AI and Machine Learning for improved wellbeing

Our client creates products and services for better healthcare and wellbeing. BfB Labs use an innovative sensor to improve mental wellbeing through gaming.

We partnered with BfB Labs on a project for ‘Champions of the Shenga’, a video game that responds to players’ stress levels and rewards those who can master them. This exciting game uses heart rate variation sensors to equip young people with emotional self-regulation skills.

The challenge

Champions of the Shenga collects second-by-second data on players’ heart rates, processed through an algorithm to derive the players’ Heart Rate Variability (HRV). BfB labs technology enables tracking of player improvement. Our client required a standardised, automatic way of analysing a player’s HRV over time. As a result, BfB Labs had a lot of unprocessed data that could add enormous value to the business and player, if explored and analysed.

BfB Labs wanted the creation of an analytics engine that could draw insight into HRV performance over time. They also required a dashboard to report analytics to users, with a focus on school or clinical customers looking to track and report progress on children in their care. Finally, the client required a predicted model, to allow for better understanding of how players perform over time.

What we did

BfB possessed a huge amount of engagement data that could be used to improve future iterations. We worked with them to build their understanding of machine learning and the value it can add to their business & customers. The process began with a focus on understanding BfB Labs data in depth, and finding how machine learning could best benefit their business. Daemon Solutions used AWS technology to build dashboards that captured insights into player engagement. Using the vast amount of user data (entries, performance, interaction, engagement) and learning metrics (sensor data, sensor graphs and metric trends) we built an algorithm that converts the heart rate into a metric showing improvement.

Using this algorithm we presented insights into player improvement, creating opportunities for Champions of the Shenga to personalise player development further. We built machine learning models that would predict how a player improves based on data, facilitating adaptive gameplay. This would enable adaptation of levelling and AI opponent performance, optimising the game to engage players further. Our solution included a machine learning module designed to create insights for education and healthcare professionals in predicting user development.

Outcome

Our solution enabled BfB Labs to harness and gain insight from previously unanalysed information. Our targeted approach to use of machine learning also created a much deeper understanding of the data, guiding the client on how to improve and personalise gameplay.

Both analytics dashboards - for the BfB team and for the healthcare professional - enabled improvement of the game to suit players needs. Engagement metrics and user behaviour was harnessed to improve game design, better understand UX and adjust difficulty to ensure engagement. The algorithm enabled tracking of learning progress.

Result

“We expect the ML module and associated dashboard to give us more information on player performance. This will help us measure the impact of the product for the first time and guide future development. The healthcare professionals who use the dashboard will for the first time be able to obtain analytics to monitor improvement. This will allow them to better support players and judge whether the game is having the effect it should be, whilst dramatically improving user experience and progress.”