Our behavioural data analysis application. It analyses location and activity data to interpret the habitual nature of Flex programme users. The analysis is shared with Alius in order to prompt healthier behaviour based on our Flex approach.
What it does
Nimbus consists of serverless functions deployed on Knative infrastructure for real-time processing. Nimbus receives data from third-party devices through Ampersand, and Luka. It computes critical scores with machine learning to establish how habitual you are. The variables include Activity, Social Opportunity, and Variety scores. The results are streamed back to other ecosystem service providers for visualisation or intervention (i.e., Vire and Alius). Nimbus functions are written in GoLang and Rust.
Nimbus' impact: users perform intake questionnaires at the start and start collecting behavioural data, they start receiving Do's based on subjective input, then Nimbus starts calculating behavioural variables which influence the next Do's that are sent, this process continues until the end of a programme at which a new questionnaire is answered to establish the progress made.
Why we developed it
With Nimbus, we developed the ability to personalise the Flex programme based on real-time behavioural data. Prior to Nimbus each programme would be personalised once at intake and only based on subjective input from the user itself. Nimbus provides a constantly evolving insight into a person's behaviours and habitual nature. Thus, the Flex programme can adapt itself, based on objective measures, to the user's changing needs.