PhD student Daniel Keitley, 2019 Winner
CEO and co-founder
Flare is a mobile, wearable and cloud-based system for getting help. It uses machine learning to autonomously and intelligently detect, monitor and respond to harmful situations.
Getting help during a harmful situation requires many tasks to be completed simultaneously. Smart devices have enormous potential to assist in these cases. They can be used to communicate with people, access important information and operate from within your pocket. Flare looks to takes advantage of these features to create software that can provide autonomous and intelligent assistance during an emergency.
Over 1/3 of people aged 65 and over fall each year. Often, this results in the individual spending many hours on the floor, unable to seek help, which increases the risk of serious health problems and loss of independence. We are building a solution that is able to detect falls and provide a personalised response that's able to work for the individual's own needs and abilities. Responses can range from simply providing advice for getting off the floor, to immediately notifying an emergency contact.
Flare is a mobile, wearable and cloud based system. It autonomously detects significant events in the environment using device sensors, such as the accelerometer, microphone and GPS. These data streams are processed by machine learning algorithms, which take into account information about the user and their preferences, to build a picture of the situation and appropriate courses of action to take.