Master theses

Boosting analytical potential of health tracking data using RML and Solid

Keywords: Linked Data, AI, Solid, Biohacking, Data Analysis, Decentralization, Health, Machine Learning, Mobile Health, Productivity

Supervision: Ben De Meester Anastasia Dimou

Students: max 1

  • More and more ways to register our daily habits become available.

    • Workouts can be registered using Polar hardware, such as the Polar V800 sports watch, H10 heart rate sensors, cadans sensor, etc.
    • Activity can be monitored using smartwatches, smartphones, step counters, etc.
    • Nutrition can be tracked using apps like Lifesum, MyFitnessPal.
    • Other health parameters such as weight, temperature, blood pressure, sleep can be tracked using, for example, Withings products.
    • Productivity time tracking (e.g., timely, etc.)
  • The main problem is the vendor lock-in.

    • When we are tracking workouts using a Polar sports watch, we have to analyse our workouts on the Polar Flow platform, where all of our workout data is stored.
    • Analysing our weight, temperature, blood pressure, would require us to use the Withings platform.
    • Although analyzing the data individually on their respective platforms might be insightful, it would become a lot more meaningful when the data can be linked together.
  • Nowadays you can integrate all this data from different devices, build your own knowledge graph, store it using Solid pods and create your own analytics!

  • Background knowledge

    • Prior knowledge of RDF, RML, Solid are bonus or interest to learn
    • Interest in Machine Learning
    • Python experience is definitely useful for data analysis, visualizations & ML
    • JS for Solid