Taking the Advantage of Smartphone Apps for Understanding Information Needs of Emergency Response Teams’ for Situational Awareness: Evidence from an Indoor Fire Game

  • Vimala NunavathEmail author
  • Andreas Prinz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9733)


In search and rescue (SAR) operation, a lot of information is being shared among different emergency response groups. However, one of the key challenges experienced by these rescue groups during SAR operation is obtaining the complete awareness of the situation from the shared information. Moreover, one of the key actions of rescue leaders is to get the needed information in order to coordinate effectively with other teams and perform well. So, in this study we conduct an indoor fire drill with the help of Smartphone application with two settings (without SmartRescue smartphone application and with SmartRescue smartphone application) to find out what type of information is mostly communicated in both scenarios and needed by response teams. The presented results combine observations, qualitative and quantitative data analysis on videotaped data after the game. The results indicate that information categories which are formulated more recurrent in second scenario than first scenario. This might be explained as technology is more effective for sharing the information which is available on the smartphone application for obtaining situational awareness and for coordination.


Information sharing Mobile HCI Emergency management tools Situational awareness Indoor fire game Smartphone applications Information needs Information categories 

1 Introduction

During any kind of disaster response, information sharing plays a vital role in spreading the information. But the volume and velocity of information shared during crises today tend to be extremely high, making it hard for emergency response teams to process the information in a timely manner. Furthermore, shared information tends to vary highly in terms of subjects and usefulness i.e., from information that could be entirely about critical information that augments situational awareness or information that contain rescuers safety or about resource information. Finding precise information from the shared information could accelerate the emergency response activities and alleviate both property and human losses.

Besides, in Search and Rescue (SAR) operations, first responders are often fragmented into different teams to carry out the different tasks (such as fire-fighting, saving victim, evacuation, getting the overview of the situation and so on) at different geo-locations. Due to geographical dispersion, these divided teams must share information within or among (intra-inter) teams to obtain or help to get the overview of the situational as well as to cooperate effectively [1]. However, to obtain the situational awareness is a challenging task as it requires first response teams to have access to the emergency related information. Without having enough and the right type of information, it is difficult to gain situational awareness (SA) [2, 3, 4]. Particularly, in dynamic and time critical situations, it becomes difficult for the first response teams to adequately decide which information might be relevant for other teams to support overall coordination. Hence, Information sharing and Task Assignment errors may occur and hence give poor awareness of the situation [5].

Furthermore, in order to support the accurate formation of SA, critical information needs should be identified [6]. In addition, when making decisions in emergencies, good information flow is required. The information which is being shared must be acquired from different sources to create a correct mental picture of what is going on. Moreover, decisions based on low-grade information can lead to poor emergency response [7, 8].

Seppänen et al. [6] have collated the major factors that hampered the Search and Rescue (SAR) organization in achieving adequate SA. These influential factors were information gaps, the lack of fluent communication, and the fact that there was no common operational picture in use. They also found out that the factors affecting information gaps were agencies focusing only on their own tasks, unclear information delivery processes, shortages of incident information, agencies passivity, and a lack of up-to date information.

Therefore the goal of this paper is to find out what type of information emergency rescue teams often needed and communicated during SAR operations in order to get the overview of the situation, to support coordination and to manage their emergency response successfully.

To understand and identify the information needs, a reality indoor fire game was conducted with voluntary participants with the help of smartphone applications. However, augmented reality games are excellent instruments particularly for emergencies to help the researchers and practitioners to better understand the communicated content and information flow patterns emerging within and across emergency response teams [9].

The rest of the paper is organized as follows: first we begin with the description of the developed game with two emergency scenarios which was used to collect the data and then the paper explains the research method that we used for analyzing the collected videotaped data. The results part shows the extracted information categories and the frequency in both scenarios. Finally, the conclusion part summarizes the lessons learned from this research and discusses directions for future research.

2 Game Design and Scenario Development

The developed scenario was about search and rescue operation in an indoor fire setting. This game was played with 23 voluntary participants to identify the information needs of different emergency leaders and their corresponding teams from the communicated content during emergency response. The duration of the game was 30 min and played inside the university building. In this game execution, 11 observers (i.e., 4 fire-fighters and the rest university staff) were present.

For this game, the research team divided these 23 voluntary participants into three teams, one as crew manager (CM), and one as medical care unit (MCU). In these divided three teams, each team consists of three members: one as smoke diver leader (SDL) and other two as smoke diver members (SDs) and the rest 12 as victims. The participants were briefed about their goals and tasks before the game start. The design of the game and division of the teams were done according to the obtained knowledge from the interviews with the real firefighters and also from the provided documents [10, 11].

In this game, two scenarios were designed. In the first scenario, only first responders and MCU got the smartphones with installed Zello application [13] and that was used as information sharing tool. Moreover, first responders further supplemented with a map of the floor layout in the building where the incident occurred to get an idea of the view of the floor. While in the second scenario both the players and victims got smartphones with installed SmartRescue application [14], but not MCU. However, walkie-talkies (WT) were given to only first response players and it was used as information sharing tool. However, the detailed description of the tools and the first responder groups division for both scenarios can be seen in the previous research [5].

However, in fact the game was designed to test a developed smartphone application called SmartRescue. This app is an Android based application which allows both first responders as well as victims to send and receive emergency-related data such as the location of the fire and the victims with the help of embedded sensors of the smartphone such as accelerometer, gyroscope, GPS, humidity, thermometer, and so on.

In the game development process, three main requirements are taken into account: complexity (the scenario must be complicated enough to involve multiple teams); concreteness (the scenario must include sufficient details to allow the participants to identify the relevant actors); and realism (the scenario must be realistic) [12].

The developed scenario was as follows: fire accident happened inside the third floor of A’ block of the university building. The building consisted of many students (who might be normal, disabled, and sick), library, laboratories and storage rooms. Most of the students noticed smoke, flames, and screams inside the building. Some of the victims also report fire intensification. Due to the fire, the emergency site became rampageous and many students inside the building were wounded and traumatized. The number of people inside the building was unknown. But, the people who were running out of the building were giving information about the seen victims [9].

3 Research Methodology

3.1 Emergency Responders’ Roles

From the interviews and provided documents, the research team got to know that only SDs enter inside the burning building in pairs to start search and rescue process to evacuate the victims from the affected area. However, SDLs does not enter into the affected building. They stay at the near to the building entrance to obtain the overview of the situation. Furthermore, SDLs are responsible for guiding his team members (SDs) by providing the needed information. SDL reports to the CM and receives orders and information from CM. If any of the SDs is injured, SDL inform to the CM and replaces his role with SD role. While, CM in charge of all crew members’ safety. He orders and shares/provides the needed information with SDLs. However, Medical Care Unit is responsible for noting down the brought victims (either injured or found or dead or conscious or unconscious) and informing to the CM.

3.2 Video-Taped Data Collection

During the game, the research team utilized four video recording cameras to record the entire game sessions for both scenarios. In both scenarios, these four cameras were placed in all corners of the building where the indoor fire game was conducted. In addition, the research team gave smart glasses and GoPros to the participants who imitated first responders’ roles to record the entire SAR operation. The reason for using video recorders, smart glasses and GoPros for data collection was that the entire game session of both scenarios can be retrieved from the recorded tapes. These tapes can provide the researchers a unique opportunity to revise them again and again. Yet, videos can be played, replayed, speedup, allowed or paused, discussed, analyzed and re-analyzed. Thus, provides the insights of the shared information with the actions [16]. Furthermore, after the game, the research team had a chance to discuss the key points with the players and with the observers. And then the players were given the opportunity to make any further comments on their experience and difficulties during game experiment if they felt to discuss. The discussion lasted approximately 30 min for both scenarios.

3.3 Video-Taped Data Analysis

The verbal content of the exchanged emergency information was analyzed through the thematic analysis which is a basic method for qualitative analysis method. Thematic analysis is a method for identifying, analyzing and reporting patterns (themes or categories) of the data [17]. The analysis was inductive with themes driven from the data collected. So, after the game, we retrieved all data from video cameras which were used to capture both scenarios. To analyze the video data, first all the data from video cameras were carefully retrieved, stored and later examined to ensure that all the communication done during the game is captured and later uploaded to our personal computer to analyze them. After uploading, we have played the videos, again and again, to extract and transcribe the shared information in the excel sheets for both scenarios.

After transcribing the shared information, the research team has done coding in NVivo 10.0 (QSR International) [15]. Here, coding is one of the several methods of working with and building knowledge from the abundant data. In coding, first node and sub-nodes are made. Each line of the transcripts gets coded into these nodes and sub-nodes. However, code is an abstract representation of an object or phenomenon or a way of identifying themes in the transcribed text. Coding the text for qualitative analysis is a way of tagging or indexing the text to facilitate later retrieval and allows re-contextualizing the data [18]. Before coding, transcripts were first read, and the content familiarized. Coded sections of the transcripts were then organized into preliminary categories.

As we code, Nvivo tool indexes (adding or tagging flags to) the text or videos by storing the references to the document at the node. In this tagging process, Nvivo is not making a copy of the text at the node, but connecting the concepts or categories with the data. The data that have been coded will be accessible from the nodes. In coding process, transcripts were re-read and double-coded and discussed with other researchers to maximize the reliability.

4 Results

In this section the results of information needs of emergency responders in both scenarios are presented with the help of qualitative and quantitative analysis.

4.1 Information Categories

The information sharing in scenario 1 was done 75 times and in second scenario the information sharing was done 68 times. The extracted shared information was separated into 3 columns in excel sheet. The data which was listed in column 1 of the excel sheet was about the information categories that were triggered. Second column was about the frequency of communicated information categories of first scenario and 3rd column was about the frequency of communicated information categories of second scenario. The data which was exchanged during the game divided into 6 categories to simplify the exchanged data, which is documented in Table 1.
Table 1.

Extracted Information categories from both scenarios

Information category


Victim status

Information about victim whether he is ok or injured or dead

Victim location

Information about the location of the victim


Information about needed resources and available during SAR operation

Building floor directions

Information about directions of the building floor (i.e., south, north, west, east) used to either to search for the victim

Fire location

Information about location of the fire development

Rescuers safety

Information about Rescuers status and location

In both scenarios, information categories which are listed in Table 1 were exchanged mostly. Whenever rescue teams find a victim, they exchange information about the victim status and location of the victim to their leader to make him obtain the situational awareness. Furthermore, when the rescue teams need ‘Resources’ during SAR operation, they exchange information about the needed resources from bottom to top (group members to leader) and available information from top to bottom (Leader to group members). Moreover, when the fire location is spotted, it is reported to the leader or inform to the other rescue members by giving the direction of the floor in the building. During SAR operation, rescuers have to check their own safety to achieve all the coordination activities.

4.2 Frequency of Information Categories for Both Scenarios

In the first scenario, the most exchanged information categories by emergency response teams were victims’ status and fire location. Whereas, in the second scenario, victims location and fire location information categories were mostly exchanged between rescue groups to obtain the situational awareness. This is because, in second scenario, the rescuers were given with smartphones with SmartRescue application. In this SmartRescue app, rescuers can see on the given mobile screen, where the victim is, fire development and status of the victims.

From the Table 2 it is visible that, Victim Status frequency is higher in the second scenario than in the first scenario i.e., from 30.66 % to 39.70 %. It is because rescue teams could find the victims with the help of SmartRescue application and pass the information to their related leaders. Moreover, MCU can also inform or confirm the victim status with the CM.
Table 2.

Frequency of Information categories from both scenarios


Scenario 1

Scenario 2


Frequency of S1

Frequency of S2

Victim status






Victim location












Building directions






Fire location






Rescuers safety











Considering Victims’ Location information category frequency, it is higher in second scenario than first scenario i.e., from 20 % to 65.33 %. The reason is from the given SmartRescue application, the rescue teams could see the location of the victim including the room number and name. When emergency rescue teams spot the victims on the SmartRescue application screen, these teams share that information with their corresponding leader. Furthermore, the leaders might have given the orders to the emergency team members by giving victims’ location information to save the victims.

When it comes to Resources information category, in scenario 2, emergency response teams exchanged information about this information category is 4 %. But, in first scenario, less than 2 % information about resources was exchanged. The reason might be that emergency teams were busy searching for the victims in first scenario than in second scenario.

As explained in the earlier paragraph, with the help of SmartRescue application, emergency rescue teams could spot the victim location. Due to the access to the victims’ location, emergency rescue teams could exchange the information about the building directions to describe the location of the victims. Therefore, from the exchanged information, Building Directions information category is acquired. So, from the Table 2, it is clearly evident that the frequency of this category is higher in second scenario i.e., 24 % than in the first scenario i.e., 16 %.

While Fire location information category in the obtained results, it is clearly visible that the frequency is 22 % in first scenario and 38 % in second scenario. So, this information category is higher in second scenario than scenario 1 as most of the information in scenario 2 is visible on the smartphone’s screen, whereas in scenario 1, rescue teams have to search themselves for the location of the fire.

But when it comes to Rescuers Safety information category, in second scenario this information category was mostly communicated i.e., 21.33 %, but only 4 % in first scenario. The reason is teams were working together in first scenario, whereas the frequency results got impacted with the distribution of SmartRescue application in the second scenario.

5 Discussion and Conclusion

In this paper, we have investigated and analyzed the data that was collected during an indoor fire game. The game was designed to test a developed Smartphone application called SmartRescue. From this study, the research team wanted to identify the information categories from the exchanged information. However, this study was done with voluntary students, and therefore some extra guidance and training of SmartRescue application were given before the game start.

The obtained results are based on from both qualitative and quantitative analysis. The acquired results provide knowledge about the critical information categories in receiving and sharing the information to obtain and maintain the situational awareness. However, the results show that emergency rescue teams and their related leaders had easy interaction with the smartphone application called SmartRescue and it helped them to obtain the SA. Therefore, the frequency is high in second scenario than in first scenario (without SmartRescue application).

Based on the results of our study, it is anticipated that emergency responders get an impression of what is being communicated during search and rescue operation. By knowing this, emergency responders can learn and understand what information was difficult to obtain and what not. Moreover, this learning can help emergency responders in real emergencies.

Our further research will be to play the same game with real fire-fighters and examine their information needs to enable a better understanding during search and rescue operations in situational awareness formation. And then compare the obtained results with the present study results for developing ICT systems.



This study is carried out in collaboration with the SmartRescue project led by Prof. Ole-Christoffer Granmo and co-funded by Aust-Agder utviklings-og-kompetansefond (AAUKF, projectnr. 2011-06). We would like to owe our gratitude to the Grimstad fire station personnel who supported us during the development of different stages of the experiment, to the students that took part in the game, and to Mehdi Lazreg Ben, Jaziar Radianti and Tina Comes for their constant support in the data analysis process. Finally, we thank the observers who provided their valuable suggestions after the game.


  1. 1.
    Netten, N., et al.: Task-adaptive information distribution for dynamic collaborative emergency response. Int. J. Intell. Control Syst. 11(4), 238–247 (2006)Google Scholar
  2. 2.
    Endsley, M.: Theoretical underpinnings of situation awareness: a critical review. In: Endsley, M., Garland, D.J. (eds.) Situation Awareness Analysis and Measurement. Laurence Erlbaum Associates, New Jersey (2000)Google Scholar
  3. 3.
    Kuusisto, R.: From Common Operational Picture to Precision Management. In: Managemental Information Flows in Crisis Management Network. Publications of the Ministry of Transport and Communications 81/2005, Helsinki (2005)Google Scholar
  4. 4.
    Toner, S.: Creating situational awareness: a systems approach. In: Altevogt, B.M., Stroud, C., Nadig, L. (eds.) Medical Surge Capacity: Workshop Summary. National Academies Press, Washington (2009)Google Scholar
  5. 5.
    Nunavath, V., Radianti, J., Comes, T., Prinz, A.: The impacts of ICT support on information distribution, task assignment for gaining teams’ situational awareness in search and rescue operations. In: Thampi, S.M., Bandyopadhyay, S., Krishnan, S., Li, K.-C., Mosin, S., Ma, M. (eds.) Advances in Signal Processing and Intelligent Recognition Systems. AISC, vol. 425, pp. 443–456. Springer, Heidelberg (2016)CrossRefGoogle Scholar
  6. 6.
    Seppänen, H., Mäkelä, J., Luokkala, P., Virrantaus, K.: Developing shared situational awareness for emergency management. Saf. Sci. 55, 1–9 (2013). doi: 10.1016/j.ssci.2012.12.009 CrossRefGoogle Scholar
  7. 7.
    Busby, S., Witucki-Brown, J.: Theory development for situational awareness in multi-casualty incidents. J. Emerg. Nurs. 37, 444–452 (2011)CrossRefGoogle Scholar
  8. 8.
    Endsley, M.R., Jones, W.M.: A model of inter- and intrateam situation awareness: Implications for design, training and measurement. In: McNeese, M., Salas, E., Endsley, M. (eds.) New Trends in Cooperative Activities: Understanding System Dynamics in Complex Environments. Human Factors and Ergonomics Society, Santa Monica (2001)Google Scholar
  9. 9.
    Nunavath, V., et al.: Visualization of information flows and exchanged information: evidence from an indoor fire game. In: The Proceedings of 12th International Conference on Information Systems for Crisis Management and Response (ISCRAM) (2015)Google Scholar
  10. 10.
    Beredskap, D. f. s. o.Veiledning til forskrift om organisering og dimensjonering av brannvesen (2003)Google Scholar
  11. 11.
    Beredskap., D. f. s.Veiledning om røyk og kjemikaliedykking (2003)Google Scholar
  12. 12.
    Eide, A.W., Haugstveit, I. M., Halvorsrud, R., Borén, M.: Inter-organizational collaboration structures during emergency response: a case study. In: Paper presented at the Proceedings of the 10th International ISCRAM Conference. Baden-Baden, Germany (2013)Google Scholar
  13. 13.
    Zellowalkie-talkieapp. Zello walkie-talkie software application.
  14. 14.
    SmartRescueProject. SmartRescue project, Center for Integrated Emergency Management (CIEM).
  15. 15.
    Nvivo tool, software application.
  16. 16.
    Morse, J.M., Pooler, C.: Analysis of videotaped data: Methodological considerations. Int. J. Qual. Methods 1, 62–67 (2008)Google Scholar
  17. 17.
    Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3, 77–101 (2006)CrossRefGoogle Scholar
  18. 18.
    Bazeley, P., Jackson, K.: Qualitative Data Analysis with Nvivo, pp. 1–305 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of ICTUniversity of Agder (UiA)GrimstadNorway

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