Abstract
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.
| Original language | English |
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| Publication status | Published - 2017 |
| Externally published | Yes |
| Event | 6th Workshop on Automated Knowledge Base Construction, AKBC 2017 at the 31st Conference on Neural Information Processing Systems, NIPS 2017 - Long Beach, United States Duration: 8 Dec 2017 → 8 Dec 2017 Conference number: 6 |
Workshop
| Workshop | 6th Workshop on Automated Knowledge Base Construction, AKBC 2017 at the 31st Conference on Neural Information Processing Systems, NIPS 2017 |
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| Abbreviated title | AKBC 2017 |
| Country/Territory | United States |
| City | Long Beach |
| Period | 8/12/17 → 8/12/17 |