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Context Aware Applications for Better Outcomes and Safety |

Public Safety Solutions
Motorola Solutions
Context Aware Applications for Better Outcomes and Safety

Context aware technology solutions are key to addressing public safety challenges, giving emergency services the actionable intelligence they need for greater first responder safety and better community outcomes.

The public safety environment is not only growing more complex, but also faces additional cost pressures, stretched resources, growing community expectations and increasing public scrutiny.

One major key to relieving those pressures comes through harnessing and making better use of the masses of data available in cities today for better frontline operational outcomes. Context aware technology solutions have a major role to play in transforming public safety operational environments

What does context aware mean?

Gartner defines context aware solutions as “when situational and environmental information about people, places and things is used to anticipate immediate needs and proactively offer enriched, situation-aware and usable content, functions and experiences”.1

Context aware computing is not new, with the term “context aware” first used in 1994.2 What is new for public safety is the recognition of context in order to change the behaviour and functionality of applications.

At its very heart, this means ensuring the technology used by frontline responders can adapt to their ever-changing environments in highly effective ways so that all interactions become second nature. Context aware solutions provide maximum benefit when used to filter and analyse relevant information from a broad range of sources in order to deliver real intelligence to public safety officials. This enables emergency service organisations to make better and faster decisions which ultimately help them to deliver better overall outcomes and higher levels of safety.

Knowledge categories of context

Context can be considered in different categories of knowledge:

  • User: who, role, personal biometrics and physical attributes (eg time on shift and related fatigue risk, position in vehicle (sitting/seat belt on/engine off));
  • Environment: time, location, safe/danger, hot/cold, moving/static, light/dark, loud/quiet, air quality – all assisted by multiple sensors;
  • Device: size, capability, accessories, battery, health, personal area network, Internet of Things (IoT);
  • Task awareness: from CAD status and/or from workflow in progress such as database queries about locations or individuals in question; and
  • Other resources: other personnel and vehicles, personal protective equipment, breathing apparatus, medical equipment being used etc.

Application in real life

To better understand context aware intelligence, let’s compare an everyday incident that a police officer may attend*, and examine the impact of different technology applications:

Scenario 1: Current technology and solutions

A car passes a parked officer. With technology available today, automatic number plate recognition (ANPR) automatically scans and checks the number plate against a database “hot list”. As a result, the officer pulls the driver over, with in-car video recording activated by lights and sirens. The vehicle’s mobile data terminal (MDT) delivers car owner details and previous offences to the officer so that the speeding driver’s licence can be crosschecked against an offences database.

This scenario uses some components of context awareness, such as task awareness (identifying workflows or patterns), and environment (location awareness, officer in pursuit).

Scenario 2: Existing technologies – not commonly deployed

This scenario employs additional capability, some of which is in use today but not used widely.

This time, ANPR data is automatically relayed in real time to the command centre, along with metadata including location information and radar data which can be associated with CAD and records information. Location look-ups are also completed for the vehicle and geofencing applied to create a virtual boundary around the officer’s location.

Data from the MDT is replicated on the officer’s personal mobile device as he or she leaves the vehicle. Trigger events – for example, unclipping the officer’s firearm – alerts the command centre, who are able to access a real-time video feed both from the in-car video and other sources in the vicinity, such as a CCTV camera in the street that has an aerial view of the situation.

This additional information, used in context, keeps the command centre informed as events unfold, providing situational awareness of what the officer is facing and any action taken. Importantly, this data transfer occurs automatically and autonomously, with no direct input from the officer.

Scenario 3: The future

Beyond the capability already described in the first two scenarios, this version starts to leverage the Internet of Things (IoT), where all devices are connected and communicating with each other without any form of human intervention.

Sensors indicate the officer has left the vehicle, automatically locking the in-vehicle terminal and other devices in the vehicle to prohibit unauthorised access. The officer’s personal device detects that the officer is out of the vehicle and changes to the appropriate mode. If data analytics indicate a high risk environment, the officer’s devices get network priority over non-essential traffic. Smart glasses or body worn video send images or high resolution video back to the command centre. A wearable bio harness measures and alerts the command centre of increases in the officer’s heart rate, respiration or perspiration. If the officer is running in pursuit, the officer’s device adapts to a mode where command can see and hear in real time, and analytics, such as facial matching, are applied to alert the command centre to perform a specific procedure.

Other emergency services

While this is a policing scenario, the concepts can be applied more generally to the environments of all emergency service organisations to adapt the behaviour of applications. For example, sensor technology could trigger an alert to advise that someone has gained unauthorised access to the drug fridge in an ambulance, or wearable sensors that monitor surrounding air composition can advise a fire fighter of hazards.

Information sharing

Importantly, critical information can be shared between emergency services. Imagine paramedics approaching a house where the occupant has allegedly fainted. Information from multiple databases that record this as a known drug residence is now shared with the paramedics. Hazard levels can be calculated and paramedics can be alerted through the use of additional sensors to monitor the air quality for contaminants, small changes in air temperature or the tone of voice of other people nearby.


Context aware applications provide actionable intelligence: delivering the right data to the right user at the right time and in a way that is tailored and easy to consume according to individual roles. As an organisation that specialises in developing these types of solutions, our responsibility is to deliver intelligence seamlessly and effectively so that our first responders have information that protects their safety and enhances their work.

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  2. Schilit, B.N. and Theimer, M.M. (1994). “Disseminating Active Map Information to Mobile Hosts”

* Please note that some assumptions and simplifications have been made in describing the events.

Graeme Stanley

Public Safety Solutions, Motorola Solutions

Graeme Stanley has been involved with technology and communications in Australia for over 25 years. Graeme has been instrumental in the delivery of major public safety projects and leading solution developments since joining Motorola Solutions in 1999. Formal qualifications in engineering, project management and business have enabled Graeme to focus on leveraging technology to deliver operational and business driven outcomes to stakeholders. ...