A Virtual Reality Framework for TrainingIncident First Responders and Digital

Forensic Investigators

Umit Karabiyik(B), Christos Mousas, Daniel Sirota, Takahide Iwai,and Mesut Akdere

Purdue University, West Lafayette, IN 47907, USA{umit,cmousas,dsirota,tiwai,makdere}@purdue.edu

Abstract. This paper presents the basic functionalities of a virtual real-ity framework developed for training first responders and digital foren-sic investigators. The framework is divided into two modules: trainingand evaluation. The framework provides a variety of functionalities andbehaviors that can be assigned to virtual objects and allows trainingand evaluation of crime scenes to be easily customized. During the train-ing module, an individual can be trained to perform various procedures,pipelines, and task execution sequences. After the training is completed,the individual can then be immersed in and interact with a virtual real-ity crime scene by collecting as much evidence as possible. This processallows the system to evaluate the trainee’s performance and the train-ing process in general. In the evaluation process, the developed frame-work captures several types of information regarding the individual’sperformance, including missing evidence, the task execution sequence,and the task completion time. The collected data can then be providedto the trainee or supervisor for evaluation. A simple customizable scoringmethod was also developed and incorporated into the developed frame-work to provide immediate performance feedback to the trainee.

Keywords: Virtual reality · Training · Digital forensics · Firstresponders · Task evaluation

1 Introduction

Digital forensics incident response and investigations are performed by individu-als who receive ongoing training in their technical domains. The required train-ing typically has the following characteristics: (i) lengthy, due to the significantchanges in technology and the proliferation of digital devices; (ii) costly, becauseof the use of commercial products [19]; and (iii) provides less creativity whereradical solutions may be needed. Training for digital forensics incident responseand investigations has, traditionally, been conducted through simple presenta-tions and on-site visits as investigators are required to travel to specific facilitiesto get proper training. However, due to the democratization of virtual realityc© Springer Nature Switzerland AG 2019G. Bebis et al. (Eds.): ISVC 2019, LNCS 11845, pp. 469–480, 2019.https://doi.org/10.1007/978-3-030-33723-0_38

470 U. Karabiyik et al.

technology, it is possible to transfer real-world training to virtual environmentswhile providing an effective and immersive training experience. Thus, this paperpresents a flexible virtual reality framework that provides a number of func-tionalities to allow realistic simulated experiences to incident first respondersand digital forensics investigators. It should be noted that the developed proof-of-concept framework allows the use of various consumer-grade virtual realityheadsets (e.g., Oculus Rift, HTC Vive, Samsung Odyssey, etc.) and hence lendsitself well to standardized distribution for scaling up.

The developed framework is divided into two modules, which are the train-ing and evaluation modules. The training module delivers simple scenarios thatprovide information about practical and cognitive tasks related to the trainingprocess, which is a learning-by-doing experience. The training is designed basedon the best practices published by United States Secret Service [36] and NationalInstitute of Justice [24]. The framework’s evaluation module allows the traineeto navigate within the virtual environment, as well as perform different typesof interactions with objects and tasks located within it to collect the necessaryevidence. Through this immersive experience, in both the training and evalua-tion module of the framework, the trainee is taught the necessary practical andcognitive knowledge and skills required to process a real-life crime scene. Addi-tionally, because the trainee performs all tasks within a virtual environment, wecan include additional features that can later be used to evaluate its performanceand provide feedback. To demonstrate the potential of the developed framework,we present a simple training and evaluation scenario.

The developed framework allows the logical integration of multiple featureswithout requiring an experienced virtual reality developer to have knowledgeof programming or other graphics- and virtual reality-related complex tasks.The framework is generalized to allow the addition of new functionalities androutines with minimal implementation effort. We believe that such a frameworkmay provide insights to future researchers and developers who wish to createvirtual reality training frameworks for various training domains and purposes.We also believe that the developed framework can be deployed in such a waythat other training scenarios can be developed in addition to those related toincident first responds and digital forensics investigations.

2 Literature Review

In the current literature, there is variety of virtual reality applications proposedfor various forensic science problems [6,17,25]. These applications focus on phys-ical evidence that does not imply or support the existence of related digital evi-dence, hence they do not contribute to the digital forensics discipline. However,significant amount of physical evidence can be used to lead digital forensics inves-tigations. Due to the lack of virtual reality training systems for digital forensics,we shift our focus in this section to discuss the literature review for virtual realitysystems in general.

Virtual reality systems utilized for training purposes can be considered pow-erful learning tools [16]. With the use of virtual reality systems, it is possible to

A VR Framework for Training First Responders and Forensic Investigators 471

integrate technologies and allow users to interact with virtual reality scenariosin a multi-sensory manner, including physical interaction and tactile sensations[3,4,29]. Some studies have indicated that virtual reality environment in trainingexperiences provides unique benefits compared to traditional monolithic train-ing approaches. For example, such systems provide a learning-by-doing teachingapproach [13,18,29]. Moreover, various types of cues, such as visual, auditory, orhaptic, that are also available in the real world can also be provided, making thewhole experience provided in virtual environments look realistic. Such cues areresponsible for facilitating the learning task, as well as providing the ability tosimulate learning modules in a flexible manner that allows adaptation to individ-ual needs and training goals [13,18]. Virtual reality systems have been used toprovide not only practical training, including medical training [12,15,39], driv-ing [5,20], dance [7,9,21], painting [23,38], and safety [14,32], but also cognitivetraining, such as motor learning [27,33] and attention enhancement [11,31]. Inthe rest of this section, we present different virtual reality training applicationsthat have been developed.

Virtual reality applications have been developed for training purposes indifferent domains. Training children to walk cross streets safely has been aninteresting topic in the broader community, and based on the reported resultsof previous studies [26,34], utilizing virtual reality for road crossing training isquite effective. Driving simulations developed to teach safe driving skills havealso been used extensively. In addition to the logical advantages that such avirtual reality driving training process provide, it has been found [2,22] thatsuch training processes are quite appealing to the exposed individuals. Othervirtual reality training applications deal with evacuation training processes inhighly emergent conditions, and most of them, similar to the one that is presentedin this paper, target professional practitioners. Virtual reality has been used forfirefighter training [1,8,35], as well as evacuation and escape from different typesof emergency situations [28,37]. It can be said that the advantage of virtualreality training in the abovementioned scenarios is that it enables people topractice skills in a safe environment that they will later need to perform underhighly pressured and hazardous situations.

Virtual reality applications provide training, but they should also have away to evaluate whether the individual has met the learning objectives. In pre-vious studies, it was found that knowledge gained during virtual reality trainingcan indeed transfer to evaluations conducted in the real-world or virtual real-ity [10,26,30]. Moreover, results obtained from previous studies [10,30] indicatethat virtual reality training in highly immersive environments enhances learners’retention of knowledge gained during the training. Based on the abovementionedfindings, we were inspired to develop a virtual reality framework to train inci-dent first responders and digital forensics investigators. We believe it is possiblethat knowledge gained from virtual training can be efficiently transferred toreal-world environments. To the best of our knowledge, no previous virtual real-ity applications or frameworks for training incident first responders and digitalforensic investigators have been developed. Therefore, the effort made to identify

472 U. Karabiyik et al.

different functionalities and incorporate them into a single framework in whichthe crime scene and the training process may be easily adapted can be consideredas the main contribution of this paper.

3 Functionalities of the Framework

This framework is implemented via the Unity3D games engine. Several stan-dalone functionalities that we implemented can be easily attached to the nec-essary training parts/object. Unity3D provides the flexibility of easily reusingscripts, which makes it ideal for such a framework since it allows a developer tochange the training process and the crime scene scenario and simply attach thenecessary scripts to the new components added in the three-dimensional (3D)scene. The next section describes the basic functionalities that a developer canchoose from when implementing a new training and evaluation process.

3.1 Interaction Modules

Several different individual interaction modules and functionalities were imple-mented in the developed framework. Each of the interactive modules and func-tionalities works independently, and a developer can simply pick which moduleor functionality to include in the training and evaluation process. The developedmodules and functionalities are presented below.

Navigation: Various navigation mechanisms were implemented. Specifically, auser can choose to either navigate using a standard gamepad, keyboard, or vir-tual reality controllers or perform natural locomotion. To do so, the user needsto pick one of the provided navigation mechanisms from the inspector windowof Unity3D.

Object Interaction: The trainee has the ability to interact with objects thatare located within the crime scene. To do so, a simple script was implementedthat assigns different properties to the objects. Specifically, this script helps thedeveloper to specify which objects are interactive. Objects that are assigned tobe interactive can be grasped, moved, and rotated by the trainee to inspect themfurther. For the interactive objects, additional functionalities can be specified bythe developer, such as turning on/off, copying clipboard data, or removing apart of a device (e.g. battery).

Repository: A digital repository was also implemented. This repository keepstrack of evidence collected by the individual trainee and provides instant visualfeedback to the trainee when needed. The repository simply represents the inven-tory list of collected evidence from the crime scene in real life investigations.

A VR Framework for Training First Responders and Forensic Investigators 473

Tools: Several tools were implemented that can be useful during an investigationprocess. These tools are quite common and used almost always by investigators:

– Tags: When collecting objects from a crime scene, the investigator is requiredto tag the collected evidence by writing down necessary information on notesand assigning the notes to the collected evidence.

– Bags: Before placing the collected evidence into the repository, the investi-gator is required to put the object into a bag. This is an important procedurethat an investigator must follow. The investigator should insert the associatedtag on the bag.

– Notebook: A notebook is provided to the trainee to write notes during theinvestigation process. This helps the investigators remember small details ifhe or she needs to revisit the crime scene again.

– Stickers: The provided stickers are quite useful to the investigators sincethey can write down information about particular objects and areas of thecrime scene and mark the visited areas.

– Ruler: A virtual ruler is provided so the investigator can measure the dimen-sions of not only the objects but also the crime scene itself. The ruler alsoallows the investigator to not only describe the object but also record thedimensions and the exact position it was found within the crime scene.

– Flashlight: A virtual flashlight is included to allow the investigator to exploredimly lit areas of the crime scene, which enhances the realism of the investi-gation process since not all crime scenes are well lit.

– Camera: The use of a camera when exploring crime scenes is a must since aninvestigator needs to collect not only the necessary evidence but also addi-tional information about the crime scene. Record of the scene in picturesis indispensible for a successful investigation. Thus, a virtual camera is alsoincluded. Using this camera, the trainee can collect additional evidence. Thecaptured pictures are placed in a separate ‘pictures’ repository, to which theinvestigator has access throughout the training and evaluation process todetermine whether a particular image has already been taken.

3.2 Data Acquisition

Data acquisition is quite important in the trainee evaluation process. For thisreason, different data that provide useful information about the evaluation pro-cess are collected during the runtime of the evaluation module:

– Task execution sequence: There are various sequences in which a task canbe executed; however, in most cases, only one sequence can be consideredcorrect. It may be noted that for various objects and types of evidence, theinvestigator needs to follow a specific sequence of actions to ensure safe andreasonable execution of the task. The developed framework provides the abil-ity to store the sequence of actions used to collect each individual piece ofevidence/object that the investigator decides to interact with, and the sys-tem stores the executed sequence. This approach was taken for three reasons.

474 U. Karabiyik et al.

First, the system can easily and automatically determine whether the taskwas performed in the right sequence. Second, the system can use this informa-tion to assess the investigator’s performance. Third, the execution sequenceof a task (especially the wrong one) can be provided as feedback to the inves-tigator, and thus, the investigator can learn from his or her mistakes. Figure 1depicts how the training tasks are executed by the trainee for a sample sce-nario where smartphone is found in turned off state. Green boxes representcorrect tasks that an investigator is expected to follow where the red arrowand yellow boxes represent wrong direction and relatively correct tasks whichmight cause destruction or alteration of an evidence. Each box is assigneda score value that the trainee would gain if they perform that specific taskbefore the conclusion of the process.

– Missing evidence: Each crime scene that is set up has predefined pieces ofevidence that should be found and collected by the trainee. Once the traineeconcludes that the investigation is complete and decides to exit the evaluationmodule of the framework, the system automatically determines whether thereare any missing pieces of evidence and provides a percentage score of missingevidence to the trainee.

– Task completion time: This is the time that an individual investigatorneeds to finish the evaluation process. Although the task completion speedmight not be directly related to the effectiveness of the training, when com-bined with the abovementioned data, it can provide information to the traineeabout how detailed the investigation was. In most cases, rushing through aninvestigation may increase the risk of making mistakes hence causing themissing/destroying/altering evidentiary data.

3.3 Evaluation Method

In order to develop an evaluation method for our training, we developed a scor-ing system based on the best practices published in [24,36] for investigators.When the training scenarios are developed, each task that an investigator shouldperform is assigned a score depending on how significant that specific task iscompared to the other. In addition, we assigned penalties (usually significantlydeducting possible points for a task) when the trainee performs a wrong action(see red/yellow items in Fig. 1). This is also decided based on the significanceof a specific mistake. The possible mistakes we took into account are commonmistakes that new investigators might possibly do.

As each training is designed not only to measure perfect actions of the traineebut also measure some partial scores for the action taken following any previousmistakes or wrong actions. As illustrated in Fig. 1, the trainee is expected tokeep the device turned off to preserve the current state of the device. Followingthis action, some correct actions are still taken hence partial scores (e.g., 5 outof 15 for the first task) are given. In case of a serious mistakes are being made,no points will be given to the trainee.

A VR Framework for Training First Responders and Forensic Investigators 475

Powering off the device

Keep the device powered on

Start

Look for SIM card and remove it(SIM cards may be removed without

removing the battery)

Remove thebattery if possible

Store the device in aFaraday bag

Look for SIM card and remove it(SIM card may be removed

without removing the battery)

Put the phone on AirplaneMode (Possible since the

phone is not locked)

Check the battery level.Connect to an external

power source if the batterlevel is low

Turn onDevelopers Mode

Turn on USBDebugging Mode and

Stay Awake Mode.

Conduct RAMacquisition on

the phone

Remove thebattery if possible

Store the device ina Faraday Bag

End

5 5

20

5

45

15

15 10 10

5

20

Fig. 1. Example diagram for the tasks given in a scenario where powered on smartphoneis found. (Color figure online)

4 Training and Evaluation Modules

This section describes the two basic modules of the developed virtual realityframework. Each module is developed similar to the real life scenarios that weidentified from the current digital forensics best practices publications.

4.1 Training Module

The training module of the developed framework affords the trainee the abilityto, first, learn how to use the developed system and, second, gain both thepractical and cognitive knowledge required. During the training process, thetrainee is asked to interact with the embedded objects, which can be potentialevidence in a crime scene. During this process, on-screen textual instructionsare given to the trainee. These instructions follow specific guidelines providedby the developer of the training simulation and can be easily altered based onthe individual needs and scenario. The trainee is then responsible for followingthe instructions and executing the necessary tasks. An example of the trainingmodule is depicted in Fig. 2. Once the training process is complete and the traineefeels confident about the knowledge he or she has gained, it can then switch tothe evaluation module. Note that the trainee is allowed to perform each of thetraining tasks more than once in case it is required.

4.2 Evaluation Module

The evaluation module allows the trainee to test his or her newly acquired knowl-edge. In this case, a virtual reality crime scene is provided. The crime scene

476 U. Karabiyik et al.

Fig. 2. Example of a training scene in which the trainee learns how to interact withthe digital evidence and follow the task sequence.

includes various pieces of evidence that must be collected. During the evaluationmodule, the system records the pertaining data. No additional feedback is pro-vided. Upon entering the virtual reality crime scene, the trainee is responsible forperforming the necessary actions, collecting the necessary evidence, and reportthe results. We decided not to include time restrictions in the evaluation modulesfor two main reasons. First, the investigation process might take a long time andsecond, we did not want to put unnecessary pressure on the trainee. However,since the framework is responsible for collecting different types of data to assessthe trainee’s performance, completion time data can be used to understand howdetailed the investigation process was.

For the purpose of this paper, a simple crime scene was developed, which ispresented in Fig. 3. In this crime scene, the investigator should collect digital evi-dence (i.e., copy hard-drive data and browser history) and virtual devices (e.g.,smartphones, laptops, and computers). The trainee is responsible for exiting theevaluation scene once he or she believes the process is complete. Next, the systemprovides a performance report to the trainee and the supervisor based on thedeveloped scoring system, as well as additional feedback about mistakes madeduring the investigation process. Based on this feedback, the trainee might beasked to complete an additional training and evaluation process to ensure thatthe necessary knowledge is gained.

A VR Framework for Training First Responders and Forensic Investigators 477

Fig. 3. The virtual crime scene (top) developed for this paper, and close-ups (bottom)of the digital evidence that should be collected.

5 Conclusions and Future Work

In this paper, we described the basic functionalities of virtual reality frameworkthat is developed to train incident first responders and digital forensics inves-tigators. The current version of the framework provides most of the necessaryfunctionalities for training first responders and digital forensics investigators;however, various issues need to be addressed in future work. Specifically, inour future work, we are planning to extend the functionalities that should beincluded in such a framework. To do so, we are planning to consult with policeofficers and digital forensic investigators. We are planning to disseminate surveysand conduct in-depth interviews regarding possible digital forensic scenarios andfunctionalities that should be included to make this framework a useful tool forexperimentation and training.

Additionally, in the current version of this framework, the individual devel-oper needs to manually specify each piece of evidence and where it should beplaced within the virtual environment. As a result, we are relying on our knowl-edge that the developed crime scenes provide effective training. For this reason,in the future extension of this framework, we are planning to develop a stochasticoptimization algorithm that optimizes the difficulty of the crime scene by con-sidering the location, as well as the number and types of pieces of evidence that

478 U. Karabiyik et al.

needs to be collected. Such an extension will allow potential individuals to trainat the same virtual crime scene repeatedly while experiencing new variationsand different levels of difficulty.

Finally, in addition to the further development of this framework, we arealso planning to conduct a number of user studies to investigate the potential ofvirtual reality training for incident first responders and digital forensics investiga-tors. Such studies will help us further understand whether the proposed trainingframework can be used to substitute current training methods (i.e., slides andwritten notes) and increase training effectiveness and enhance trainee experiencein the simulation.

References

1. Backlund, P., Engstrom, H., Hammar, C., Johannesson, M., Lebram, M.: Sidh-agame based firefighter training simulation. In: 2007 11th International ConferenceInformation Visualization (IV 2007), pp. 899–907. IEEE (2007)

2. Backlund, P., Engstrom, H., Johannesson, M., Lebram, M.: Games and trafficsafety-an experimental study in a game-based simulation environment. In: 200711th International Conference Information Visualization (IV 2007), pp. 908–916.IEEE (2007)

3. Basdogan, C., De, S., Kim, J., Muniyandi, M., Kim, H., Srinivasan, M.A.: Hap-tics in minimally invasive surgical simulation and training. IEEE Comput. Graph.Appl. 24(2), 56–64 (2004)

4. Baxter, B., Scheib, V., Lin, M.C., Manocha, D.: DAB: interactive haptic paint-ing with 3D virtual brushes. In: Proceedings of the 28th Annual Conference onComputer Graphics and Interactive Techniques, pp. 461–468. ACM (2001)

5. Bayarri, S., Fernandez, M., Perez, M.: Virtual reality for driving simulation. Com-mun. ACM 39(5), 72–76 (1996)

6. Buck, U., Naether, S., Rass, B., Jackowski, C., Thali, M.J.: Accident or homicide-virtual crime scene reconstruction using 3D methods. Forensic Sci. Int. 225(1–3),75–84 (2013)

7. Cai, Z.F., Liu, D.J., Zhang, A.Y.: Scheme study of automobile driving trainingsimulator based on virtual reality. Acta Simulata Systematica Sinica 6 (2002)

8. Cha, M., Han, S., Lee, J., Choi, B.: A virtual reality based fire training simulatorintegrated with fire dynamics data. Fire Saf. J. 50, 12–24 (2012)

9. Chan, J.C., Leung, H., Tang, J.K., Komura, T.: A virtual reality dance trainingsystem using motion capture technology. IEEE Trans. Learn. Technol. 4(2), 187–195 (2010)

10. Chittaro, L., Buttussi, F.: Assessing knowledge retention of an immersive seri-ous game vs. a traditional education method in aviation safety. IEEE Trans. Vis.Comput. Graph. 21(4), 529–538 (2015)

11. Cho, B.H., et al.: The effect of virtual reality cognitive training for attentionenhancement. CyberPsychology Behav. 5(2), 129–137 (2002)

12. Colt, H.G., Crawford, S.W., Galbraith III, O.: Virtual reality bronchoscopy simu-lation: a revolution in procedural training. Chest 120(4), 1333–1339 (2001)

13. Derossis, A., Bothwell, J., Sigman, H., Fried, G.: The effect of practice on perfor-mance in a laparoscopic simulator. Surg. Endosc. 12(9), 1117–1120 (1998)

14. Filigenzi, M.T., Orr, T.J., Ruff, T.M.: Virtual reality for mine safety training.Appl. Occup. Environ. Hyg. 15(6), 465–469 (2000)

A VR Framework for Training First Responders and Forensic Investigators 479

15. Gallagher, A.G., Cates, C.U.: Virtual reality training for the operating room andcardiac catheterisation laboratory. Lancet 364(9444), 1538–1540 (2004)

16. Gavish, N., et al.: Evaluating virtual reality and augmented reality training forindustrial maintenance and assembly tasks. Interact. Learn. Environ. 23(6), 778–798 (2015)

17. Gee, A.P., Escamilla-Ambrosio, P.J., Webb, M., Mayol-Cuevas, W., Calway, A.:Augmented crime scenes: virtual annotation of physical environments for foren-sic investigation. In: Proceedings of the 2nd ACM Workshop on Multimedia inForensics, Security and Intelligence, pp. 105–110. ACM (2010)

18. Gosselin, F., Ferlay, F., Bouchigny, S., Megard, C., Taha, F.: Design of a multi-modal VR platform for the training of surgery skills. In: Kappers, A.M.L., vanErp, J.B.F., Bergmann Tiest, W.M., van der Helm, F.C.T. (eds.) EuroHaptics2010. LNCS, vol. 6192, pp. 109–116. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14075-4 16

19. Hibshi, H., Vidas, T., Cranor, L.: Usability of forensics tools: a user study. In:2011 Sixth International Conference on IT Security Incident Management and ITForensics, pp. 81–91. IEEE (2011)

20. Ktena, S.I., Abbott, W., Faisal, A.A.: A virtual reality platform for safe evaluationand training of natural gaze-based wheelchair driving. In: 2015 7th InternationalIEEE/EMBS Conference on Neural Engineering (NER), pp. 236–239. IEEE (2015)

21. Kyan, M., et al.: An approach to ballet dance training through ms kinect and visu-alization in a cave virtual reality environment. ACM Trans. Intell. Syst. Technol.(TIST) 6(2), 23 (2015)

22. Lang, Y., Wei, L., Xu, F., Zhao, Y., Yu, L.F.: Synthesizing personalized trainingprograms for improving driving habits via virtual reality. In: 2018 IEEE Conferenceon Virtual Reality and 3D User Interfaces (VR), pp. 297–304. IEEE (2018)

23. Lee, G.A., et al.: Virtual reality content-based training for spray painting tasks inthe shipbuilding industry. ETRI J. 32(5), 695–703 (2010)

24. Lothridge, K., Ftzpatrick, F.: Crime scene investigation: a guide forlaw enfocement (2013). https://www.nist.gov/sites/default/files/documents/forensics/Crime-Scene-Investigation.pdf

25. Ma, M., Zheng, H., Lallie, H.: Virtual reality and 3D animation in forensic visual-ization. J. Forensic Sci. 55(5), 1227–1231 (2010)

26. McComas, J., MacKay, M., Pivik, J.: Effectiveness of virtual reality for teachingpedestrian safety. CyberPsychology Behav. 5(3), 185–190 (2002)

27. Mirelman, A., Maidan, I., Herman, T., Deutsch, J.E., Giladi, N., Hausdorff, J.M.:Virtual reality for gait training: can it induce motor learning to enhance complexwalking and reduce fall risk in patients with Parkinson’s disease? J. Gerontol. Ser.A 66(2), 234–240 (2011)

28. Mol, A.C.A., Jorge, C.A.F., Couto, P.M.: Using a game engine for VR simulationsin evacuation planning. IEEE Comput. Graph. Appl. 28(3), 6–12 (2008)

29. Nishino, H., Murayama, K., Kagawa, T., Utsumiya, K.: A Japanese calligraphytrainer based on skill acquisition through haptization. In: 2010 24th IEEE Inter-national Conference on Advanced Information Networking and Applications, pp.1225–1232. IEEE (2010)

30. Padgett, L.S., Strickland, D., Coles, C.D.: Case study: using a virtual reality com-puter game to teach fire safety skills to children diagnosed with fetal alcohol syn-drome. J. Pediatr. Psychol. 31(1), 65–70 (2005)

31. Rizzo, A.A., et al.: The virtual classroom: a virtual reality environment for theassessment and rehabilitation of attention deficits. CyberPsychology Behav. 3(3),483–499 (2000)

480 U. Karabiyik et al.

32. Sacks, R., Perlman, A., Barak, R.: Construction safety training using immersivevirtual reality. Constr. Manag. Econ. 31(9), 1005–1017 (2013)

33. dos Santos Mendes, F.A., et al.: Motor learning, retention and transfer after virtual-reality-based training in Parkinson’s disease-effect of motor and cognitive demandsof games: a longitudinal, controlled clinical study. Physiotherapy 98(3), 217–223(2012)

34. Schwebel, D.C., Gaines, J., Severson, J.: Validation of virtual reality as a tool tounderstand and prevent child pedestrian injury. Accid. Anal. Prev. 40(4), 1394–1400 (2008)

35. Tate, D.L., Sibert, L., King, T.: Virtual environments for shipboard firefightingtraining. In: Proceedings of IEEE 1997 Annual International Symposium on VirtualReality, pp. 61–68. IEEE (1997)

36. U.S. Secret Service: Best practices for seizing electronic evidence (2015).https://www.cwagweb.org/wp-content/uploads/2018/05/BestPracticesforSeizingElectronicEvidence.pdf

37. Xi, M., Smith, S.P.: Simulating cooperative fire evacuation training in a virtualenvironment using gaming technology. In: 2014 IEEE Virtual Reality (VR), pp.139–140. IEEE (2014)

38. Yang, U., Lee, G.A., Shin, S., Hwang, S., Son, W.: Virtual reality based paintspray training system. In: 2007 IEEE Virtual Reality Conference, pp. 289–290.IEEE (2007)

39. Ziegler, R., Mueller, W., Fischer, G., Goebel, M.: A virtual reality medical trainingsystem. In: Ayache, N. (ed.) CVRMed 1995. LNCS, vol. 905, pp. 282–286. Springer,Heidelberg (1995). https://doi.org/10.1007/978-3-540-49197-2 36