With AI already transforming how home safety is managed, monitored and safeguarded, FireAngel’s technical director, John McEwan, looks at the benefits of using AI to protect homes and enable emergency interventions.
AI has ushered in an era of rapid digital transformations in the way that assets and infrastructure are monitored, protected and maintained. By analysing swathes of data from real-time monitoring systems and using predictive technologies, it is redefining asset protection and management. These innovations are quickly becoming a necessity for organisations that need comprehensive safeguarding procedures for properties and critical infrastructure.
Through the intelligent application of connected systems, real-time data flows and predictive analytics, local authorities, housing providers and emergency services are in a better position to proactively protect their communities while optimising operational efficiency.
From reactive to proactive
Legacy property management systems and processes often rely on the reactive approach of intervention or remediation once an alarm triggers or a tenant reports a major maintenance problem, respectively. This style of property management, with scheduled servicing and after-the-fact interventions, often leads to costlier fixes that are more disruptive to operations and tenants’ lives than they should have been.
Predictive technologies are now challenging this model by analysing huge amounts of live data from networks of smoke, heat and carbon monoxide alarms alongside environmental monitors and using AI analytics to anticipate device degradation, identify anomalies and flag environmental risks before failure. Instead of replacing equipment when it malfunctions or bearing the burden of expensive emergency repairs, housing providers can target their interventions more effectively, reducing downtime and saving on costs.
For housing providers with large portfolios, the implications of a ‘smart’ management style can be significant. Fewer missed and false alarms, lower overall costs and stronger compliance through the generation of reports that can automatically register standards updates and legislative changes.
Real-time data
Previously, scaling safeguarding systems has always been a necessary headache for housing providers where growth means more complicated safety assurance. A local authority with 10,000 properties may miss the occasional defunct alarm and may struggle to prioritise its risk assessments across such dispersed stock. However, these blind spots create real threats that can’t be ignored – real-time monitoring delivers the necessary visibility to mitigate risk.
IoT-enabled devices integrated into connected platforms allow housing providers to access live status data across their entire estates. AI-based filtering can then create a picture of the status of devices and individuals, then learning from pattern recognition and identifying potential false flags to only return critical faults and actionable insights.
For example, FireAngel Connected aggregates home safety data across housing portfolios through interconnected alarms and environmental sensors to predict how the status of each property might degrade. With individual sensors, the predictive algorithm can calculate the likelihood of mould developing in a home, before it does.
By aggregating data across portfolios, systemic risks that were previously invisible suddenly become visible. If multiple flats in the same development show recurring mould problems, there might be an underlying ventilation issue or poor insulation. If similar household appliances repeatedly fail across a borough, property managers can determine the root cause of those failures and implement strategic, data-led interventions.
This allows property managers to move from systems reliant on hardware to more fluid systems that surround and support safety procedures and interventions to improve them as time goes on.
Emergency responses
Predicting risks and maintenance requirements before they occur is one thing but the response time and method for an emergency can be the difference between life and death.
AI is reshaping how emergency services respond to crises as they unfold. Prior to emergency call-outs, predictive technologies, such as FireAngel Predict, can analyse trends such as repeated alarms or delayed silencing to provide an early warning framework to intervene proactively. This technology also registers potential false alarms, prompting interventions before requesting the fire and rescue services (FRS), saving resources and potential distractions from real emergencies.
During an emergency, the FRS are beginning to integrate AI into their response methodologies. Real-time data analysis from contemporary systems can streamline decision-making in high-stake situations, allowing for clear and informed interventions. Imaging and sensing technologies can provide situational awareness by accurately spotting trapped residents or by identifying hazards and factors such as wind direction that could alter a fire’s behaviour.
Whether hazard detection, communication support or planning, connected technologies will continue to synergise, creating a network of data that will improve emergency response times and effectiveness. The FRS will have an increasingly comprehensive picture of the at-risk situation as more smart technologies are deployed.
Challenges & barriers
As with all new technologies, the widescale adoption of integrated AI systems isn’t without barriers. Caution with data-based technologies always calls for concerns around privacy which must be carefully considered, especially where personal and health-related data is collected.
The National Fire Chiefs’ Council (NFCC) released its AI and digital ethics framework last year in an effort to guide the responsible use of AI in fire safety. With connected technologies, ethical governance is imperative for its successful adoption and although this can be a difficult challenge to achieve without compromising its predictive value, it is nonetheless necessary.
Besides privacy, there is the concern of disproportionate adoption, resulting in less-connected estates excluded from digital ecosystems. For universal predictive safety and data that is ubiquitous for fire and rescue services, this digital divide must be closed. The benefits for housing providers to invest in the technology early should be a driver for this, although the initial capital investment is still a considerable barrier for many housing providers.
Additionally, integrating these new systems with legacy systems is difficult (at times, impossible), creating another barrier that’s often simply not worth the headache in the short term. However, the benefit of investing in adaptable platforms offers long-term cost benefits and a more flexible compliance approach, some of which are compatible with legacy systems.
Conclusion
The application and integration of AI into assets and infrastructure management should be considered as a necessary next step in the evolution of smarter and more accurate safety monitoring. These new technologies can process and analyse data in volumes simply impossible for legacy systems and can anticipate risks rather than react, resulting in fewer incidents and lower maintenance costs.
Real-time data will continue to enhance housing providers’ oversight of their housing stock and the effectiveness of emergency responders, better protecting assets and, most importantly, the residents. Predictive technologies are already identifying behavioural patterns in tenants and facilitating interventions to improve wellbeing; as they become further enhanced, both residents and housing providers can expect to benefit from intelligent management.
For more information, please visit fireangel.co.uk/trade/contact-us-connected.
John McEwan is the technical director at FireAngel.
