Can AI improve fire safety in homes?
Inside Housing hears from Andy Speake of safety technology specialist Aico about how social landlords can use artificial intelligence and machine learning to improve fire safety in their homes
Inside Housing hears from Andy Speake at Aico about how social landlords can use artificial intelligence and machine learning to improve fire safety in homes (sponsored) #UKhousingIn association with:
Artificial intelligence (AI) and machine learning are transforming a multitude of sectors, and fire safety is no exception. Inside Housing spoke to Andy Speake, national technical manager at safety technology specialist Aico, to find out how much of a difference these technologies are making – and what the future holds.
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What are some of the ways in which AI and machine learning can improve fire safety?
Machine learning is a specific subset of AI; it is essentially a complex array of algorithms that can spot trends in data and make decision-making a lot easier.
At the most basic level, it helps us get better at detecting fires by using historical data to help make more informed decisions today. It looks for patterns, and it allows either administrators or the fire and rescue service to recognise those patterns and make changes. It’s not just about fire detection; it’s also getting better at detecting nuisance alarms, because that can be counterproductive. So it can help landlords or occupants understand whether the situation needs an engineer or the emergency services.
The decisions will ultimately always be taken by a human – the last thing we want is a computer making a decision about whether an alarm is genuine.
These advanced systems will also be able to communicate more effectively with other life safety systems, better assisting the occupant and property owner to protect themselves and their home.
What might that communication with other safety systems involve?
In-home sensors these days can capture information on things like CO2 levels, humidity and temperature, and we can make correlations between a lot of those data points. CO2 can be given off when cookers are being used, and that’s correlated with carbon monoxide, for example. Or we can see dust levels within a room, and that can contaminate modern smoke alarm systems. So if we’re able to see how much dust and contamination is in an environment, then we can factor for that in the alarm system and essentially turn the sensitivity up or down as necessary.
What sort of data do these systems need, and how is this gathered?
There are multiple sources of data available for AI and machine learning systems to utilise in life safety systems. Obvious ones would include live sensor data, but this could come from multiple different sensors around the property, further improving the accuracy of any action or notification. Equally, environmental data and historic alarm data can also enhance the efficacy of these technologies.
Data about the property itself can also be factored in: the year it was built, the method of construction, and even the type of house. If it’s a terraced property, for example, then there is a greater risk of a fire spreading than in a detached property. We can even draw on geographical data, which can tell us which areas have had more issues with fires and even arson.
How much of a difference can these technologies make to fire safety as a whole?
The improvements that can be obtained through AI and machine learning will ultimately lead to safer homes, making the difference hugely important. Although the gains may seem small for now, these improvements will only become more extensive over time.
Andy Speake joined Aico in 2016. As national technical manager, his primary role is managing the technical department, including a dedicated team that deals with customer enquiries and provides technical support.
How widespread are these systems in the sector today?
Historically, the fire sector has been seen to be slow in adopting new technology – but it’s not slow to innovate, it’s just heavily regulated. This is in part due to the life safety aspect of fire detection and alarm systems, and the need for strict, harmonised standards governing the operation of these systems.
However, as we’ve seen from the aviation and automotive sectors, technology can be introduced at a considerable rate without sacrificing safety standards. In some countries, people already feel safe letting their car drive itself. That would have seemed crazy a few years back, and now it’s established technology. Aico already uses machine learning on many of its connected systems, providing landlords and homeowners with valuable insights into the health of their alarms, sensors and homes.
How can landlords take advantage – and how much does this technology cost?
As connected alarm systems become more widely adopted, landlords will as a matter of course begin to see the benefits of utilising AI and machine learning to help manage their alarm systems and homes. This will make systems safer and will reduce the demand on the person managing the property – and the costs of using these kinds of systems can usually be offset against the savings generated by reducing the amount of maintenance required.
We can give engineers valuable data before they go out and deal with an incident – such as whether the alarm system is working, whether it’s using mains power or a battery back-up, for example, or whether its sensor is working properly. Equally, issues can be reported remotely now. These improvements can reduce the amount of resources landlords need to spend, which in turn allows for a more effective use of their time.
Has there been any pushback from residents? How can landlords maximise resident buy-in?
Most residents are welcoming of improved technology within their homes as long as they understand the purpose and the benefits, and are able to access the data directly. Aico’s systems allow residents to access all data and insights gained through connected alarms and devices directly.
What does the future hold for this technology, and for the sector?
The future is massively exciting as we get better at protecting people from the risks of fire, CO and other indoor air quality conditions. We’re already seeing a huge improvement in how effective alarms can be at detecting risks and alerting occupants, but moving forward, home life safety protection systems will be able to reduce the risk of fire occurring by being more proactive and reducing the risk of these events starting.
Ultimately, these systems will become better at prevention, detection, communication, education and alerting the necessary support services. And, of course, the bigger the datasets, the more analysis we can do on them, and the better we become at using that data effectively. That will, I believe, increase levels of trust in the technologies.
I think there is an element of fear when it comes to any life-saving system and AI and automation. As I alluded to earlier, it would take a very brave person – or very capable AI – to put decisions about whether an alarm goes off in the hands of a computer today. But maybe in the future the technology will get to a point where it can be automated to that degree.
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