Couch potato or gym addict? Semantic lifestyle profiling with wearables and knowledge graphs

Automatic lifestyle profiling to categorize users according to their daily routine-based lifestyles is an unexplored area. Despite the current trends on having wearable devices that generate large amounts of heterogeneous data, figuring out the lifestyle patterns of people is not a trivial task. We present Lifestyles-KG, a knowledge graph (fuzzy ontology) for semantic reasoning from wearable sensors. It can serve as a pre-processing taxonomical step that can be integrated into further prediction techniques for intuitively categorizing fuzzy lifestyle concepts, treats or profiles. The ultimate aim is to help tasks such as long-term human behavior classification and consequently, improve virtual coaching or customize lifestyle recommendation and intervention programs from free form non-labelled sensor data.