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Topic of the competition: Recognition of activities carried out by first responders at a fire scene based on body sensor network readings

AAIA'15 Data Mining Competition: Tagging Firefighter Activities at a Fire Scene is a continuation of the last year's competition organized within the framework of International Symposium on Advances in Artificial Intelligence and Applications (AAIA'15, https://fedcsis.org/aaia). It is also an integral part of the 2nd Complex Events and Information Modelling workshop (CEIM'15 https://fedcsis.org/ceim) devoted to the fire protection engeneering. This time, the task is related to the problem of recognizing activities carried out by firefighters based on streams of information from body sensor networks. Prizes worth over 4,000 PLN will be awarded to the most successful teams. The contest is sponsored by Polish Information Processing Society (http://www.pti.org.pl/), with a support from University of Warsaw (http://www.mimuw.edu.pl/) and ICRA project (http://icra-project.org/).

Topic outline

  • Introduction

    Firefighter wearing a smart jacket by Karol Krenski

    A fire ground is considered to be one of the most challenging decision taking environment. In dynamically changing situations, such as those occurring at a fire scene, all decisions need to be taken in a very short time. Since wrong decisions might have severe consequences, a commander of the response team is forced to act under a huge psychological pressure. This fact, combined with incomplete or inaccurate information about the current situation, sometimes leads to committing serious mistakes [1].

    There are several initiatives that investigate this complex problem. One of them is The National Near Miss program in the USA (www.nationalnearmiss.org/). It gathers and analyzes reports describing real-life dangerous situations, and tries to draw some conclusions regarding their causes. Based on several thousand of carefully analyzed reports, experts identified the “lack of situational awareness” as the main factor associated with major accidents among firefighters [2]. This observation is in accordance with the results of the previous edition of our data mining competition [3]. The situational awareness corresponds to the cautiousness of a commander and his understanding of the actual state of the environment. Conditions affecting the situational awareness can be broken down into three groups: a lack of information, a lack of knowledge and a lack of cognition [4]. In this context, it seems that an increase in the situational awareness of commanders would result in taking better decision and thus increasing the safety of firefighters. Studies on the causes for mortal accidents during the actions of firefighters were also conducted by the Department of Homeland Security of the United States [5]. One conclusion of their research is that over 43% of deaths at a fire scene was caused by the stress or overexertion. Therefore, another critical way of increasing the firefighter safety is by monitoring their kinematics and psychophysical condition during the course of fire & rescue actions. 

    Our research team works on those problems within a frame of ICRA project (www.icra-project.org/). One of prototype tools developed as a result of this project is, so called, a "smart jacket". This device is a wearable set of body sensors that allows to automatically track a firefighter at a fire scene. It also enables real-time screening of firefighter’s vital functions and monitoring of ongoing activities at the scene. The later of those two tasks is the main scope of this year’s AAIA Data Mining Competition. We would like to ask participants to come up with efficient algorithms for labelling activities conducted by firefighters during their training exercises, based on provided data sets from our body sensor network. More details regarding the data and their acquisition process can be found in Task description section. Moreover, a good starting point for research on the problem of activity recognition based on body sensor data was described in [6]. We hope that your expertise and innovative ideas will become a valuable contribution in our effort to increase the safety of brave men and women serving in Polish State Fire Service.

    Special session at CEIM'15: As in the previous year, a special session devoted to the competition will be held at 2nd Complex Events and Information Modelling workshop (CEIM'15 https://fedcsis.org/ceim) which is a part of 10th International Symposium on Advances in Artificial Intelligence and Applications (AAIA'15, https://fedcsis.org/aaia). We will invite authors of selected reports to extend them for publication in the conference proceedings (after reviews by Organizing Committee members) and presentation at the conference. The publications will be treated as regular papers (including indexation in the Thomson Reuters Web of Science, DBLP, Scopus and other portals). The invited teams will be chosen based on their final rank, innovativeness of their approach and quality of the submitted report.

    Promotional video:

    Awards: Authors of the top ranked solutions (based on the final evaluation scores) will be awarded with prizes:

    • First Prize: 3000 PLN + one free FedCSIS'15 conference registration,
    • Second Prize: 1000 PLN + one free FedCSIS'15 conference registration,
    • Third Prize: one free FedCSIS'15 conference registration.

    The award ceremony will take place during the FedCSIS'15 conference (September 13 - 16, Łódź). Traditionally, invited authors who decide to attend the conference will receive a diploma and a competition T-shirt.

    Schedule:

    • March 9, 2015: start of the competition, data sets become available,
    • June 1, 2015: deadline for submitting the predictions,
    • June 5, 2015: deadline for sending the reports, end of the challenge,
    • June 8, 2015: on-line publication of final results, sending invitations for submitting short papers for the special session,
    • June 22, 2015: deadline for submissions of papers describing the selected solutions,
    • July 6, 2015: deadline for submissions of camera-ready papers selected for presentation at the CEIM'15 workshop.

    Contest Organizing Committee:

    • Andrzej Janusz, University of Warsaw
    • Michał Meina, University of Warsaw
    • Adam Krasuski, Main School of Fire Service
    • Krzysztof Rykaczewski, University of Warsaw
    • Bartosz Celmer, Main School of Fire Service
    • Dominik Ślęzak, University of Warsaw & Infobright Inc.

    In case of any questions please post on the forum or write us an email: AAIA15Contest@mimuw.edu.pl

    References:

    1. A. Krasuski: “A framework for Dynamic Analytical Risk Management at the emergency scene. From tribal to top down in the risk management maturity model”, FedCSIS 2014, pp. 323-330
    2. L. J. Grorud and D. Smith: “The National Fire Fighter Near-Miss Reporting. Annual Report 2008”, in An exclusive supplement to Fire & Rescue magazine. Elsevier Public Safety, 2008, pp. 1–24
    3. A. Janusz, A. Krasuski, S. Stawicki, M. Rosiak, D. Ślęzak, H. S. Nguyen: “Key Risk Factors for Polish State Fire Service: a Data Mining Competition at Knowledge Pit”, FedCSIS 2014, pp. 345-354
    4. A. Krasuski, A. Jankowski, A. Skowron, and D. Ślęzak: “From sensory data to decision making: A perspective on supporting a fire commander”, in Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM International Joint Conferences on, vol. 3. IEEE, 2013, pp. 229–236
    5. United States Fire Administration: “Annual report on firefighter fatalities in the United States”, http://apps.usfa.fema.gov/firefighter-fatalities/
    6. M. Meina, B. Celmer, K. Rykaczewski: “Towards Robust Framework for On-line Human Activity Reporting Using Accelerometer Readings”, AMT 2014, pp. 347-358
  • This topic

    Summation

    A summary of the competition.

  • Challenge information and data

    Below is a link to Terms and Conditions of the competition. It MUST be read and accepted in order to enroll.