Gamification in the Fire Service
A few years ago, when working at a University, I had learnt to fly a drone. We were building prototype drones for search and rescue operations, and we needed pilots to get as many potential hours in the air as we could to test the efficiency of the tactics. Rather than letting me loose on a pretty expensive, unique and fragile prototype, I was given access to a software simulator, a log-in and told to rack up 250 hours of crash free flying before being allowed anywhere near the real thing. To cut a long story short, I spent about 150 hours crashing the drone into a variety of virtual obstacles, and the virtual ground, before we all decided that my talents probably lay in other aspects of the project.
The point of this anecdote though is to highlight a trend, existing in many industries, of gamification – the concept of building training for real work, often safety critical skills, through a gaming environment. The advantages of gamification in the fire service are immediately apparent; we can give our staff and recruits experience of dangerous scenarios without them having to physically be in danger.
This concept of gamification is not necessarily a new one. The use of games, or rhymes to learn key tactical procedures is well documented. For example, the British Library has a collection of recordings from the early 20th Century of soldiers and sailors singing ditties to accompany bugle or pipe calls to remember their meanings .
The use of a recreational activity to drive a functional activity is often believed to be routed in “green stamp” rewards schemes associated with petrol stations from the early 20th Century. From the 1920s to the 1970s the stamps were collected and sought after in their own right, driving sales to individual companies. Many iterations of this “collection” marketing strategy have existed, some of which becoming crazes in their own rights, and so pervasive into society that that the original aim (to buy someone’s product) is virtually forgotten.
The two main branches of game theory are cooperative and non-cooperative. Within non-cooperative game theory, which deals largely with how intelligent individuals interact with one another in an effort to achieve their own goals, there are sub categories such as, economic theory which has three further main branches: decision theory, general equilibrium theory and mechanism design theory. Decision and mechanism design theory are the interesting ones for the development of belief systems and as such link to the method of how augmented reality can involve the user in a systems that appears and feels as reel as actual reality. Theory can be understood has having knowledge of an issue however “knowing”, or experience, can be understood as having applied that knowledge to a practical situation to experience the feelings which in turn begin to form beliefs based on the reactions of applying your knowledge .
Understanding how these theories link to the technology of AR and how that can then be used to form belief systems for future application of your knowledge in a command decision making environment is where we see the application of AR as a significant bridge between what is realistic training and reality.
Clearly, modern Game theory in all its complexity requires and generates a great deal of data. But, if we start to incorporate the wearable computing technologies covered in previous articles it’s clear that we will be generating huge amounts of usable data through simply participating in virtual training.
Sensors recoding participants’ affect (how their stance, movement and posture display emotion) and biometric data will be recorded constantly, as a standard. This data can be combined with more prosaic information such as times of events, decisions made and so on to feed the game theory algorithms.
What this allows the possibility for is a comparison between the reactions to, and decisions within, real and virtual training scenarios. For example, we could create the same training set-up, say a 3 pump house fire, with the subject being an officer in charge, but create one in virtua and one on a physical training rig. By recording the physical; and emotional responses to both, we can start to understand the differences between virtual and physical training – we can start to uncover how real virtual training feels.
But we can take that one step further, and really start to investigate how people operate in a training environment. If we take it as read that firefighters are all wearing an array of sensors, woven into fabrics, worn on., or in, helmets and theses sensors are recording physiological data, decisions, video, thermal and locations and that this is now the norm on operations, we can see that we are creating a vast data set of real operational data – i.e., we have a dataset that underlies how real humans operate, react and behave in real conditions.
Where this can be undertaken as a standard form of data collection in the operational environment we will start to see the differences in reactions and decision making due to the wearers “knowing” they are in an exercise and the theory is this is safe and “knowing they are in an operational scenario and there is no “pause button”, in other words uncertain outcomes which may result in a less than favourable outcome.
If we then use that data to create our virtual training worlds, figure 1, we can carry out a three-way comparison. We can compare how a subject reacts (and how their body reacts) in the same scenario, but on three testbeds – virtual, live training and real life. Within these comparisons lie the answers to how we get the most out of personnel, how to make training most effective and challenging and how we protect our personnel from threats such as PTSD and how we avoid overloading spheres of decision traps with the command decision making processes in operations in high stress situations.
Figure 1: Still from experimental FDNY training game, by Karlen Tam, NYU-Poly.
Like the use of rhymes or ditties to learn duties, the idea of employing games to aid in firefighting training is in itself not new. Holmatro have a game for understanding the principles of a phased approach to extrication in RTC . Nearly ten years ago, gaming artist Karlen Tam developed a game whilst at NYU-Poly, to train New York firefighters the principles of a ventilating fan . There have a number of “simulation” games on the market over the last 15-20 years based on varying levels of realism in firefighting tactics. Even the diagrammatic cartoons explaining drills in recruits’ manuals contain some of the elements commonly found in games. Imagine if the recruit could have spent months before even arriving at training school virtually moving to the right position, responding to the correct commands and becoming familiar with the terminology associated with equipment.
Figure 2: Lone Echo’s Virtual Hands, allowing a more nuanced interaction with a virtual world.
Recent advances in “hand presence” allow for a much more realistic interaction with a virtual world and would allow for a more immersive training experience, especially with something like pump operation, or the use of small tools. For example, candidates learning to operate a manual winch would learn the fact that a button must be pressed in order to free the locking handle – but more critically learn the muscle memory associated with that act. Imagine the efficiencies to be gained with whole cohorts of recruits arriving at raining school already “knowing” how to operate the equipment, and how to move through all the drills. Furthermore, the nature of the gaming environment means that the candidate’s interaction with the training game is recorded, so fire services would only need to accept trainees who have already demonstrated sustained levels of success in performing drills and operating equipment. Simply having recruits train in a gaming environment to learn the layouts of training vehicles – i.e. where to find the dividing breech – and demonstrate hour of competence in that, would save training and recruiting departments hundreds of hours of training time. The growth and advances in gaming sophistication mean that virtual training that is as good as live training, or even operations is within our grasp.
The cognitive development of skills through the application of knowledge is as one of the most effective methods of creating and retaining information. Experiential learning and the continual practice of these skills as part of a pathway of progression within any organisation is critical to growing the organisational competence and as a result organisational safety. Edgar Dale’s cone of experience indicates AR would have a 90% link to the retention knowledge initially experienced through this process.
But, in order for this to work, we need to do a number of things, and create a positive feedback loop between the three worlds.
The holy grail in integrating virtual and real-world training is create a positive feedback where each iteration of either training or operational activity contributes to the next occurrence of either. So, to put it simply, we gather data from real operational incidents about how individuals react and act – biometric data, movement and the operation of machinery or devices. This data is then used by our virtual training systems to create realistic scenarios, but also to take advantage of elements of game theory to create realistic virtual colleagues and “opponents” within the game. Similarly, we can take the physiological and deterministic reactions to a game-based simulation and use them to determine how we might better write policies, or create safety mechanisms for operations – effectively using the virtual world as a test bed for operations.
Conscious to Sub-conscious learning
Learning a new skill is all about memory and how you use it to develop beliefs through experience. Initially your short-term memory (located in the prefrontal cortex) stores your activities, experiences and the reactions your body and mind encounter. The mind is really busy figuring out how it’s done and helping to direct to body to create the muscle memory required to be developed to become proficient. This is the part of your brain involved with conscious decision-making and planning. Once you develop proficiency it is freed up by as much as 90% and you can now perform that skill automatically, leaving your conscious mind to use the new capacity on other information. Importantly this spare capacity provides the mind with capacity assess the current performance through the ability to be self-aware, or be reflexive, to identify where there are opportunities to avoid poor performance or improve it.
Achieving and maintaining this automatic transference within the mind comes through overtraining by continuously practicing something you’ve already learned inside and out. Once you’ve over-learned a skill, you no longer need conscious thought to perform or even teach those skills. The key is to understand that the trained mind is not necessarily working much faster than an untrained mind—it is simply working more effectively, which means that the conscious mind has less to deal with.
Any example within the fire service is the repetition of basic drills until they could be achieved without direction and conscious communication between the members of the crew. Figure 3
Figure 3: Recruits Manual, showing positions for a ladder drill.
One thing we have to be very careful of, is tripping into the Dunning-Kruger effect, figure 4. It is (relatively) easy to create these virtual worlds, and as such easy to give people rich and realistic experiences of scenarios in which they may perform well. Without care, we could find ourselves falling foul of the “little knowledge is a dangerous thing” syndrome, in which we create a number of high confidence, low competence individuals – something ‘tickbox training’ (where training is received passively and the trainee not truly challenged) has produced. The creation of “virtual knowing” and it subsequent development of beliefs base on the experiences within the system could develop false risk perceptions and bias that reality would challenge with potentially significant effects for decision making, incident management and safety.
Figure 4: The Dunning-Kruger Effect
So why is all of this important? MarketsandMarkets estimates the global gamification market to be around $11.10 Billion by 2020. It would seem clear that the application is likely to be effective in the fire sector, leading to safer operations, and a move away from “tick box” training online. Bringing elements of the real world into a virtual world that we can manipulate will truly challenge our personnel, but allow better and more detailed training worlds to be created based on actual data and outcomes. The effectiveness and efficiencies that could be created through reduced reliance on physical training locations that are expensive to build and maintain that could also create flexibility and the ability to access training with limited impacts on operational availability. Ultimately AR is one of the keys to providing improved realistic training that meets an array of organisational needs on many fronts and keeps fire services in line with the changing legislative environment, but most importantly protects our staff and the public safe.
About the authors
Dr Ian Greatbatch FRGS, MEPS, FHEA is a firefighter for Surrey Fire & Rescue Service, a free-lance researcher and formerly an Associate Professor at Kingston University, London. He specialises in Search and Rescue (SAR), Fire and Rescue and applications of Geographical Information to those disciplines.
AC Iain Houseman is currently the Head of regulatory fire safety protection and prevention for Surrey fire and rescue service. He has held roles in the Local Authority Trading Company as a contract and business development manager, Head of Training, Cross service Support and operations resources manager creating new systems and processes to support change in the modern fire service. Iain is currently completing a Masters in Systems Thinking in Practice.