coefficient. Based on the performance observed, some considerations about the choice between these two methods are provided.

An approach is presented in this paper, combining methods and models that are efficient enough to simulate elastic deformation, obtaining equilibrium between visual and haptic realism. Many medical training computational applications manipulate 3D objects that represent organs and human tissues. These representations, in function of the training requirements for which they are meant, may include parameter such as shape, topology, color, volume texture and, in certain cases, physical properties such as elasticity and stiffness. Based on this model, visual and/or haptic outputs are generated for users, and need to be realistic. In other words, they need to provide the learner with sensations close enough to those they would have if the training were provided with real life objects. However, its computational cost is too high to simultaneously provide visual and haptic realism in real-time. The results from deformation response time are compatible with those required for haptic interaction and the visual results from using meshes composed of a large number of polygons.

Central Venous Catheterization (CVC) involves the insertion of a catheter through the subclavian or jugular vein and into the heart when the patient requires a large influx of drugs. With the target veins so close to the lungs and vital arteries, mistakes during this procedure can be fatal. Unfortunately, it is particularly challenging for students to learn this procedure and is often practiced by novice students on live patients as current simulators are ineffective. Simulators on the market are expensive, inconvenient, and often anatomically inaccurate, leading to improper student training. To address the shortcomings of current simulators, our patent-pending design includes features such as a fully rotatable head and proper anatomical landmarks. Our training simulator is ultrasoundable and can replicate both the pulsing of the arteries and the expanding of the veins under changes in fluid pressure. In addition, our simulator comes with training software allowing for tracking of students as they progress through the curriculum. We are currently performing usability testing of our prototype. Novice residents and expert practicing doctors perform CVC on our simulator for assessment of accuracy, ease of use, and training effectiveness in comparison to commercially available simulators.

Many computational applications for virtual medical training manipulate 3D objects that represent organs and human tissues. These representations, in function of the training requirements, may include parameters such as shape, topology, color, volume, texture and, in certain cases, physical properties such as elasticity and stiffness. An approach is presented in this paper, combining methods and models that are efficient to simulate elastic deformation, obtaining equilibrium between visual and haptic realism. Based on this model, visual and/or haptic outputs are generated for users in order to provide to learner sensations close enough to those they would have if the training were provided with real objects. The results regarding deformation processing time are compatible with those required for haptic interaction and provide adequate visual feedback from using meshes composed of a large number of polygons.

This paper describes the development of a system for medical training of the pleural puncture protocol, built based on virtual reality techniques and performance analysis of the head mounted display (HMD) in this system. The system includes 3D geometric models for a virtual operating room and the virtual patient's anatomy, and the modules required to promote the training of users. One of the contributions of this work is to analyze the impact of the use of HMD, model xSight 3120, and conventional monitors in learning the procedure of pleural puncture through simulations, considering the items: visual discomfort, time to perform tasks and success and error rates.

The evolution of computational techniques currently used in the multimedia and virtual reality fields enables new educational applications in various domains of human knowledge. One of the challenges presented by this new context is the setting of efficient methods for the evaluation of sensory-motor skills acquisition in three-dimensional virtual environments in medical training. A concept model was defined after the conduction of a systematic review process which showed parameters and evaluation methods used in related work. Initial experimental studies were also conducted proving that data from learners' human trajectories can be captured within a framework of medical training. These studies can also contribute with a conceptual model to be used in the development of an evaluation method for sensory-motor skills acquisition in three-dimensional virtual environments.